42.
Jorge C C; Mehrotra S; Tielman M; Jonker C
Trust should correspond to trustworthiness: a formalization of appropriate, mutual trust in human-agent teams Proceedings Article
In: Proceedings of the 22nd International Workshop on Trust in Agent Societies, London, UK, 2021.
@inproceedings{jorge2021trust,
title = {Trust should correspond to trustworthiness: a formalization of appropriate, mutual trust in human-agent teams},
author = {Jorge, C Centeio and Mehrotra, Siddharth and Tielman, ML and Jonker, CM},
url = {https://ceur-ws.org/Vol-3022/paper4.pdf},
year = {2021},
date = {2021-01-01},
booktitle = {Proceedings of the 22nd International Workshop on Trust in Agent Societies},
address = {London, UK},
abstract = {In human-agent teams, how one teammate trusts another teammate should correspond to the latter’s actual trustworthiness, creating what we would call appropriate mutual trust. Although this sounds obvious, the notion of appropriate mutual trust for human-agent teamwork lacks a formal definition. In this article, we propose a formalization which represents trust as a belief about trustworthiness. Then, we address mutual trust, and pose that agents can use beliefs about trustworthiness to represent how they trust their human teammates, as well as to reason about how their human teammates trust them. This gives us a formalization with nested beliefs about beliefs of trustworthiness. Next, we highlight that mutual trust should also be appropriate, where we define appropriate trust in an agent as the trust which corresponds directly to that agent’s trustworthiness. Finally, we explore how agents can define their own trustworthiness, using the concepts of ability, benevolence and integrity. This formalization of appropriate mutual trust can form the base for developing agents which can promote such trust.},
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In human-agent teams, how one teammate trusts another teammate should correspond to the latter’s actual trustworthiness, creating what we would call appropriate mutual trust. Although this sounds obvious, the notion of appropriate mutual trust for human-agent teamwork lacks a formal definition. In this article, we propose a formalization which represents trust as a belief about trustworthiness. Then, we address mutual trust, and pose that agents can use beliefs about trustworthiness to represent how they trust their human teammates, as well as to reason about how their human teammates trust them. This gives us a formalization with nested beliefs about beliefs of trustworthiness. Next, we highlight that mutual trust should also be appropriate, where we define appropriate trust in an agent as the trust which corresponds directly to that agent’s trustworthiness. Finally, we explore how agents can define their own trustworthiness, using the concepts of ability, benevolence and integrity. This formalization of appropriate mutual trust can form the base for developing agents which can promote such trust.
41.
Steging C; Renooij S; Verheij B
Discovering the Rationale of Decisions: Towards a Method for Aligning Learning and Reasoning Proceedings Article
In: Proceedings of the Eighteenth International Conference on Artificial Intelligence and Law, pp. 235–239, Association for Computing Machinery, São Paulo, Brazil, 2021, ISBN: 9781450385268.
@inproceedings{StegingICAIL21,
title = {Discovering the Rationale of Decisions: Towards a Method for Aligning Learning and Reasoning},
author = {Steging, Cor and Renooij, Silja and Verheij, Bart},
url = {https://doi.org/10.1145/3462757.3466059},
doi = {10.1145/3462757.3466059},
isbn = {9781450385268},
year = {2021},
date = {2021-01-01},
booktitle = {Proceedings of the Eighteenth International Conference on Artificial Intelligence and Law},
pages = {235–239},
publisher = {Association for Computing Machinery},
address = {São Paulo, Brazil},
series = {ICAIL '21},
abstract = {In AI and law, systems that are designed for decision support should be explainable when pursuing justice. In order for these systems to be fair and responsible, they should make correct decisions and make them using a sound and transparent rationale. In this paper, we introduce a knowledge-driven method for model-agnostic rationale evaluation using dedicated test cases, similar to unit-testing in professional software development. We apply this new quantitative human-in-the-loop method in a machine learning experiment aimed at extracting known knowledge structures from artificial datasets from a real-life legal setting. We show that our method allows us to analyze the rationale of black box machine learning systems by assessing which rationale elements are learned or not. Furthermore, we show that the rationale can be adjusted using tailor-made training data based on the results of the rationale evaluation.},
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In AI and law, systems that are designed for decision support should be explainable when pursuing justice. In order for these systems to be fair and responsible, they should make correct decisions and make them using a sound and transparent rationale. In this paper, we introduce a knowledge-driven method for model-agnostic rationale evaluation using dedicated test cases, similar to unit-testing in professional software development. We apply this new quantitative human-in-the-loop method in a machine learning experiment aimed at extracting known knowledge structures from artificial datasets from a real-life legal setting. We show that our method allows us to analyze the rationale of black box machine learning systems by assessing which rationale elements are learned or not. Furthermore, we show that the rationale can be adjusted using tailor-made training data based on the results of the rationale evaluation.
40.
van Bekkum M; de Boer M; van Harmelen F; Meyer-Vitali A; ten Teije A
Modular design patterns for hybrid learning and reasoning systems Journal Article
In: Appl. Intell., vol. 51, no. 9, pp. 6528–6546, 2021.
@article{DBLP:journals/apin/BekkumBHMT21,
title = {Modular design patterns for hybrid learning and reasoning systems},
author = {Michael van Bekkum and Maaike de Boer and Frank van Harmelen and André Meyer{-}Vitali and Annette ten Teije},
url = {https://link.springer.com/article/10.1007/s10489-021-02394-3},
doi = {10.1007/s10489-021-02394-3},
year = {2021},
date = {2021-01-01},
journal = {Appl. Intell.},
volume = {51},
number = {9},
pages = {6528–6546},
abstract = {The unification of statistical (data-driven) and symbolic (knowledge-driven) methods is widely recognised as one of the key challenges of modern AI. Recent years have seen large number of publications on such hybrid neuro-symbolic AI systems. That rapidly growing literature is highly diverse and mostly empirical, and is lacking a unifying view of the large variety of these hybrid systems. In this paper we analyse a large body of recent literature and we propose a set of modular design patterns for such hybrid, neuro-symbolic systems. We are able to describe the architecture of a very large number of hybrid systems by composing only a small set of elementary patterns as building blocks. The main contributions of this paper are: 1) a taxonomically organised vocabulary to describe both processes and data structures used in hybrid systems; 2) a set of 15+ design patterns for hybrid AI systems, organised in a set of elementary patterns and a set of compositional patterns; 3) an application of these design patterns in two realistic use-cases for hybrid AI systems. Our patterns reveal similarities between systems that were not recognised until now. Finally, our design patterns extend and refine Kautz' earlier attempt at categorising neuro-symbolic architectures.},
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The unification of statistical (data-driven) and symbolic (knowledge-driven) methods is widely recognised as one of the key challenges of modern AI. Recent years have seen large number of publications on such hybrid neuro-symbolic AI systems. That rapidly growing literature is highly diverse and mostly empirical, and is lacking a unifying view of the large variety of these hybrid systems. In this paper we analyse a large body of recent literature and we propose a set of modular design patterns for such hybrid, neuro-symbolic systems. We are able to describe the architecture of a very large number of hybrid systems by composing only a small set of elementary patterns as building blocks. The main contributions of this paper are: 1) a taxonomically organised vocabulary to describe both processes and data structures used in hybrid systems; 2) a set of 15+ design patterns for hybrid AI systems, organised in a set of elementary patterns and a set of compositional patterns; 3) an application of these design patterns in two realistic use-cases for hybrid AI systems. Our patterns reveal similarities between systems that were not recognised until now. Finally, our design patterns extend and refine Kautz' earlier attempt at categorising neuro-symbolic architectures.
39.
Kuzina A; Welling M; Tomczak J M
Diagnosing Vulnerability of Variational Auto-Encoders to Adversarial Attacks Proceedings Article
In: ICLR 2021 Workshop on Robust and Reliable Machine Learning in the Real World, 2021.
@inproceedings{kuzina2021diagnosing,
title = {Diagnosing Vulnerability of Variational Auto-Encoders to Adversarial Attacks},
author = {Kuzina, Anna and Welling, Max and Tomczak, Jakub M},
url = {https://arxiv.org/pdf/2103.06701.pdf},
year = {2021},
date = {2021-01-01},
booktitle = {ICLR 2021 Workshop on Robust and Reliable Machine Learning in the Real World},
abstract = {In this work, we explore adversarial attacks on the Variational Autoencoders (VAE). We show how to modify data point to obtain a prescribed latent code (supervised attack) or just get a drastically different code (unsupervised attack). We examine the influence of model modifications (β-VAE, NVAE) on the robustness of VAEs and suggest metrics to quantify it.},
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In this work, we explore adversarial attacks on the Variational Autoencoders (VAE). We show how to modify data point to obtain a prescribed latent code (supervised attack) or just get a drastically different code (unsupervised attack). We examine the influence of model modifications (β-VAE, NVAE) on the robustness of VAEs and suggest metrics to quantify it.
38.
Zheng H; Verheij B
Rules, cases and arguments in artificial intelligence and law Book Section
In: Vogl, R (Ed.): Research Handbook on Big Data Law, pp. 373–387, Edgar Elgar Publishing, 2021.
@incollection{Zheng:2021,
title = {Rules, cases and arguments in artificial intelligence and law},
author = {H. Zheng and B. Verheij},
editor = {R Vogl},
url = {https://www.ai.rug.nl/~verheij/publications/handbook2021.htm},
year = {2021},
date = {2021-01-01},
booktitle = {Research Handbook on Big Data Law},
pages = {373–387},
publisher = {Edgar Elgar Publishing},
abstract = {Artificial intelligence and law is an interdisciplinary field of research that dates back at least to the 1970s, with academic conferences starting in the 1980s. In the field, complex problems are addressed about the computational modeling and automated support of legal reasoning and argumentation. Scholars have different backgrounds, and progress is driven by insights from lawyers, judges, computer scientists, philosophers and others. The community investigates and develops artificial intelligence techniques applicable in the legal domain, in order to enhance access to law for citizens and to support the efficiency and quality of work in the legal domain, aiming to promote a just society. Integral to the legal domain, legal reasoning and its structure and process have gained much attention in AI & Law research. Such research is today especially relevant, since in these days of big data and widespread use of algorithms, there is a need in AI to connect knowledge-based and data-driven AI techniques in order to arrive at a social, explainable and responsible AI. By considering knowledge in the form of rules and data in the form of cases connected by arguments, the field of AI & Law contributes relevant representations and algorithms for handling a combination of knowledge and data. In this chapter, as an entry point into the literature on AI & Law, three major styles of modeling legal reasoning are studied: rule-based reasoning, case-based reasoning and argument-based reasoning, which are the focus of this chapter. We describe selected key ideas, leaving out formal detail. As we will see, these styles of modeling legal reasoning are related, and there is much research investigating relations. We use the example domain of Dutch tort law (Section 2) to illustrate these three major styles, which are then more fully explained (Sections 3 to 5)},
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Artificial intelligence and law is an interdisciplinary field of research that dates back at least to the 1970s, with academic conferences starting in the 1980s. In the field, complex problems are addressed about the computational modeling and automated support of legal reasoning and argumentation. Scholars have different backgrounds, and progress is driven by insights from lawyers, judges, computer scientists, philosophers and others. The community investigates and develops artificial intelligence techniques applicable in the legal domain, in order to enhance access to law for citizens and to support the efficiency and quality of work in the legal domain, aiming to promote a just society. Integral to the legal domain, legal reasoning and its structure and process have gained much attention in AI & Law research. Such research is today especially relevant, since in these days of big data and widespread use of algorithms, there is a need in AI to connect knowledge-based and data-driven AI techniques in order to arrive at a social, explainable and responsible AI. By considering knowledge in the form of rules and data in the form of cases connected by arguments, the field of AI & Law contributes relevant representations and algorithms for handling a combination of knowledge and data. In this chapter, as an entry point into the literature on AI & Law, three major styles of modeling legal reasoning are studied: rule-based reasoning, case-based reasoning and argument-based reasoning, which are the focus of this chapter. We describe selected key ideas, leaving out formal detail. As we will see, these styles of modeling legal reasoning are related, and there is much research investigating relations. We use the example domain of Dutch tort law (Section 2) to illustrate these three major styles, which are then more fully explained (Sections 3 to 5)
37.
Kurtan A C; Yolum P
Assisting humans in privacy management: an agent-based approach Journal Article
In: Autonomous Agents and Multi-Agent Systems, vol. 35, no. 7, 2021.
@article{kurtan-yolum-21,
title = {Assisting humans in privacy management: an agent-based approach},
author = {A. Can Kurtan and P{ı}nar Yolum},
url = {https://link.springer.com/article/10.1007/s10458-020-09488-1},
doi = {https://doi.org/10.1007/s10458-020-09488-1},
year = {2021},
date = {2021-01-01},
journal = {Autonomous Agents and Multi-Agent Systems},
volume = {35},
number = {7},
abstract = {Image sharing is a service offered by many online social networks. In order to preserve privacy of images, users need to think through and specify a privacy setting for each image that they upload. This is difficult for two main reasons: first, research shows that many times users do not know their own privacy preferences, but only become aware of them over time. Second, even when users know their privacy preferences, editing these privacy settings is cumbersome and requires too much effort, interfering with the quick sharing behavior expected on an online social network. Accordingly, this paper proposes a privacy recommendation model for images using tags and an agent that implements this, namely pelte. Each user agent makes use of the privacy settings that its user have set for previous images to predict automatically the privacy setting for an image that is uploaded to be shared. When in doubt, the agent analyzes the sharing behavior of other users in the user's network to be able to recommend to its user about what should be considered as private. Contrary to existing approaches that assume all the images are available to a centralized model, pelte is compatible to distributed environments since each agent accesses only the privacy settings of the images that the agent owner has shared or those that have been shared with the user. Our simulations on a real-life dataset shows that pelte can accurately predict privacy settings even when a user has shared a few images with others, the images have only a few tags or the user's friends have varying privacy preferences.},
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Image sharing is a service offered by many online social networks. In order to preserve privacy of images, users need to think through and specify a privacy setting for each image that they upload. This is difficult for two main reasons: first, research shows that many times users do not know their own privacy preferences, but only become aware of them over time. Second, even when users know their privacy preferences, editing these privacy settings is cumbersome and requires too much effort, interfering with the quick sharing behavior expected on an online social network. Accordingly, this paper proposes a privacy recommendation model for images using tags and an agent that implements this, namely pelte. Each user agent makes use of the privacy settings that its user have set for previous images to predict automatically the privacy setting for an image that is uploaded to be shared. When in doubt, the agent analyzes the sharing behavior of other users in the user's network to be able to recommend to its user about what should be considered as private. Contrary to existing approaches that assume all the images are available to a centralized model, pelte is compatible to distributed environments since each agent accesses only the privacy settings of the images that the agent owner has shared or those that have been shared with the user. Our simulations on a real-life dataset shows that pelte can accurately predict privacy settings even when a user has shared a few images with others, the images have only a few tags or the user's friends have varying privacy preferences.
36.
Liscio E; van der Meer M; Jonker C M; Murukannaiah P K
A Collaborative Platform for Identifying Context-Specific Values Proceedings Article
In: Proceedings of the 20th International Conference on Autonomous Agents and Multiagent Systems, pp. 1773–1775, IFAAMAS, Online, 2021.
@inproceedings{Liscio2021a,
title = {A Collaborative Platform for Identifying Context-Specific Values},
author = {Liscio, Enrico and van der Meer, Michiel and Jonker, Catholijn M. and Murukannaiah, Pradeep K.},
url = {https://www.ifaamas.org/Proceedings/aamas2021/pdfs/p1773.pdf},
year = {2021},
date = {2021-01-01},
booktitle = {Proceedings of the 20th International Conference on Autonomous Agents and Multiagent Systems},
pages = {1773–1775},
publisher = {IFAAMAS},
address = {Online},
series = {AAMAS '21},
abstract = {Value alignment is a crucial aspect of ethical multiagent systems. An important step toward value alignment is identifying values specific to an application context. However, identifying context-specific values is complex and cognitively demanding. To support this process, we develop a methodology and a collaborative web platform that employs AI techniques. We describe this platform, highlighting its intuitive design and implementation.},
keywords = {},
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Value alignment is a crucial aspect of ethical multiagent systems. An important step toward value alignment is identifying values specific to an application context. However, identifying context-specific values is complex and cognitively demanding. To support this process, we develop a methodology and a collaborative web platform that employs AI techniques. We describe this platform, highlighting its intuitive design and implementation.
35.
Liscio E; van der Meer M; Siebert L C; Jonker C M; Mouter N; Murukannaiah P K
Axies: Identifying and Evaluating Context-Specific Values Proceedings Article
In: Proc. of the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021), pp. 799–808, IFAAMAS, Online, 2021.
@inproceedings{Liscio2021b,
title = {Axies: Identifying and Evaluating Context-Specific Values},
author = {Liscio, Enrico and van der Meer, Michiel and Siebert, Luciano C. and Jonker, Catholijn M. and Mouter, Niek and Murukannaiah, Pradeep K.},
url = {https://ii.tudelft.nl/~pradeep/doc/Liscio-2021-AAMAS-Axies.pdf},
year = {2021},
date = {2021-01-01},
booktitle = {Proc. of the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021)},
pages = {799–808},
publisher = {IFAAMAS},
address = {Online},
abstract = {The pursuit of values drives human behavior and promotes cooperation. Existing research is focused on general (e.g., Schwartz) values that transcend contexts. However, context-specific values are necessary to (1) understand human decisions, and (2) engineer intelligent agents that can elicit human values and take value-aligned actions. We propose Axies, a hybrid (human and AI) methodology to identify context-specific values. Axies simplifies the abstract task of value identification as a guided value annotation process involving human annotators. Axies exploits the growing availability of value-laden text corpora and Natural Language Processing to assist the annotators in systematically identifying context-specific values. We evaluate Axies in a user study involving 60 subjects. In our study, six annotators generate value lists for two timely and important contexts: Covid-19 measures, and sustainable Energy. Then, two policy experts and 52 crowd workers evaluate Axies value lists. We find that Axies yields values that are context-specific, consistent across different annotators, and comprehensible to end users},
keywords = {},
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The pursuit of values drives human behavior and promotes cooperation. Existing research is focused on general (e.g., Schwartz) values that transcend contexts. However, context-specific values are necessary to (1) understand human decisions, and (2) engineer intelligent agents that can elicit human values and take value-aligned actions. We propose Axies, a hybrid (human and AI) methodology to identify context-specific values. Axies simplifies the abstract task of value identification as a guided value annotation process involving human annotators. Axies exploits the growing availability of value-laden text corpora and Natural Language Processing to assist the annotators in systematically identifying context-specific values. We evaluate Axies in a user study involving 60 subjects. In our study, six annotators generate value lists for two timely and important contexts: Covid-19 measures, and sustainable Energy. Then, two policy experts and 52 crowd workers evaluate Axies value lists. We find that Axies yields values that are context-specific, consistent across different annotators, and comprehensible to end users
34.
Manggala P; Hoos H H; Nalisnick E
Bayesian Regression from Multiple Sources of Weak Supervision Proceedings Article
In: ICML 2021 Machine Learning for Data: Automated Creation, Privacy, Bias, 2021.
@inproceedings{manggala2021bayesianregression,
title = {Bayesian Regression from Multiple Sources of Weak Supervision},
author = {Manggala, Putra and Hoos, Holger H. and Nalisnick, Eric},
url = {https://pmangg.github.io/papers/brfmsows_mhn_ml4data_icml.pdf},
year = {2021},
date = {2021-01-01},
booktitle = {ICML 2021 Machine Learning for Data: Automated Creation, Privacy, Bias},
abstract = {We describe a Bayesian approach to weakly supervised regression. Our proposed framework propagates uncertainty from the weak supervision to an aggregated predictive distribution. We use a generalized Bayes procedure to account for the supervision being weak and therefore likely misspecified.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We describe a Bayesian approach to weakly supervised regression. Our proposed framework propagates uncertainty from the weak supervision to an aggregated predictive distribution. We use a generalized Bayes procedure to account for the supervision being weak and therefore likely misspecified.
33.
Steging C; Renooij S; Verheij B
Discovering the Rationale of Decisions: Experiments on Aligning Learning and Reasoning Proceedings Article
In: 4th EXplainable AI in Law Workshop (XAILA 2021), pp. 235–239, ACM, 2021.
@inproceedings{StegingXAILA21,
title = {Discovering the Rationale of Decisions: Experiments on
Aligning Learning and Reasoning},
author = {Cor Steging and Silja Renooij and Bart Verheij},
url = {https://arxiv.org/abs/2105.06758},
year = {2021},
date = {2021-01-01},
booktitle = {4th EXplainable AI in Law Workshop (XAILA 2021)},
pages = {235–239},
publisher = {ACM},
abstract = {In AI and law, systems that are designed for decision support should be explainable when pursuing justice. In order for these systems to be fair and responsible, they should make correct decisions and make them using a sound and transparent rationale. In this paper, we introduce a knowledge-driven method for model-agnostic rationale evaluation using dedicated test cases, similar to unit-testing in professional software development. We apply this new method in a set of machine learning experiments aimed at extracting known knowledge structures from artificial datasets from fictional and non-fictional legal settings. We show that our method allows us to analyze the rationale of black-box machine learning systems by assessing which rationale elements are learned or not. Furthermore, we show that the rationale can be adjusted using tailor-made training data based on the results of the rationale evaluation.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
In AI and law, systems that are designed for decision support should be explainable when pursuing justice. In order for these systems to be fair and responsible, they should make correct decisions and make them using a sound and transparent rationale. In this paper, we introduce a knowledge-driven method for model-agnostic rationale evaluation using dedicated test cases, similar to unit-testing in professional software development. We apply this new method in a set of machine learning experiments aimed at extracting known knowledge structures from artificial datasets from fictional and non-fictional legal settings. We show that our method allows us to analyze the rationale of black-box machine learning systems by assessing which rationale elements are learned or not. Furthermore, we show that the rationale can be adjusted using tailor-made training data based on the results of the rationale evaluation.
32.
Vessies M B; Vadgama S P; van de Leur R R; Doevendans P A F M; Hassink R J; Bekkers E; van Es R
Interpretable ECG classification via a query-based latent space traversal (qLST) Journal Article
In: CoRR, vol. abs/2111.07386, 2021.
@article{DBLP:journals/corr/abs-2111-07386,
title = {Interpretable ECG classification via a query-based latent space traversal (qLST)},
author = {Melle B. Vessies and Sharvaree P. Vadgama and Rutger R. van de Leur and Pieter A. F. M. Doevendans and Rutger J. Hassink and Erik Bekkers and René van Es},
url = {https://arxiv.org/abs/2111.07386},
year = {2021},
date = {2021-01-01},
journal = {CoRR},
volume = {abs/2111.07386},
abstract = {Electrocardiography (ECG) is an effective and non-invasive diagnostic tool that measures the electrical activity of the heart. Interpretation of ECG signals to detect various abnormalities is a challenging task that requires expertise. Recently, the use of deep neural networks for ECG classification to aid medical practitioners has become popular, but their black box nature hampers clinical implementation. Several saliency-based interpretability techniques have been proposed, but they only indicate the location of important features and not the actual features. We present a novel interpretability technique called qLST, a query-based latent space traversal technique that is able to provide explanations for any ECG classification model. With qLST, we train a neural network that learns to traverse in the latent space of a variational autoencoder trained on a large university hospital dataset with over 800,000 ECGs annotated for 28 diseases. We demonstrate through experiments that we can explain different black box classifiers by generating ECGs through these traversals.},
keywords = {},
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}
Electrocardiography (ECG) is an effective and non-invasive diagnostic tool that measures the electrical activity of the heart. Interpretation of ECG signals to detect various abnormalities is a challenging task that requires expertise. Recently, the use of deep neural networks for ECG classification to aid medical practitioners has become popular, but their black box nature hampers clinical implementation. Several saliency-based interpretability techniques have been proposed, but they only indicate the location of important features and not the actual features. We present a novel interpretability technique called qLST, a query-based latent space traversal technique that is able to provide explanations for any ECG classification model. With qLST, we train a neural network that learns to traverse in the latent space of a variational autoencoder trained on a large university hospital dataset with over 800,000 ECGs annotated for 28 diseases. We demonstrate through experiments that we can explain different black box classifiers by generating ECGs through these traversals.
31.
Dudzik B; Columbus S; Hrkalovic T M; Balliet D; Hung H
Recognizing Perceived Interdependence in Face-to-Face Negotiations through Multimodal Analysis of Nonverbal Behavior Book Chapter
In: Proceedings of the 2021 International Conference on Multimodal Interaction, pp. 121–130, Association for Computing Machinery, New York, NY, USA, 2021, ISBN: 9781450384810.
@inbook{10.1145/3462244.3479935,
title = {Recognizing Perceived Interdependence in Face-to-Face Negotiations through Multimodal Analysis of Nonverbal Behavior},
author = {Dudzik, Bernd and Columbus, Simon and Hrkalovic, Tiffany Matej and Balliet, Daniel and Hung, Hayley},
url = {https://research.tudelft.nl/en/publications/recognizing-perceived-interdependence-in-face-to-face-negotiation},
doi = {10.1145/3462244.3479935},
isbn = {9781450384810},
year = {2021},
date = {2021-01-01},
booktitle = {Proceedings of the 2021 International Conference on Multimodal Interaction},
pages = {121–130},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
abstract = {Enabling computer-based applications to display intelligent behavior in complex social settings requires them to relate to important aspects of how humans experience and understand such situations. One crucial driver of peoples' social behavior during an interaction is the interdependence they perceive, i.e., how the outcome of an interaction is determined by their own and others' actions. According to psychological studies, both the nonverbal behavior displayed by Motivated by this, we present a series of experiments to automatically recognize interdependence perceptions in dyadic face-to-face negotiations using these sources. Concretely, our approach draws on a combination of features describing individuals' Facial, Upper Body, and Vocal Behavior with state-of-the-art algorithms for multivariate time series classification. Our findings demonstrate that differences in some types of interdependence perceptions can be detected through the automatic analysis of nonverbal behaviors. We discuss implications for developing socially intelligent systems and opportunities for future research.},
keywords = {},
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}
Enabling computer-based applications to display intelligent behavior in complex social settings requires them to relate to important aspects of how humans experience and understand such situations. One crucial driver of peoples' social behavior during an interaction is the interdependence they perceive, i.e., how the outcome of an interaction is determined by their own and others' actions. According to psychological studies, both the nonverbal behavior displayed by Motivated by this, we present a series of experiments to automatically recognize interdependence perceptions in dyadic face-to-face negotiations using these sources. Concretely, our approach draws on a combination of features describing individuals' Facial, Upper Body, and Vocal Behavior with state-of-the-art algorithms for multivariate time series classification. Our findings demonstrate that differences in some types of interdependence perceptions can be detected through the automatic analysis of nonverbal behaviors. We discuss implications for developing socially intelligent systems and opportunities for future research.
30.
Steging C; Renooij S; Verheij B
Rationale Discovery and Explainable AI Proceedings Article
In: Erich, Schweighofer (Ed.): Legal Knowledge and Information Systems - JURIX 2021: The Thirty-fourth Annual Conference, Vilnius, Lithuania, 8-10 December 2021, pp. 225–234, IOS Press, 2021.
@inproceedings{DBLP:conf/jurix/StegingRV21,
title = {Rationale Discovery and Explainable AI},
author = {Cor Steging and Silja Renooij and Bart Verheij},
editor = {Schweighofer Erich},
url = {https://doi.org/10.3233/FAIA210341},
doi = {10.3233/FAIA210341},
year = {2021},
date = {2021-01-01},
booktitle = {Legal Knowledge and Information Systems - JURIX 2021: The Thirty-fourth Annual Conference, Vilnius, Lithuania, 8-10 December 2021},
volume = {346},
pages = {225–234},
publisher = {IOS Press},
series = {Frontiers in Artificial Intelligence and Applications},
abstract = {The justification of an algorithm's outcomes is important in many domains, and in particular in the law. However, previous research has shown that machine learning systems can make the right decisions for the wrong reasons: despite high accuracies, not all of the conditions that define the domain of the training data are learned. In this study, we investigate what the system does learn, using state-of-the-art explainable AI techniques. With the use of SHAP and LIME, we are able to show which features impact the decision making process and how the impact changes with different distributions of the training data. However, our results also show that even high accuracy and good relevant feature detection are no guarantee for a sound rationale. Hence these state-of-the-art explainable AI techniques cannot be used to fully expose unsound rationales, further advocating the need for a separate method for rationale evaluation.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
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The justification of an algorithm's outcomes is important in many domains, and in particular in the law. However, previous research has shown that machine learning systems can make the right decisions for the wrong reasons: despite high accuracies, not all of the conditions that define the domain of the training data are learned. In this study, we investigate what the system does learn, using state-of-the-art explainable AI techniques. With the use of SHAP and LIME, we are able to show which features impact the decision making process and how the impact changes with different distributions of the training data. However, our results also show that even high accuracy and good relevant feature detection are no guarantee for a sound rationale. Hence these state-of-the-art explainable AI techniques cannot be used to fully expose unsound rationales, further advocating the need for a separate method for rationale evaluation.
29.
Ho L
Knowledge Representation Formalisms for Hybrid Intelligence Conference
2021, (18th International Conference on Principles of Knowledge Representation and Reasoning, KR 2021 ; Conference date: 03-11-2021 Through 12-11-2021).
@conference{dbdf453dfbad49d981cb01a6964a056a,
title = {Knowledge Representation Formalisms for Hybrid Intelligence},
author = {Loan Ho},
url = {https://research.vu.nl/en/publications/knowledge-representation-formalisms-for-hybrid-intelligence},
year = {2021},
date = {2021-01-01},
pages = {22–25},
abstract = {Knowledge graphs can play an important role to store and provide access to global knowledge, common and accessible to both human and artificial agents, and store local knowledge of individual agents in a larger network of agents. Studying suitable formalisms to model complex, conflicting, dynamic and contextualised knowledge is still a big challenge. Therefore, we investigate the usage of knowledge representation formalisms that allows artificial intelligence systems to adapt and work with complex, conflicting, dynamic and contextualized knowledge.},
note = {18th International Conference on Principles of Knowledge Representation and Reasoning, KR 2021 ; Conference date: 03-11-2021 Through 12-11-2021},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Knowledge graphs can play an important role to store and provide access to global knowledge, common and accessible to both human and artificial agents, and store local knowledge of individual agents in a larger network of agents. Studying suitable formalisms to model complex, conflicting, dynamic and contextualised knowledge is still a big challenge. Therefore, we investigate the usage of knowledge representation formalisms that allows artificial intelligence systems to adapt and work with complex, conflicting, dynamic and contextualized knowledge.
28.
Krause L; Vossen P
When to explain: Identifying explanation triggers in human-agent interaction Proceedings Article
In: 2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence, pp. 55–60, Association for Computational Linguistics, Dublin, Ireland, 2020.
@inproceedings{krause-vossen-2020-explain,
title = {When to explain: Identifying explanation triggers in human-agent interaction},
author = {Krause, Lea and Vossen, Piek},
url = {https://www.aclweb.org/anthology/2020.nl4xai-1.12},
year = {2020},
date = {2020-11-01},
booktitle = {2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence},
pages = {55–60},
publisher = {Association for Computational Linguistics},
address = {Dublin, Ireland},
abstract = {With more agents deployed than ever, users need to be able to interact and cooperate with them in an effective and comfortable manner. Explanations have been shown to increase the understanding and trust of a user in human-agent interaction. There have been numerous studies investigating this effect, but they rely on the user explicitly requesting an explanation. We propose a first overview of when an explanation should be triggered and show that there are many instances that would be missed if the agent solely relies on direct questions. For this, we differentiate between direct triggers such as commands or questions and introduce indirect triggers like confusion or uncertainty detection.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
With more agents deployed than ever, users need to be able to interact and cooperate with them in an effective and comfortable manner. Explanations have been shown to increase the understanding and trust of a user in human-agent interaction. There have been numerous studies investigating this effect, but they rely on the user explicitly requesting an explanation. We propose a first overview of when an explanation should be triggered and show that there are many instances that would be missed if the agent solely relies on direct questions. For this, we differentiate between direct triggers such as commands or questions and introduce indirect triggers like confusion or uncertainty detection.
27.
Akata Z; Balliet D; de Rijke M; Dignum F; Dignum V; Eiben G; Fokkens A; Grossi D; Hindriks K; Hoos H; Hung H; Jonker C; Monz C; Neerincx M; Oliehoek F; Prakken H; Schlobach S; van der Gaag L; van Harmelen F; van Hoof H; van Riemsdijk B; van Wynsberghe A; Verbrugge R; Verheij B; Vossen P; Welling M
In: IEEE Computer, vol. 53, no. 08, pp. 18–28, 2020, ISSN: 1558-0814.
@article{9153877,
title = {A Research Agenda for Hybrid Intelligence: Augmenting Human Intellect With Collaborative, Adaptive, Responsible, and Explainable Artificial Intelligence},
author = {Zeyneb Akata and Dan Balliet and Maarten de Rijke and Frank Dignum and Virginia Dignum and Guszti Eiben and Anstke Fokkens and Davide Grossi and Koen Hindriks and Holger Hoos and Hayley Hung and Catholijn Jonker and Christof Monz and Mark Neerincx and Frans Oliehoek and Henri Prakken and Stefan Schlobach and Linda van der Gaag and Frank van Harmelen and Herke van Hoof and Birna van Riemsdijk and Aimee van Wynsberghe and Rineke Verbrugge and Bart Verheij and Piek Vossen and Max Welling},
url = {http://www.cs.vu.nl/~frankh/postscript/IEEEComputer2020.pdf},
doi = {10.1109/MC.2020.2996587},
issn = {1558-0814},
year = {2020},
date = {2020-08-01},
journal = {IEEE Computer},
volume = {53},
number = {08},
pages = {18–28},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
abstract = {We define hybrid intelligence (HI) as the combination of human and machine intelligence, augmenting human intellect and capabilities instead of replacing them and achieving goals that were unreachable by either humans or machines. HI is an important new research focus for artificial intelligence, and we set a research agenda for HI by formulating four challenges.},
keywords = {},
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We define hybrid intelligence (HI) as the combination of human and machine intelligence, augmenting human intellect and capabilities instead of replacing them and achieving goals that were unreachable by either humans or machines. HI is an important new research focus for artificial intelligence, and we set a research agenda for HI by formulating four challenges.
26.
Renting B M; Hoos H H; Jonker C M
Automated Configuration of Negotiation Strategies Proceedings Article
In: Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems, pp. 1116–1124, International Foundation for Autonomous Agents and Multiagent Systems, 2020, ISBN: 978-1-4503-7518-4.
@inproceedings{Renting2020AutomatedStrategies,
title = {Automated Configuration of Negotiation Strategies},
author = {Renting, Bram M. and Hoos, Holger H. and Jonker, Catholijn M.},
url = {https://ifaamas.org/Proceedings/aamas2020/pdfs/p1116.pdf},
isbn = {978-1-4503-7518-4},
year = {2020},
date = {2020-05-01},
booktitle = {Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems},
pages = {1116–1124},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
series = {AAMAS '20},
abstract = {Bidding and acceptance strategies have a substantial impact on the outcome of negotiations in scenarios with linear additive and nonlinear utility functions. Over the years, it has become clear that there is no single best strategy for all negotiation settings, yet many fixed strategies are still being developed. We envision a shift in the strategy design question from: What is a good strategy?, towards: What could be a good strategy? For this purpose, we developed a method leveraging automated algorithm configuration to find the best strategies for a specific set of negotiation settings. By empowering automated negotiating agents using automated algorithm configuration, we obtain a flexible negotiation agent that can be configured automatically for a rich space of opponents and negotiation scenarios. To critically assess our approach, the agent was tested in an ANAC-like bilateral automated negotiation tournament setting against past competitors. We show that our automatically configured agent outperforms all other agents, with a 5.1 percent increase in negotiation payoff compared to the next-best agent. We note that without our agent in the tournament, the top-ranked agent wins by a margin of only 0.01 percent .},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Bidding and acceptance strategies have a substantial impact on the outcome of negotiations in scenarios with linear additive and nonlinear utility functions. Over the years, it has become clear that there is no single best strategy for all negotiation settings, yet many fixed strategies are still being developed. We envision a shift in the strategy design question from: What is a good strategy?, towards: What could be a good strategy? For this purpose, we developed a method leveraging automated algorithm configuration to find the best strategies for a specific set of negotiation settings. By empowering automated negotiating agents using automated algorithm configuration, we obtain a flexible negotiation agent that can be configured automatically for a rich space of opponents and negotiation scenarios. To critically assess our approach, the agent was tested in an ANAC-like bilateral automated negotiation tournament setting against past competitors. We show that our automatically configured agent outperforms all other agents, with a 5.1 percent increase in negotiation payoff compared to the next-best agent. We note that without our agent in the tournament, the top-ranked agent wins by a margin of only 0.01 percent .
25.
Dvorák W; Zafarghandi A K; Woltran S
Expressiveness of SETAFs and Support-Free ADFs under 3-valued Semantics Journal Article
In: CoRR, vol. abs/2007.03581, 2020.
@article{DBLP:journals/corr/abs-2007-03581,
title = {Expressiveness of SETAFs and Support-Free ADFs under 3-valued Semantics},
author = {Wolfgang Dvorák and
Atefeh {Keshavarzi Zafarghandi} and
Stefan Woltran},
url = {https://arxiv.org/abs/2007.03581},
year = {2020},
date = {2020-01-01},
journal = {CoRR},
volume = {abs/2007.03581},
abstract = {Generalizing the attack structure in argumentation frameworks (AFs) has been studied in different ways. Most prominently, the binary attack relation of Dung frameworks has been extended to the notion of collective attacks. The resulting formalism is often termed SETAFs. Another approach is provided via abstract dialectical frameworks (ADFs), where acceptance conditions specify the relation between arguments; restricting these conditions naturally allows for so-called support-free ADFs. The aim of the paper is to shed light on the relation between these two different approaches. To this end, we investigate and compare the expressiveness of SETAFs and support-free ADFs under the lens of 3-valued semantics. Our results show that it is only the presence of unsatisfiable acceptance conditions in support-free ADFs that discriminate the two approaches.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Generalizing the attack structure in argumentation frameworks (AFs) has been studied in different ways. Most prominently, the binary attack relation of Dung frameworks has been extended to the notion of collective attacks. The resulting formalism is often termed SETAFs. Another approach is provided via abstract dialectical frameworks (ADFs), where acceptance conditions specify the relation between arguments; restricting these conditions naturally allows for so-called support-free ADFs. The aim of the paper is to shed light on the relation between these two different approaches. To this end, we investigate and compare the expressiveness of SETAFs and support-free ADFs under the lens of 3-valued semantics. Our results show that it is only the presence of unsatisfiable acceptance conditions in support-free ADFs that discriminate the two approaches.
24.
Verheij B
Artificial intelligence as law Journal Article
In: Artif. Intell. Law, vol. 28, no. 2, pp. 181–206, 2020.
@article{Verheij20,
title = {Artificial intelligence as law},
author = {Bart Verheij},
url = {https://doi.org/10.1007/s10506-020-09266-0},
doi = {10.1007/s10506-020-09266-0},
year = {2020},
date = {2020-01-01},
journal = {Artif. Intell. Law},
volume = {28},
number = {2},
pages = {181–206},
abstract = {Information technology is so ubiquitous and AI’s progress so inspiring that also legal professionals experience its benefits and have high expectations. At the same time, the powers of AI have been rising so strongly that it is no longer obvious that AI applications (whether in the law or elsewhere) help promoting a good society; in fact they are sometimes harmful. Hence many argue that safeguards are needed for AI to be trustworthy, social, responsible, humane, ethical. In short: AI should be good for us. But how to establish proper safeguards for AI? One strong answer readily available is: consider the problems and solutions studied in AI & Law. AI & Law has worked on the design of social, explainable, responsible AI aligned with human values for decades already, AI & Law addresses the hardest problems across the breadth of AI (in reasoning, knowledge, learning and language), and AI & Law inspires new solutions (argumentation, schemes and norms, rules and cases, interpretation). It is argued that the study of AI as Law supports the development of an AI that is good for us, making AI & Law more relevant than ever.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Information technology is so ubiquitous and AI’s progress so inspiring that also legal professionals experience its benefits and have high expectations. At the same time, the powers of AI have been rising so strongly that it is no longer obvious that AI applications (whether in the law or elsewhere) help promoting a good society; in fact they are sometimes harmful. Hence many argue that safeguards are needed for AI to be trustworthy, social, responsible, humane, ethical. In short: AI should be good for us. But how to establish proper safeguards for AI? One strong answer readily available is: consider the problems and solutions studied in AI & Law. AI & Law has worked on the design of social, explainable, responsible AI aligned with human values for decades already, AI & Law addresses the hardest problems across the breadth of AI (in reasoning, knowledge, learning and language), and AI & Law inspires new solutions (argumentation, schemes and norms, rules and cases, interpretation). It is argued that the study of AI as Law supports the development of an AI that is good for us, making AI & Law more relevant than ever.
23.
Kökciyan N; Yolum P
TURP: Managing Trust for Regulating Privacy in Internet of Things Journal Article
In: IEEE Internet Computing, vol. 24, no. 6, pp. 9–16, 2020.
@article{turp-ic-2020,
title = {TURP: Managing Trust for Regulating Privacy in Internet of Things},
author = {Kökciyan, Nadin and Yolum, P{ı}nar},
url = {https://webspace.science.uu.nl/~yolum001/papers/InternetComputing-20-TURP.pdf},
doi = {https://doi.org/10.1109/MIC.2020.3020006},
year = {2020},
date = {2020-01-01},
journal = {IEEE Internet Computing},
volume = {24},
number = {6},
pages = {9–16},
abstract = {Internet of Things [IoT] applications, such as smart home or ambient assisted livingsystems, promise useful services to end users. Most of these services rely heavily on sharingand aggregating information among devices; many times raising privacy concerns. Contrary totraditional systems, where privacy of each user is managed through well-defined policies, thescale, dynamism, and heterogeneity of the IoT systems make it impossible to specify privacypolicies for all possible situations. Alternatively, this paper argues that handling of privacy has tobe reasoned by the IoT devices, depending on the norms, context, as well as the trust amongentities. We present a technique, where an IoT device collects information from others, evaluatesthe trustworthiness of the information sources to decide the suitability of sharing informationwith others. We demonstrate the applicability of the technique over an IoT pilot study.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Internet of Things [IoT] applications, such as smart home or ambient assisted livingsystems, promise useful services to end users. Most of these services rely heavily on sharingand aggregating information among devices; many times raising privacy concerns. Contrary totraditional systems, where privacy of each user is managed through well-defined policies, thescale, dynamism, and heterogeneity of the IoT systems make it impossible to specify privacypolicies for all possible situations. Alternatively, this paper argues that handling of privacy has tobe reasoned by the IoT devices, depending on the norms, context, as well as the trust amongentities. We present a technique, where an IoT device collects information from others, evaluatesthe trustworthiness of the information sources to decide the suitability of sharing informationwith others. We demonstrate the applicability of the technique over an IoT pilot study.
22.
Ulusoy O; Yolum P
Agents for Preserving Privacy: Learning and Decision Making Collaboratively Proceedings Article
In: Bassiliades, Nick; Chalkiadakis, Georgios; de Jonge, Dave (Ed.): Multi-Agent Systems and Agreement Technologies, pp. 116–131, Springer International Publishing, 2020, ISBN: 978-3-030-66412-1.
@inproceedings{ulusoy-yolum-20,
title = {Agents for Preserving Privacy: Learning and Decision Making Collaboratively},
author = {Ulusoy, Onuralp and Yolum, P{ı}nar},
editor = {Bassiliades, Nick and Chalkiadakis, Georgios and de Jonge, Dave},
url = {https://webspace.science.uu.nl/~yolum001/papers/ulusoy-yolum-20.pdf},
doi = {https://doi.org/10.1007/978-3-030-66412-1_8},
isbn = {978-3-030-66412-1},
year = {2020},
date = {2020-01-01},
booktitle = {Multi-Agent Systems and Agreement Technologies},
pages = {116–131},
publisher = {Springer International Publishing},
abstract = {Privacy is a right of individuals to keep personal information to themselves. Often online systems enable their users to select what information they would like to share with others and what information to keep private. When an information pertains only to a single individual, it is possible to preserve privacy by providing the right access options to the user. However, when an information pertains to multiple individuals, such as a picture of a group of friends or a collaboratively edited document, deciding how to share this information and with whom is challenging as individuals might have conflicting privacy constraints. Resolving this problem requires an automated mechanism that takes into account the relevant individuals' concerns to decide on the privacy configuration of information. Accordingly, this paper proposes an auction-based privacy mechanism to manage the privacy of users when information related to multiple individuals are at stake. We propose to have a software agent that acts on behalf of each user to enter privacy auctions, learn the subjective privacy valuations of the individuals over time, and to bid to respect their privacy. We show the workings of our proposed approach over multiagent simulations.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Privacy is a right of individuals to keep personal information to themselves. Often online systems enable their users to select what information they would like to share with others and what information to keep private. When an information pertains only to a single individual, it is possible to preserve privacy by providing the right access options to the user. However, when an information pertains to multiple individuals, such as a picture of a group of friends or a collaboratively edited document, deciding how to share this information and with whom is challenging as individuals might have conflicting privacy constraints. Resolving this problem requires an automated mechanism that takes into account the relevant individuals' concerns to decide on the privacy configuration of information. Accordingly, this paper proposes an auction-based privacy mechanism to manage the privacy of users when information related to multiple individuals are at stake. We propose to have a software agent that acts on behalf of each user to enter privacy auctions, learn the subjective privacy valuations of the individuals over time, and to bid to respect their privacy. We show the workings of our proposed approach over multiagent simulations.
21.
Murukannaiah P K; Ajmeri N; Jonker C M; Singh M P
New Foundations of Ethical Multiagent Systems Proceedings Article
In: Proceedings of the 19th Conference on Autonomous Agents and MultiAgent Systems, pp. 1706–1710, Auckland, 2020.
@inproceedings{Murukannaiah-2020-AAMASBlueSky-EthicalMAS,
title = {New Foundations of Ethical Multiagent Systems},
author = {Pradeep K. Murukannaiah and Nirav Ajmeri and Catholijn M. Jonker and Munindar P. Singh},
url = {https://ii.tudelft.nl/~pradeep/doc/Murukannaiah-2020-AAMASBlueSky-EthicalMAS.pdf},
year = {2020},
date = {2020-01-01},
booktitle = {Proceedings of the 19th Conference on Autonomous Agents and MultiAgent Systems},
pages = {1706–1710},
address = {Auckland},
series = {AAMAS '20},
abstract = {Ethics is inherently a multiagent concern. However, research on AI ethics today is dominated by work on individual agents: (1) how an autonomous robot or car may harm or (differentially) benefit people in hypothetical situations (the so-called trolley problems) and (2) how a machine learning algorithm may produce biased decisions or recommendations. The societal framework is largely omitted. To develop new foundations for ethics in AI, we adopt a sociotechnical stance in which agents (as technical entities) help autonomous social entities or principals (people and organizations). This multiagent conception of a sociotechnical system (STS) captures how ethical concerns arise in the mutual interactions of multiple stakeholders. These foundations would enable us to realize ethical STSs that incorporate social and technical controls to respect stated ethical postures of the agents in the STSs. The envisioned foundations require new thinking, along two broad themes, on how to realize (1) an STS that reflects its stakeholders' values and (2) individual agents that function effectively in such an STS.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Ethics is inherently a multiagent concern. However, research on AI ethics today is dominated by work on individual agents: (1) how an autonomous robot or car may harm or (differentially) benefit people in hypothetical situations (the so-called trolley problems) and (2) how a machine learning algorithm may produce biased decisions or recommendations. The societal framework is largely omitted. To develop new foundations for ethics in AI, we adopt a sociotechnical stance in which agents (as technical entities) help autonomous social entities or principals (people and organizations). This multiagent conception of a sociotechnical system (STS) captures how ethical concerns arise in the mutual interactions of multiple stakeholders. These foundations would enable us to realize ethical STSs that incorporate social and technical controls to respect stated ethical postures of the agents in the STSs. The envisioned foundations require new thinking, along two broad themes, on how to realize (1) an STS that reflects its stakeholders' values and (2) individual agents that function effectively in such an STS.
20.
Liu D; Libbi C A; Javdani Rikhtehgar D
What Would You Like to Visit Next? – Using a Knowledge-Graph Driven Museum Guide in a Virtual Exhibition Proceedings Article
In: HHAI2022: Augmenting Human Intellect, pp. 275-277, IOS Press, Amsterdam, the Netherlands, 0000.
@inproceedings{Javdani2022,
title = {What Would You Like to Visit Next? – Using a Knowledge-Graph Driven Museum Guide in a Virtual Exhibition},
author = {Liu, Dou AND Libbi, Claudia Alessandra AND Javdani Rikhtehgar, Delaram},
url = {https://ebooks.iospress.nl/volumearticle/60883},
booktitle = {HHAI2022: Augmenting Human Intellect},
pages = {275-277},
publisher = {IOS Press},
address = {Amsterdam, the Netherlands},
series = {HHAI '22},
abstract = {Conversational agents have been recently incorporated into Virtual Heritage to provide more immersive and interactive user experience. However, existing chatbot guides lack the capacity to leverage the rich background knowledge graphs (KGs) to provide better interactions between visitors and cultural collections. In this paper, we present a KG driven conversational museum guide that answers visitor’s questions and recommend relevant art objects in a virtual exhibition, while modelling user interest to offer personalised information and guidance.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Conversational agents have been recently incorporated into Virtual Heritage to provide more immersive and interactive user experience. However, existing chatbot guides lack the capacity to leverage the rich background knowledge graphs (KGs) to provide better interactions between visitors and cultural collections. In this paper, we present a KG driven conversational museum guide that answers visitor’s questions and recommend relevant art objects in a virtual exhibition, while modelling user interest to offer personalised information and guidance.
19.
Liu D; Libbi C A; Javdani Rikhtehgar D
What Would You Like to Visit Next? – Using a Knowledge-Graph Driven Museum Guide in a Virtual Exhibition Proceedings Article
In: HHAI2022: Augmenting Human Intellect, pp. 275-277, IOS Press, Amsterdam, the Netherlands, 0000.
@inproceedings{Javdani2022b,
title = {What Would You Like to Visit Next? – Using a Knowledge-Graph Driven Museum Guide in a Virtual Exhibition},
author = {Liu, Dou AND Libbi, Claudia Alessandra AND Javdani Rikhtehgar, Delaram},
url = {https://ebooks.iospress.nl/volumearticle/60883},
booktitle = {HHAI2022: Augmenting Human Intellect},
pages = {275-277},
publisher = {IOS Press},
address = {Amsterdam, the Netherlands},
series = {HHAI '22},
abstract = {Conversational agents have been recently incorporated into Virtual Heritage to provide more immersive and interactive user experience. However, existing chatbot guides lack the capacity to leverage the rich background knowledge graphs (KGs) to provide better interactions between visitors and cultural collections. In this paper, we present a KG driven conversational museum guide that answers visitor’s questions and recommend relevant art objects in a virtual exhibition, while modelling user interest to offer personalised information and guidance.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Conversational agents have been recently incorporated into Virtual Heritage to provide more immersive and interactive user experience. However, existing chatbot guides lack the capacity to leverage the rich background knowledge graphs (KGs) to provide better interactions between visitors and cultural collections. In this paper, we present a KG driven conversational museum guide that answers visitor’s questions and recommend relevant art objects in a virtual exhibition, while modelling user interest to offer personalised information and guidance.
18.
Liu D; Libbi C A; Javdani Rikhtehgar D
What Would You Like to Visit Next? – Using a Knowledge-Graph Driven Museum Guide in a Virtual Exhibition Proceedings Article
In: HHAI2022: Augmenting Human Intellect, pp. 275-277, IOS Press, Amsterdam, the Netherlands, 0000.
@inproceedings{Javdani2022c,
title = {What Would You Like to Visit Next? – Using a Knowledge-Graph Driven Museum Guide in a Virtual Exhibition},
author = {Liu, Dou AND Libbi, Claudia Alessandra AND Javdani Rikhtehgar, Delaram},
url = {https://ebooks.iospress.nl/volumearticle/60883},
booktitle = {HHAI2022: Augmenting Human Intellect},
pages = {275-277},
publisher = {IOS Press},
address = {Amsterdam, the Netherlands},
series = {HHAI '22},
abstract = {Conversational agents have been recently incorporated into Virtual Heritage to provide more immersive and interactive user experience. However, existing chatbot guides lack the capacity to leverage the rich background knowledge graphs (KGs) to provide better interactions between visitors and cultural collections. In this paper, we present a KG driven conversational museum guide that answers visitor’s questions and recommend relevant art objects in a virtual exhibition, while modelling user interest to offer personalised information and guidance.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Conversational agents have been recently incorporated into Virtual Heritage to provide more immersive and interactive user experience. However, existing chatbot guides lack the capacity to leverage the rich background knowledge graphs (KGs) to provide better interactions between visitors and cultural collections. In this paper, we present a KG driven conversational museum guide that answers visitor’s questions and recommend relevant art objects in a virtual exhibition, while modelling user interest to offer personalised information and guidance.
17.
Liu D; Libbi C A; Javdani Rikhtehgar D
What Would You Like to Visit Next? – Using a Knowledge-Graph Driven Museum Guide in a Virtual Exhibition Proceedings Article
In: HHAI2022: Augmenting Human Intellect, pp. 275-277, IOS Press, Amsterdam, the Netherlands, 0000.
@inproceedings{Javdani2022d,
title = {What Would You Like to Visit Next? – Using a Knowledge-Graph Driven Museum Guide in a Virtual Exhibition},
author = {Liu, Dou AND Libbi, Claudia Alessandra AND Javdani Rikhtehgar, Delaram},
url = {https://ebooks.iospress.nl/volumearticle/60883},
booktitle = {HHAI2022: Augmenting Human Intellect},
pages = {275-277},
publisher = {IOS Press},
address = {Amsterdam, the Netherlands},
series = {HHAI '22},
abstract = {Conversational agents have been recently incorporated into Virtual Heritage to provide more immersive and interactive user experience. However, existing chatbot guides lack the capacity to leverage the rich background knowledge graphs (KGs) to provide better interactions between visitors and cultural collections. In this paper, we present a KG driven conversational museum guide that answers visitor’s questions and recommend relevant art objects in a virtual exhibition, while modelling user interest to offer personalised information and guidance.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Conversational agents have been recently incorporated into Virtual Heritage to provide more immersive and interactive user experience. However, existing chatbot guides lack the capacity to leverage the rich background knowledge graphs (KGs) to provide better interactions between visitors and cultural collections. In this paper, we present a KG driven conversational museum guide that answers visitor’s questions and recommend relevant art objects in a virtual exhibition, while modelling user interest to offer personalised information and guidance.
16.
Liu D; Libbi C A; Javdani Rikhtehgar D
What Would You Like to Visit Next? – Using a Knowledge-Graph Driven Museum Guide in a Virtual Exhibition Proceedings Article
In: HHAI2022: Augmenting Human Intellect, pp. 275-277, IOS Press, Amsterdam, the Netherlands, 0000.
@inproceedings{Javdani2022e,
title = {What Would You Like to Visit Next? – Using a Knowledge-Graph Driven Museum Guide in a Virtual Exhibition},
author = {Liu, Dou AND Libbi, Claudia Alessandra AND Javdani Rikhtehgar, Delaram},
url = {https://ebooks.iospress.nl/volumearticle/60883},
booktitle = {HHAI2022: Augmenting Human Intellect},
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abstract = {Conversational agents have been recently incorporated into Virtual Heritage to provide more immersive and interactive user experience. However, existing chatbot guides lack the capacity to leverage the rich background knowledge graphs (KGs) to provide better interactions between visitors and cultural collections. In this paper, we present a KG driven conversational museum guide that answers visitor’s questions and recommend relevant art objects in a virtual exhibition, while modelling user interest to offer personalised information and guidance.},
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Conversational agents have been recently incorporated into Virtual Heritage to provide more immersive and interactive user experience. However, existing chatbot guides lack the capacity to leverage the rich background knowledge graphs (KGs) to provide better interactions between visitors and cultural collections. In this paper, we present a KG driven conversational museum guide that answers visitor’s questions and recommend relevant art objects in a virtual exhibition, while modelling user interest to offer personalised information and guidance.
15.
Liu D; Libbi C A; Javdani Rikhtehgar D
What Would You Like to Visit Next? – Using a Knowledge-Graph Driven Museum Guide in a Virtual Exhibition Proceedings Article
In: HHAI2022: Augmenting Human Intellect, pp. 275-277, IOS Press, Amsterdam, the Netherlands, 0000.
@inproceedings{Javdani2022f,
title = {What Would You Like to Visit Next? – Using a Knowledge-Graph Driven Museum Guide in a Virtual Exhibition},
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Conversational agents have been recently incorporated into Virtual Heritage to provide more immersive and interactive user experience. However, existing chatbot guides lack the capacity to leverage the rich background knowledge graphs (KGs) to provide better interactions between visitors and cultural collections. In this paper, we present a KG driven conversational museum guide that answers visitor’s questions and recommend relevant art objects in a virtual exhibition, while modelling user interest to offer personalised information and guidance.
14.
Liu D; Libbi C A; Javdani Rikhtehgar D
What Would You Like to Visit Next? – Using a Knowledge-Graph Driven Museum Guide in a Virtual Exhibition Proceedings Article
In: HHAI2022: Augmenting Human Intellect, pp. 275-277, IOS Press, Amsterdam, the Netherlands, 0000.
@inproceedings{Javdani2022g,
title = {What Would You Like to Visit Next? – Using a Knowledge-Graph Driven Museum Guide in a Virtual Exhibition},
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Conversational agents have been recently incorporated into Virtual Heritage to provide more immersive and interactive user experience. However, existing chatbot guides lack the capacity to leverage the rich background knowledge graphs (KGs) to provide better interactions between visitors and cultural collections. In this paper, we present a KG driven conversational museum guide that answers visitor’s questions and recommend relevant art objects in a virtual exhibition, while modelling user interest to offer personalised information and guidance.
13.
Liu D; Libbi C A; Javdani Rikhtehgar D
What Would You Like to Visit Next? – Using a Knowledge-Graph Driven Museum Guide in a Virtual Exhibition Proceedings Article
In: HHAI2022: Augmenting Human Intellect, pp. 275-277, IOS Press, Amsterdam, the Netherlands, 0000.
@inproceedings{Javdani2022h,
title = {What Would You Like to Visit Next? – Using a Knowledge-Graph Driven Museum Guide in a Virtual Exhibition},
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abstract = {Conversational agents have been recently incorporated into Virtual Heritage to provide more immersive and interactive user experience. However, existing chatbot guides lack the capacity to leverage the rich background knowledge graphs (KGs) to provide better interactions between visitors and cultural collections. In this paper, we present a KG driven conversational museum guide that answers visitor’s questions and recommend relevant art objects in a virtual exhibition, while modelling user interest to offer personalised information and guidance.},
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Conversational agents have been recently incorporated into Virtual Heritage to provide more immersive and interactive user experience. However, existing chatbot guides lack the capacity to leverage the rich background knowledge graphs (KGs) to provide better interactions between visitors and cultural collections. In this paper, we present a KG driven conversational museum guide that answers visitor’s questions and recommend relevant art objects in a virtual exhibition, while modelling user interest to offer personalised information and guidance.
12.
Liu D; Libbi C A; Javdani Rikhtehgar D
What Would You Like to Visit Next? – Using a Knowledge-Graph Driven Museum Guide in a Virtual Exhibition Proceedings Article
In: HHAI2022: Augmenting Human Intellect, pp. 275-277, IOS Press, Amsterdam, the Netherlands, 0000.
@inproceedings{Javdani2022i,
title = {What Would You Like to Visit Next? – Using a Knowledge-Graph Driven Museum Guide in a Virtual Exhibition},
author = {Liu, Dou AND Libbi, Claudia Alessandra AND Javdani Rikhtehgar, Delaram},
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abstract = {Conversational agents have been recently incorporated into Virtual Heritage to provide more immersive and interactive user experience. However, existing chatbot guides lack the capacity to leverage the rich background knowledge graphs (KGs) to provide better interactions between visitors and cultural collections. In this paper, we present a KG driven conversational museum guide that answers visitor’s questions and recommend relevant art objects in a virtual exhibition, while modelling user interest to offer personalised information and guidance.},
keywords = {},
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Conversational agents have been recently incorporated into Virtual Heritage to provide more immersive and interactive user experience. However, existing chatbot guides lack the capacity to leverage the rich background knowledge graphs (KGs) to provide better interactions between visitors and cultural collections. In this paper, we present a KG driven conversational museum guide that answers visitor’s questions and recommend relevant art objects in a virtual exhibition, while modelling user interest to offer personalised information and guidance.
11.
Liu D; Libbi C A; Javdani Rikhtehgar D
What Would You Like to Visit Next? – Using a Knowledge-Graph Driven Museum Guide in a Virtual Exhibition Proceedings Article
In: HHAI2022: Augmenting Human Intellect, pp. 275-277, IOS Press, Amsterdam, the Netherlands, 0000.
@inproceedings{Javdani2022j,
title = {What Would You Like to Visit Next? – Using a Knowledge-Graph Driven Museum Guide in a Virtual Exhibition},
author = {Liu, Dou AND Libbi, Claudia Alessandra AND Javdani Rikhtehgar, Delaram},
url = {https://ebooks.iospress.nl/volumearticle/60883},
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abstract = {Conversational agents have been recently incorporated into Virtual Heritage to provide more immersive and interactive user experience. However, existing chatbot guides lack the capacity to leverage the rich background knowledge graphs (KGs) to provide better interactions between visitors and cultural collections. In this paper, we present a KG driven conversational museum guide that answers visitor’s questions and recommend relevant art objects in a virtual exhibition, while modelling user interest to offer personalised information and guidance.},
keywords = {},
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Conversational agents have been recently incorporated into Virtual Heritage to provide more immersive and interactive user experience. However, existing chatbot guides lack the capacity to leverage the rich background knowledge graphs (KGs) to provide better interactions between visitors and cultural collections. In this paper, we present a KG driven conversational museum guide that answers visitor’s questions and recommend relevant art objects in a virtual exhibition, while modelling user interest to offer personalised information and guidance.
10.
Liu D; Libbi C A; Javdani Rikhtehgar D
What Would You Like to Visit Next? – Using a Knowledge-Graph Driven Museum Guide in a Virtual Exhibition Proceedings Article
In: HHAI2022: Augmenting Human Intellect, pp. 275-277, IOS Press, Amsterdam, the Netherlands, 0000.
@inproceedings{Javdani2022k,
title = {What Would You Like to Visit Next? – Using a Knowledge-Graph Driven Museum Guide in a Virtual Exhibition},
author = {Liu, Dou AND Libbi, Claudia Alessandra AND Javdani Rikhtehgar, Delaram},
url = {https://ebooks.iospress.nl/volumearticle/60883},
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abstract = {Conversational agents have been recently incorporated into Virtual Heritage to provide more immersive and interactive user experience. However, existing chatbot guides lack the capacity to leverage the rich background knowledge graphs (KGs) to provide better interactions between visitors and cultural collections. In this paper, we present a KG driven conversational museum guide that answers visitor’s questions and recommend relevant art objects in a virtual exhibition, while modelling user interest to offer personalised information and guidance.},
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Conversational agents have been recently incorporated into Virtual Heritage to provide more immersive and interactive user experience. However, existing chatbot guides lack the capacity to leverage the rich background knowledge graphs (KGs) to provide better interactions between visitors and cultural collections. In this paper, we present a KG driven conversational museum guide that answers visitor’s questions and recommend relevant art objects in a virtual exhibition, while modelling user interest to offer personalised information and guidance.
9.
Liu D; Libbi C A; Javdani Rikhtehgar D
What Would You Like to Visit Next? – Using a Knowledge-Graph Driven Museum Guide in a Virtual Exhibition Proceedings Article
In: HHAI2022: Augmenting Human Intellect, pp. 275-277, IOS Press, Amsterdam, the Netherlands, 0000.
@inproceedings{Javdani2022l,
title = {What Would You Like to Visit Next? – Using a Knowledge-Graph Driven Museum Guide in a Virtual Exhibition},
author = {Liu, Dou AND Libbi, Claudia Alessandra AND Javdani Rikhtehgar, Delaram},
url = {https://ebooks.iospress.nl/volumearticle/60883},
booktitle = {HHAI2022: Augmenting Human Intellect},
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abstract = {Conversational agents have been recently incorporated into Virtual Heritage to provide more immersive and interactive user experience. However, existing chatbot guides lack the capacity to leverage the rich background knowledge graphs (KGs) to provide better interactions between visitors and cultural collections. In this paper, we present a KG driven conversational museum guide that answers visitor’s questions and recommend relevant art objects in a virtual exhibition, while modelling user interest to offer personalised information and guidance.},
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Conversational agents have been recently incorporated into Virtual Heritage to provide more immersive and interactive user experience. However, existing chatbot guides lack the capacity to leverage the rich background knowledge graphs (KGs) to provide better interactions between visitors and cultural collections. In this paper, we present a KG driven conversational museum guide that answers visitor’s questions and recommend relevant art objects in a virtual exhibition, while modelling user interest to offer personalised information and guidance.
8.
Liu D; Libbi C A; Javdani Rikhtehgar D
What Would You Like to Visit Next? – Using a Knowledge-Graph Driven Museum Guide in a Virtual Exhibition Proceedings Article
In: HHAI2022: Augmenting Human Intellect, pp. 275-277, IOS Press, Amsterdam, the Netherlands, 0000.
@inproceedings{Javdani2022m,
title = {What Would You Like to Visit Next? – Using a Knowledge-Graph Driven Museum Guide in a Virtual Exhibition},
author = {Liu, Dou AND Libbi, Claudia Alessandra AND Javdani Rikhtehgar, Delaram},
url = {https://ebooks.iospress.nl/volumearticle/60883},
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abstract = {Conversational agents have been recently incorporated into Virtual Heritage to provide more immersive and interactive user experience. However, existing chatbot guides lack the capacity to leverage the rich background knowledge graphs (KGs) to provide better interactions between visitors and cultural collections. In this paper, we present a KG driven conversational museum guide that answers visitor’s questions and recommend relevant art objects in a virtual exhibition, while modelling user interest to offer personalised information and guidance.},
keywords = {},
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Conversational agents have been recently incorporated into Virtual Heritage to provide more immersive and interactive user experience. However, existing chatbot guides lack the capacity to leverage the rich background knowledge graphs (KGs) to provide better interactions between visitors and cultural collections. In this paper, we present a KG driven conversational museum guide that answers visitor’s questions and recommend relevant art objects in a virtual exhibition, while modelling user interest to offer personalised information and guidance.
7.
Liu D; Libbi C A; Javdani Rikhtehgar D
What Would You Like to Visit Next? – Using a Knowledge-Graph Driven Museum Guide in a Virtual Exhibition Proceedings Article
In: HHAI2022: Augmenting Human Intellect, pp. 275-277, IOS Press, Amsterdam, the Netherlands, 0000.
@inproceedings{Javdani2022n,
title = {What Would You Like to Visit Next? – Using a Knowledge-Graph Driven Museum Guide in a Virtual Exhibition},
author = {Liu, Dou AND Libbi, Claudia Alessandra AND Javdani Rikhtehgar, Delaram},
url = {https://ebooks.iospress.nl/volumearticle/60883},
booktitle = {HHAI2022: Augmenting Human Intellect},
pages = {275-277},
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series = {HHAI '22},
abstract = {Conversational agents have been recently incorporated into Virtual Heritage to provide more immersive and interactive user experience. However, existing chatbot guides lack the capacity to leverage the rich background knowledge graphs (KGs) to provide better interactions between visitors and cultural collections. In this paper, we present a KG driven conversational museum guide that answers visitor’s questions and recommend relevant art objects in a virtual exhibition, while modelling user interest to offer personalised information and guidance.},
keywords = {},
pubstate = {published},
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Conversational agents have been recently incorporated into Virtual Heritage to provide more immersive and interactive user experience. However, existing chatbot guides lack the capacity to leverage the rich background knowledge graphs (KGs) to provide better interactions between visitors and cultural collections. In this paper, we present a KG driven conversational museum guide that answers visitor’s questions and recommend relevant art objects in a virtual exhibition, while modelling user interest to offer personalised information and guidance.
6.
Liu D; Libbi C A; Javdani Rikhtehgar D
What Would You Like to Visit Next? – Using a Knowledge-Graph Driven Museum Guide in a Virtual Exhibition Proceedings Article
In: HHAI2022: Augmenting Human Intellect, pp. 275-277, IOS Press, Amsterdam, the Netherlands, 0000.
@inproceedings{Javdani2022o,
title = {What Would You Like to Visit Next? – Using a Knowledge-Graph Driven Museum Guide in a Virtual Exhibition},
author = {Liu, Dou AND Libbi, Claudia Alessandra AND Javdani Rikhtehgar, Delaram},
url = {https://ebooks.iospress.nl/volumearticle/60883},
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abstract = {Conversational agents have been recently incorporated into Virtual Heritage to provide more immersive and interactive user experience. However, existing chatbot guides lack the capacity to leverage the rich background knowledge graphs (KGs) to provide better interactions between visitors and cultural collections. In this paper, we present a KG driven conversational museum guide that answers visitor’s questions and recommend relevant art objects in a virtual exhibition, while modelling user interest to offer personalised information and guidance.},
keywords = {},
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Conversational agents have been recently incorporated into Virtual Heritage to provide more immersive and interactive user experience. However, existing chatbot guides lack the capacity to leverage the rich background knowledge graphs (KGs) to provide better interactions between visitors and cultural collections. In this paper, we present a KG driven conversational museum guide that answers visitor’s questions and recommend relevant art objects in a virtual exhibition, while modelling user interest to offer personalised information and guidance.
5.
van Woerkom ; Grossi D; Prakken H; Verheij B
Hierarchical empha Fortiori Reasoning with Dimensions Book Section
In: Sileno, Giovanni; Spanakis, Jerry; family=Dijck, (Ed.): Legal Knowledge and Information Systems. JURIX 2023: The Thirty-sixth Annual Conference, pp. 43–52, IOS Press, 0000.
@incollection{vanwoerkom2023HierarchicalFortiori,
title = {Hierarchical empha Fortiori Reasoning with Dimensions},
author = {van Woerkom, Wijnand, and Grossi, Davide and Prakken, Henry and Verheij, Bart},
editor = {{Sileno, Giovanni and Spanakis, Jerry and family=Dijck},
doi = {10.3233/FAIA230944},
booktitle = {Legal Knowledge and Information Systems. JURIX 2023: The Thirty-sixth Annual Conference},
pages = {43–52},
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4.
Liu D; Libbi C A; Javdani Rikhtehgar D
What Would You Like to Visit Next? – Using a Knowledge-Graph Driven Museum Guide in a Virtual Exhibition Proceedings Article
In: HHAI2022: Augmenting Human Intellect, pp. 275-277, IOS Press, Amsterdam, the Netherlands, 0000.
@inproceedings{Javdani2022p,
title = {What Would You Like to Visit Next? – Using a Knowledge-Graph Driven Museum Guide in a Virtual Exhibition},
author = {Liu, Dou AND Libbi, Claudia Alessandra AND Javdani Rikhtehgar, Delaram},
url = {https://ebooks.iospress.nl/volumearticle/60883},
booktitle = {HHAI2022: Augmenting Human Intellect},
pages = {275-277},
publisher = {IOS Press},
address = {Amsterdam, the Netherlands},
series = {HHAI '22},
abstract = {Conversational agents have been recently incorporated into Virtual Heritage to provide more immersive and interactive user experience. However, existing chatbot guides lack the capacity to leverage the rich background knowledge graphs (KGs) to provide better interactions between visitors and cultural collections. In this paper, we present a KG driven conversational museum guide that answers visitor’s questions and recommend relevant art objects in a virtual exhibition, while modelling user interest to offer personalised information and guidance.},
keywords = {},
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Conversational agents have been recently incorporated into Virtual Heritage to provide more immersive and interactive user experience. However, existing chatbot guides lack the capacity to leverage the rich background knowledge graphs (KGs) to provide better interactions between visitors and cultural collections. In this paper, we present a KG driven conversational museum guide that answers visitor’s questions and recommend relevant art objects in a virtual exhibition, while modelling user interest to offer personalised information and guidance.
3.
Liu D; Libbi C A; Javdani Rikhtehgar D
What Would You Like to Visit Next? – Using a Knowledge-Graph Driven Museum Guide in a Virtual Exhibition Proceedings Article
In: HHAI2022: Augmenting Human Intellect, pp. 275-277, IOS Press, Amsterdam, the Netherlands, 0000.
@inproceedings{Javdani2022q,
title = {What Would You Like to Visit Next? – Using a Knowledge-Graph Driven Museum Guide in a Virtual Exhibition},
author = {Liu, Dou AND Libbi, Claudia Alessandra AND Javdani Rikhtehgar, Delaram},
url = {https://ebooks.iospress.nl/volumearticle/60883},
booktitle = {HHAI2022: Augmenting Human Intellect},
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publisher = {IOS Press},
address = {Amsterdam, the Netherlands},
series = {HHAI '22},
abstract = {Conversational agents have been recently incorporated into Virtual Heritage to provide more immersive and interactive user experience. However, existing chatbot guides lack the capacity to leverage the rich background knowledge graphs (KGs) to provide better interactions between visitors and cultural collections. In this paper, we present a KG driven conversational museum guide that answers visitor’s questions and recommend relevant art objects in a virtual exhibition, while modelling user interest to offer personalised information and guidance.},
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Conversational agents have been recently incorporated into Virtual Heritage to provide more immersive and interactive user experience. However, existing chatbot guides lack the capacity to leverage the rich background knowledge graphs (KGs) to provide better interactions between visitors and cultural collections. In this paper, we present a KG driven conversational museum guide that answers visitor’s questions and recommend relevant art objects in a virtual exhibition, while modelling user interest to offer personalised information and guidance.
2.
Liu D; Libbi C A; Javdani Rikhtehgar D
What Would You Like to Visit Next? – Using a Knowledge-Graph Driven Museum Guide in a Virtual Exhibition Proceedings Article
In: HHAI2022: Augmenting Human Intellect, pp. 275-277, IOS Press, Amsterdam, the Netherlands, 0000.
@inproceedings{Javdani2022r,
title = {What Would You Like to Visit Next? – Using a Knowledge-Graph Driven Museum Guide in a Virtual Exhibition},
author = {Liu, Dou AND Libbi, Claudia Alessandra AND Javdani Rikhtehgar, Delaram},
url = {https://ebooks.iospress.nl/volumearticle/60883},
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abstract = {Conversational agents have been recently incorporated into Virtual Heritage to provide more immersive and interactive user experience. However, existing chatbot guides lack the capacity to leverage the rich background knowledge graphs (KGs) to provide better interactions between visitors and cultural collections. In this paper, we present a KG driven conversational museum guide that answers visitor’s questions and recommend relevant art objects in a virtual exhibition, while modelling user interest to offer personalised information and guidance.},
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Conversational agents have been recently incorporated into Virtual Heritage to provide more immersive and interactive user experience. However, existing chatbot guides lack the capacity to leverage the rich background knowledge graphs (KGs) to provide better interactions between visitors and cultural collections. In this paper, we present a KG driven conversational museum guide that answers visitor’s questions and recommend relevant art objects in a virtual exhibition, while modelling user interest to offer personalised information and guidance.
1.
van Woerkom ; Grossi D; Prakken H; Verheij B
A Case-Based-Reasoning Analysis of the COMPAS Dataset Book Section
In: Legal Knowledge and Information Systems. JURIX 2024: The Thirty-seventh Annual Conference, IOS Press, 0000.
@incollection{vanwoerkom2024CaseBasedReasoningAnalysis,
title = {A Case-Based-Reasoning Analysis of the COMPAS Dataset},
author = {van Woerkom, Wijnand, and Grossi, Davide and Prakken, Henry and Verheij, Bart},
url = {https://ebooks.iospress.nl/volumearticle/71009},
booktitle = {Legal Knowledge and Information Systems. JURIX 2024: The Thirty-seventh Annual Conference},
publisher = {IOS Press},
abstract = {In this paper we build on a formal model of reasoning with dimensions to analyze data from the COMPAS program—a widely used and studied tool for predicting recidivism. We extend the underlying theory of the model by introducing a notion of consistency and apply it to assess whether COMPAS follows this principle in its risk assessments and supervision level recommendations. Our analysis yields three key findings. First, the program’s risk score assignments appear highly inconsistent, but we argue this is due to important input features missing from the dataset. Second, the program’s recommended supervision levels do exhibit a high degree of consistency. Third, we uncover errors in the dataset related to the conversion of raw scores to decile scores. These findings cast doubts on previous studies conducted on the COMPAS dataset, and demonstrate the need for evaluation studies like ours.},
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pubstate = {published},
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In this paper we build on a formal model of reasoning with dimensions to analyze data from the COMPAS program—a widely used and studied tool for predicting recidivism. We extend the underlying theory of the model by introducing a notion of consistency and apply it to assess whether COMPAS follows this principle in its risk assessments and supervision level recommendations. Our analysis yields three key findings. First, the program’s risk score assignments appear highly inconsistent, but we argue this is due to important input features missing from the dataset. Second, the program’s recommended supervision levels do exhibit a high degree of consistency. Third, we uncover errors in the dataset related to the conversion of raw scores to decile scores. These findings cast doubts on previous studies conducted on the COMPAS dataset, and demonstrate the need for evaluation studies like ours.