12.
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.},
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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.
11.
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}
}
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.
10.
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 = {},
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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.
9.
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.
8.
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.
7.
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},
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}
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 .
6.
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},
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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.
5.
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},
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}
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.
4.
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.
3.
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},
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}
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.
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{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},
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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.
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.