Publications

2022

  • E. van Krieken, E. Acar, and F. van Harmelen, “Analyzing differentiable fuzzy logic operators,” Artificial Intelligence, vol. 302, p. 103602, 2022.
    [BibTeX] [Abstract] [Download PDF]

    The AI community is increasingly putting its attention towards combining symbolic and neural approaches, as it is often argued that the strengths and weaknesses of these approaches are complementary. One recent trend in the literature are weakly supervised learning techniques that employ operators from fuzzy logics. In particular, these use prior background knowledge described in such logics to help the training of a neural network from unlabeled and noisy data. By interpreting logical symbols using neural networks, this background knowledge can be added to regular loss functions, hence making reasoning a part of learning. We study, both formally and empirically, how a large collection of logical operators from the fuzzy logic literature behave in a differentiable learning setting. We find that many of these operators, including some of the most well-known, are highly unsuitable in this setting. A further finding concerns the treatment of implication in these fuzzy logics, and shows a strong imbalance between gradients driven by the antecedent and the consequent of the implication. Furthermore, we introduce a new family of fuzzy implications (called sigmoidal implications) to tackle this phenomenon. Finally, we empirically show that it is possible to use Differentiable Fuzzy Logics for semi-supervised learning, and compare how different operators behave in practice. We find that, to achieve the largest performance improvement over a supervised baseline, we have to resort to non-standard combinations of logical operators which perform well in learning, but no longer satisfy the usual logical laws.

    @article{van2022analyzing,
    title = {Analyzing differentiable fuzzy logic operators},
    author = {van Krieken, Emile and Acar, Erman and van Harmelen,
    Frank},
    journal = {Artificial Intelligence},
    volume = {302},
    pages = {103602},
    year = {2022},
    publisher = {Elsevier},
    url = "https://research.vu.nl/ws/portalfiles/portal/146020254/2002.06100v2.pdf",
    abstract = "The AI community is increasingly putting its
    attention towards combining symbolic and neural
    approaches, as it is often argued that the strengths
    and weaknesses of these approaches are
    complementary. One recent trend in the literature
    are weakly supervised learning techniques that
    employ operators from fuzzy logics. In particular,
    these use prior background knowledge described in
    such logics to help the training of a neural network
    from unlabeled and noisy data. By interpreting
    logical symbols using neural networks, this
    background knowledge can be added to regular loss
    functions, hence making reasoning a part of
    learning. We study, both formally and empirically,
    how a large collection of logical operators from the
    fuzzy logic literature behave in a differentiable
    learning setting. We find that many of these
    operators, including some of the most well-known,
    are highly unsuitable in this setting. A further
    finding concerns the treatment of implication in
    these fuzzy logics, and shows a strong imbalance
    between gradients driven by the antecedent and the
    consequent of the implication. Furthermore, we
    introduce a new family of fuzzy implications (called
    sigmoidal implications) to tackle this
    phenomenon. Finally, we empirically show that it is
    possible to use Differentiable Fuzzy Logics for
    semi-supervised learning, and compare how different
    operators behave in practice. We find that, to
    achieve the largest performance improvement over a
    supervised baseline, we have to resort to
    non-standard combinations of logical operators which
    perform well in learning, but no longer satisfy the
    usual logical laws."
    }

  • F. Sarvi, M. Heuss, M. Aliannejadi, S. Schelter, and M. de Rijke, “Understanding and Mitigating the Effect of Outliers in Fair Ranking,” in WSDM 2022: The Fifteenth International Conference on Web Search and Data Mining, 2022.
    [BibTeX] [Abstract] [Download PDF]

    Traditional ranking systems are expected to sort items in the order of their relevance and thereby maximize their utility. In fair ranking, utility is complemented with fairness as an optimization goal. Recent work on fair ranking focuses on developing algorithms to optimize for fairness, given position-based exposure. In contrast, we identify the potential of outliers in a ranking to influence exposure and thereby negatively impact fairness. An outlier in a list of items can alter the examination probabilities, which can lead to different distributions of attention, compared to position-based exposure. We formalize outlierness in a ranking, show that outliers are present in realistic datasets, and present the results of an eye-tracking study, showing that users scanning order and the exposure of items are influenced by the presence of outliers. We then introduce OMIT, a method for fair ranking in the presence of outliers. Given an outlier detection method, OMIT improves fair allocation of exposure by suppressing outliers in the top-k ranking. Using an academic search dataset, we show that outlierness optimization leads to a fairer policy that displays fewer outliers in the top-k, while maintaining a reasonable trade-off between fairness and utility.

    @inproceedings{sarvi-2022-understanding,
    author = {Sarvi, Fatemeh and Heuss, Maria and Aliannejadi,
    Mohammad and Schelter, Sebastian and de Rijke,
    Maarten},
    booktitle = {WSDM 2022: The Fifteenth International Conference on
    Web Search and Data Mining},
    month = {February},
    publisher = {ACM},
    title = {Understanding and Mitigating the Effect of Outliers
    in Fair Ranking},
    year = {2022},
    url = {https://arxiv.org/abs/2112.11251},
    abstract = "Traditional ranking systems are expected to sort
    items in the order of their relevance and thereby
    maximize their utility. In fair ranking, utility is
    complemented with fairness as an optimization
    goal. Recent work on fair ranking focuses on
    developing algorithms to optimize for fairness,
    given position-based exposure. In contrast, we
    identify the potential of outliers in a ranking to
    influence exposure and thereby negatively impact
    fairness. An outlier in a list of items can alter
    the examination probabilities, which can lead to
    different distributions of attention, compared to
    position-based exposure. We formalize outlierness in
    a ranking, show that outliers are present in
    realistic datasets, and present the results of an
    eye-tracking study, showing that users scanning
    order and the exposure of items are influenced by
    the presence of outliers. We then introduce OMIT, a
    method for fair ranking in the presence of
    outliers. Given an outlier detection method, OMIT
    improves fair allocation of exposure by suppressing
    outliers in the top-k ranking. Using an academic
    search dataset, we show that outlierness
    optimization leads to a fairer policy that displays
    fewer outliers in the top-k, while maintaining a
    reasonable trade-off between fairness and utility."
    }

  • E. Liscio, M. van der Meer, L. C. Siebert, C. M. Jonker, and P. K. Murukannaiah, “What values should an agent align with?,” Autonomous Agents and Multi-Agent Systems, vol. 36, iss. 23, 2022. doi:10.1007/s10458-022-09550-0
    [BibTeX] [Abstract] [Download PDF]

    The pursuit of values drives human behavior and promotes cooperation. Existing research is focused on general values (e.g., Schwartz) that transcend contexts. However, context-specific values are necessary to (1) understand human decisions, and (2) engineer intelligent agents that can elicit and align with human values. 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 80 human subjects. In our study, six annotators generate value lists for two timely and important contexts: Covid-19 measures and sustainable Energy. We employ two policy experts and 72 crowd workers to evaluate Axies value lists and compare them to a list of general (Schwartz) values. We find that Axies yields values that are (1) more context-specific than general values, (2) more suitable for value annotation than general values, and (3) independent of the people applying the methodology.

    @Article{Liscio2022,
    author = {Liscio, Enrico and van der Meer, Michiel and Siebert, Luciano C. and Jonker, Catholijn M. and Murukannaiah, Pradeep K.},
    title = {What values should an agent align with?},
    journal = {Autonomous Agents and Multi-Agent Systems},
    year = 2022,
    volume = 36,
    number = 23,
    month = {March},
    DOI = {10.1007/s10458-022-09550-0},
    URL = {https://doi.org/10.1007/s10458-022-09550-0},
    abstract = {The pursuit of values drives human behavior and promotes cooperation. Existing research is focused on general values (e.g., Schwartz) that transcend contexts. However, context-specific values are necessary to (1) understand human decisions, and (2) engineer intelligent agents that can elicit and align with human values. 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 80 human subjects. In our study, six annotators generate value lists for two timely and important contexts: Covid-19 measures and sustainable Energy. We employ two policy experts and 72 crowd workers to evaluate Axies value lists and compare them to a list of general (Schwartz) values. We find that Axies yields values that are (1) more context-specific than general values, (2) more suitable for value annotation than general values, and (3) independent of the people applying the methodology.}
    }

  • G. Nadizar, E. Medvet, and K. Miras, “On the Schedule for Morphological Development of Evolved Modular Soft Robots,” in European Conference on Genetic Programming (Part of EvoStar), 2022, p. 146–161.
    [BibTeX] [Abstract] [Download PDF]

    Development is fundamental for living beings. As robots are often designed to mimic biological organisms, development is believed to be crucial for achieving successful results in robotic agents, as well. What is not clear, though, is the most appropriate scheduling for development. While in real life systems development happens mostly during the initial growth phase of organisms, it has not yet been investigated whether such assumption holds also for artificial creatures. In this paper, we employ a evolutionary approach to optimize the development—according to different representations—of Voxel-based Soft Robots (VSRs), a kind of modular robots. In our study, development consists in the addition of new voxels to the VSR, at fixed time instants, depending on the development schedule. We experiment with different schedules and show that, similarly to living organisms, artificial agents benefit from development occurring at early stages of life more than from development lasting for their entire life.

    @inproceedings{nadizar2022schedule,
    title={On the Schedule for Morphological Development of Evolved Modular Soft Robots},
    author={Nadizar, Giorgia and Medvet, Eric and Miras, Karine},
    booktitle={European Conference on Genetic Programming (Part of EvoStar)},
    pages={146--161},
    year={2022},
    organization={Springer},
    url={https://link.springer.com/chapter/10.1007/978-3-031-02056-8_10} ,
    abstract={Development is fundamental for living beings. As robots are often designed to mimic biological organisms, development is believed to be crucial for achieving successful results in robotic agents, as well. What is not clear, though, is the most appropriate scheduling for development. While in real life systems development happens mostly during the initial growth phase of organisms, it has not yet been investigated whether such assumption holds also for artificial creatures. In this paper, we employ a evolutionary approach to optimize the development—according to different representations—of Voxel-based Soft Robots (VSRs), a kind of modular robots. In our study, development consists in the addition of new voxels to the VSR, at fixed time instants, depending on the development schedule. We experiment with different schedules and show that, similarly to living organisms, artificial agents benefit from development occurring at early stages of life more than from development lasting for their entire life.}
    }

  • J. Kiseleva, Z. Li, M. Aliannejadi, S. Mohanty, M. ter Hoeve, M. Burtsev, A. Skrynnik, A. Zholus, A. Panov, K. Srinet, A. Szlam, Y. Sun, M. K. Côté. Hofmann, A. Awadallah, L. Abdrazakov, I. Churin, P. Manggala, K. Naszadi, M. van der Meer, and T. Kim, “Interactive Grounded Language Understanding in a Collaborative Environment: IGLU 2021,” , 2022. doi:10.48550/ARXIV.2205.02388
    [BibTeX] [Abstract] [Download PDF]

    Human intelligence has the remarkable ability to quickly adapt to new tasks and environments. Starting from a very young age, humans acquire new skills and learn how to solve new tasks either by imitating the behavior of others or by following provided natural language instructions. To facilitate research in this direction, we propose \emph{IGLU: Interactive Grounded Language Understanding in a Collaborative Environment}. The primary goal of the competition is to approach the problem of how to build interactive agents that learn to solve a task while provided with grounded natural language instructions in a collaborative environment. Understanding the complexity of the challenge, we split it into sub-tasks to make it feasible for participants.

    @article{IGLU2022,
    author = {Kiseleva, Julia and Li, Ziming and Aliannejadi, Mohammad
    and Mohanty, Shrestha and ter Hoeve, Maartje and
    Burtsev, Mikhail and Skrynnik, Alexey and Zholus,
    Artem and Panov, Aleksandr and Srinet, Kavya and
    Szlam, Arthur and Sun, Yuxuan and Hofmann,
    Marc-Alexandre Côté Katja and Awadallah, Ahmed and
    Abdrazakov, Linar and Churin, Igor and Manggala,
    Putra and Naszadi, Kata and van der Meer, Michiel
    and Kim, Taewoon},
    keywords = {Computation and Language (cs.CL), Artificial
    Intelligence (cs.AI), FOS: Computer and information
    sciences, FOS: Computer and information sciences},
    title = {Interactive Grounded Language Understanding in a
    Collaborative Environment: IGLU 2021},
    publisher = {arXiv},
    year = {2022},
    abstract = "Human intelligence has the remarkable ability to quickly
    adapt to new tasks and environments. Starting from a
    very young age, humans acquire new skills and learn
    how to solve new tasks either by imitating the
    behavior of others or by following provided natural
    language instructions. To facilitate research in
    this direction, we propose \emph{IGLU: Interactive
    Grounded Language Understanding in a Collaborative
    Environment}. The primary goal of the competition
    is to approach the problem of how to build
    interactive agents that learn to solve a task while
    provided with grounded natural language instructions
    in a collaborative environment. Understanding the
    complexity of the challenge, we split it into
    sub-tasks to make it feasible for participants.",year = {2022},
    doi = {10.48550/ARXIV.2205.02388},
    url = {https://arxiv.org/abs/2205.02388},
    copyright = {Creative Commons Attribution Share Alike 4.0 International}
    }

  • D. Grossi, “Social Choice Around the Block: On the Computational Social Choice of Blockchain,” in 21st International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2022, Auckland, New Zealand, May 9-13, 2022, 2022, p. 1788–1793.
    [BibTeX] [Abstract] [Download PDF]

    One of the most innovative aspects of blockchain technology con- sists in the introduction of an incentive layer to regulate the behav- ior of distributed protocols. The designer of a blockchain system faces therefore issues that are akin to those relevant for the design of economic mechanisms, and faces them in a computational setting. From this perspective the present paper argues for the importance of computational social choice in blockchain research. It identifies a few challenges at the interface of the two fields that illustrate the strong potential for cross-fertilization between them.

    @inproceedings{DBLP:conf/atal/Grossi22,
    author = {Davide Grossi},
    editor = {Piotr Faliszewski and
    Viviana Mascardi and
    Catherine Pelachaud and
    Matthew E. Taylor},
    title = {Social Choice Around the Block: On the Computational Social Choice
    of Blockchain},
    booktitle = {21st International Conference on Autonomous Agents and Multiagent
    Systems, {AAMAS} 2022, Auckland, New Zealand, May 9-13, 2022},
    pages = {1788--1793},
    publisher = {International Foundation for Autonomous Agents and Multiagent Systems
    {(IFAAMAS)}},
    year = {2022},
    url = {https://www.ifaamas.org/Proceedings/aamas2022/pdfs/p1788.pdf},
    abstract = {One of the most innovative aspects of blockchain technology con-
    sists in the introduction of an incentive layer to regulate the behav-
    ior of distributed protocols. The designer of a blockchain system
    faces therefore issues that are akin to those relevant for the design
    of economic mechanisms, and faces them in a computational setting.
    From this perspective the present paper argues for the importance
    of computational social choice in blockchain research. It identifies
    a few challenges at the interface of the two fields that illustrate the
    strong potential for cross-fertilization between them.}
    }

2021

  • M. van Bekkum, M. de Boer, F. van Harmelen, A. M. -, and A. ten Teije, “Modular design patterns for hybrid learning and reasoning systems,” Appl. Intell., vol. 51, iss. 9, p. 6528–6546, 2021. doi:10.1007/s10489-021-02394-3
    [BibTeX]
    @article{DBLP:journals/apin/BekkumBHMT21,
    author = {Michael van Bekkum and
    Maaike de Boer and
    Frank van Harmelen and
    Andr{\'{e}} Meyer{-}Vitali and
    Annette ten Teije},
    title = {Modular design patterns for hybrid learning and reasoning systems},
    journal = {Appl. Intell.},
    volume = {51},
    number = {9},
    pages = {6528--6546},
    year = {2021},
    url = {https://doi.org/10.1007/s10489-021-02394-3},
    doi = {10.1007/s10489-021-02394-3},
    timestamp = {Wed, 01 Sep 2021 12:45:13 +0200},
    biburl = {https://dblp.org/rec/journals/apin/BekkumBHMT21.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org},
    url = https://link.springer.com/article/10.1007/s10489-021-02394-3}

  • A. Kuzina, M. Welling, and J. M. Tomczak, “Diagnosing Vulnerability of Variational Auto-Encoders to Adversarial Attacks,” in ICLR 2021 Workshop on Robust and Reliable Machine Learning in the Real World, 2021.
    [BibTeX] [Abstract] [Download PDF]

    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 ($\beta$-VAE, NVAE) on the robustness of VAEs and suggest metrics to quantify it.

    @inproceedings{kuzina2021diagnosing,
    title={Diagnosing Vulnerability of Variational Auto-Encoders to Adversarial Attacks},
    author={Kuzina, Anna and Welling, Max and Tomczak, Jakub M},
    year={2021},
    booktitle = {ICLR 2021 Workshop on Robust and Reliable Machine Learning in the Real World},
    url={https://arxiv.org/pdf/2103.06701.pdf},
    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 ($\beta$-VAE, NVAE) on the robustness of VAEs and suggest metrics to quantify it.}
    }

  • H. Zheng and B. Verheij, “Rules, cases and arguments in artificial intelligence and law,” in Research Handbook on Big Data Law, R. Vogl, Ed., Edgar Elgar Publishing, 2021, pp. 373-387.
    [BibTeX] [Abstract] [Download PDF]

    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)

    @InCollection{Zheng:2021,
    author = {H. Zheng and B. Verheij},
    title = {Rules, cases and arguments in artificial intelligence and law},
    booktitle = {Research Handbook on Big Data Law},
    publisher = {Edgar Elgar Publishing},
    editor = {R Vogl},
    year = 2021,
    url = {https://www.ai.rug.nl/~verheij/publications/handbook2021.htm},
    pages = {373-387},
    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)}
    }

  • C. A. Kurtan and P. i, “Assisting humans in privacy management: an agent-based approach,” Autonomous Agents and Multi-Agent Systems, vol. 35, iss. 7, 2021. doi:https://doi.org/10.1007/s10458-020-09488-1
    [BibTeX] [Abstract] [Download PDF]

    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.

    @Article{kurtan-yolum-21,
    author = {A. Can Kurtan and P{\i}nar Yolum},
    title = {Assisting humans in privacy management: an agent-based approach},
    journal = {Autonomous Agents and Multi-Agent Systems},
    year = {2021},
    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.},
    url = {https://link.springer.com/article/10.1007/s10458-020-09488-1},
    doi = {https://doi.org/10.1007/s10458-020-09488-1}
    }

  • E. Liscio, M. van der Meer, L. C. Siebert, C. M. Jonker, N. Mouter, and P. K. Murukannaiah, “Axies: Identifying and Evaluating Context-Specific Values,” in Proc. of the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021), Online, 2021, p. 799–808.
    [BibTeX] [Abstract] [Download PDF]

    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

    @inproceedings{Liscio2021a,
    address = {Online},
    author = {Liscio, Enrico and van der Meer, Michiel and
    Siebert, Luciano C. and Jonker, Catholijn M. and
    Mouter, Niek and Murukannaiah, Pradeep K.},
    booktitle = {Proc. of the 20th International Conference on
    Autonomous Agents and Multiagent Systems (AAMAS
    2021)},
    keywords = {Context,Ethics,Natural Language
    Processing,Values,acm reference format,catholijn
    m,context,enrico liscio,ethics,jonker,luciano
    c,michiel van der meer,natural language
    processing,siebert,values},
    pages = {799--808},
    publisher = {IFAAMAS},
    title = {{Axies: Identifying and Evaluating Context-Specific
    Values}},
    year = {2021},
    url =
    "https://ii.tudelft.nl/~pradeep/doc/Liscio-2021-AAMAS-Axies.pdf",
    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"
    }

  • E. Liscio, M. van der Meer, C. M. Jonker, and P. K. Murukannaiah, “A Collaborative Platform for Identifying Context-Specific Values,” in Proc. of the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021), Online, 2021, p. 1773–1775.
    [BibTeX] [Abstract] [Download PDF]

    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.

    @inproceedings{Liscio2021,
    address = {Online},
    author = {Liscio, Enrico and van der Meer, Michiel and Jonker,
    Catholijn M. and Murukannaiah, Pradeep K.},
    booktitle = {Proc. of the 20th International Conference on
    Autonomous Agents and Multiagent Systems (AAMAS
    2021)},
    keywords = {Context,Ethics,Natural Language
    Processing,Values,acm reference format,and
    pradeep,catholijn m,context,enrico
    liscio,ethics,jonker,michiel van der meer,natural
    language processing,values},
    pages = {1773--1775},
    publisher = {IFAAMAS},
    title = {{A Collaborative Platform for Identifying
    Context-Specific Values}},
    year = {2021},
    url =
    "https://www.ifaamas.org/Proceedings/aamas2021/pdfs/p1773.pdf",
    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."
    }

  • K. Miras, J. Cuijpers, B. Gülhan, and A. Eiben, “The Impact of Early-death on Phenotypically Plastic Robots that Evolve in Changing Environments,” in ALIFE 2021: The 2021 Conference on Artificial Life, 2021.
    [BibTeX] [Abstract] [Download PDF]

    In this work, we evolve phenotypically plastic robots-robots that adapt their bodies and brains according to environmental conditions-in changing environments. In particular, we investigate how the possibility of death in early environmental conditions impacts evolvability and robot traits. Our results demonstrate that early-death improves the efficiency of the evolutionary process for the earlier environmental conditions. On the other hand, the possibility of early-death in the earlier environmental conditions results in a dramatic loss of performance in the latter environmental conditions.

    @inproceedings{miras2021impact,
    title = {The Impact of Early-death on Phenotypically Plastic
    Robots that Evolve in Changing Environments},
    author = {Miras, Karine and Cuijpers, Jim and G{\"u}lhan,
    Bahadir and Eiben, AE},
    booktitle = {ALIFE 2021: The 2021 Conference on Artificial Life},
    year = {2021},
    organization = {MIT Press},
    url =
    "https://direct.mit.edu/isal/proceedings-pdf/isal/33/25/1929813/isal_a_00371.pdf",
    abstract = "In this work, we evolve phenotypically plastic
    robots-robots that adapt their bodies and brains
    according to environmental conditions-in changing
    environments. In particular, we investigate how the
    possibility of death in early environmental
    conditions impacts evolvability and robot
    traits. Our results demonstrate that early-death
    improves the efficiency of the evolutionary process
    for the earlier environmental conditions. On the
    other hand, the possibility of early-death in the
    earlier environmental conditions results in a
    dramatic loss of performance in the latter
    environmental conditions."
    }

  • K. Miras, “Constrained by Design: Influence of Genetic Encodings on Evolved Traits of Robots,” Frontiers Robotics AI, vol. 8, p. 672379, 2021. doi:10.3389/frobt.2021.672379
    [BibTeX] [Abstract] [Download PDF]

    Genetic encodings and their particular properties are known to have a strong influence on the success of evolutionary systems. However, the literature has widely focused on studying the effects that encodings have on performance, i.e., fitness-oriented studies. Notably, this anchoring of the literature to performance is limiting, considering that performance provides bounded information about the behavior of a robot system. In this paper, we investigate how genetic encodings constrain the space of robot phenotypes and robot behavior. In summary, we demonstrate how two generative encodings of different nature lead to very different robots and discuss these differences. Our principal contributions are creating awareness about robot encoding biases, demonstrating how such biases affect evolved morphological, control, and behavioral traits, and finally scrutinizing the trade-offs among different biases.

    @article{DBLP:journals/firai/Miras21,
    author = {Karine Miras},
    title = {Constrained by Design: Influence of Genetic Encodings on Evolved Traits
    of Robots},
    journal = {Frontiers Robotics {AI}},
    volume = {8},
    pages = {672379},
    year = {2021},
    url = {https://doi.org/10.3389/frobt.2021.672379},
    doi = {10.3389/frobt.2021.672379},
    abstract = "Genetic encodings and their particular properties are known to have a strong influence on the success of evolutionary systems. However, the literature has widely focused on studying the effects that encodings have on performance, i.e., fitness-oriented studies. Notably, this anchoring of the literature to performance is limiting, considering that performance provides bounded information about the behavior of a robot system. In this paper, we investigate how genetic encodings constrain the space of robot phenotypes and robot behavior. In summary, we demonstrate how two generative encodings of different nature lead to very different robots and discuss these differences. Our principal contributions are creating awareness about robot encoding biases, demonstrating how such biases affect evolved morphological, control, and behavioral traits, and finally scrutinizing the trade-offs among different biases."
    }

  • P. Manggala, H. H. Hoos, and E. Nalisnick, “Bayesian Regression from Multiple Sources of Weak Supervision,” in ICML 2021 Machine Learning for Data: Automated Creation, Privacy, Bias, 2021.
    [BibTeX] [Abstract] [Download PDF]

    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.

    @inproceedings{manggala2021bayesianregression,
    title = {Bayesian Regression from Multiple Sources of Weak
    Supervision},
    author = {Manggala, Putra and Hoos, Holger H. and Nalisnick,
    Eric},
    year = {2021},
    booktitle = {ICML 2021 Machine Learning for Data: Automated
    Creation, Privacy, Bias},
    url =
    {https://pmangg.github.io/papers/brfmsows_mhn_ml4data_icml.pdf},
    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.}
    }

  • C. Steging, S. Renooij, and B. Verheij, “Discovering the rationale of decisions: towards a method for aligning learning and reasoning,” in ICAIL ’21: Eighteenth International Conference for Artificial Intelligence and Law, São Paulo Brazil, June 21 – 25, 2021, 2021, p. 235–239. doi:10.1145/3462757.3466059
    [BibTeX] [Abstract] [Download PDF]

    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.

    @inproceedings{StegingICAIL21,
    author = {Cor Steging and
    Silja Renooij and
    Bart Verheij},
    editor = {Juliano Maranh{\~{a}}o and
    Adam Zachary Wyner},
    title = {Discovering the rationale of decisions: towards a method for aligning
    learning and reasoning},
    booktitle = {{ICAIL} '21: Eighteenth International Conference for Artificial Intelligence
    and Law, S{\~{a}}o Paulo Brazil, June 21 - 25, 2021},
    pages = {235--239},
    publisher = {{ACM}},
    year = {2021},
    url = {https://doi.org/10.1145/3462757.3466059},
    doi = {10.1145/3462757.3466059},
    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."
    }

  • C. Steging, S. Renooij, and B. Verheij, “Discovering the Rationale of Decisions: Experiments on Aligning Learning and Reasoning,” in 4th EXplainable AI in Law Workshop (XAILA 2021), 2021, p. 235–239.
    [BibTeX] [Abstract] [Download PDF]

    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.

    @inproceedings{StegingXAILA21,
    author = {Cor Steging and
    Silja Renooij and
    Bart Verheij},
    title = {Discovering the Rationale of Decisions: Experiments on Aligning Learning
    and Reasoning},
    maintitle = {{ICAIL} '21: Eighteenth International Conference for Artificial Intelligence
    and Law, S{\~{a}}o Paulo Brazil, June 21 - 25, 2021},
    booktitle = {4th EXplainable AI in Law Workshop (XAILA 2021) },
    pages = {235--239},
    publisher = {{ACM}},
    year = {2021},
    url = {https://arxiv.org/abs/2105.06758},
    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."
    }

  • U. Khurana, E. Nalisnick, and A. Fokkens, “How Emotionally Stable is ALBERT? Testing Robustness with Stochastic Weight Averaging on a Sentiment Analysis Task,” in Proceedings of the 2nd Workshop on Evaluation and Comparison of NLP Systems, Punta Cana, Dominican Republic, 2021, p. 16–31.
    [BibTeX] [Abstract] [Download PDF]

    Despite their success, modern language models are fragile. Even small changes in their training pipeline can lead to unexpected results. We study this phenomenon by examining the robustness of ALBERT (Lan et al., 2020) in combination with Stochastic Weight Averaging (SWA){–-}a cheap way of ensembling{–-}on a sentiment analysis task (SST-2). In particular, we analyze SWA{‘}s stability via CheckList criteria (Ribeiro et al., 2020), examining the agreement on errors made by models differing only in their random seed. We hypothesize that SWA is more stable because it ensembles model snapshots taken along the gradient descent trajectory. We quantify stability by comparing the models{‘} mistakes with Fleiss{‘} Kappa (Fleiss, 1971) and overlap ratio scores. We find that SWA reduces error rates in general; yet the models still suffer from their own distinct biases (according to CheckList).

    @inproceedings{khurana-etal-2021-emotionally,
    title = "How Emotionally Stable is {ALBERT}? Testing
    Robustness with Stochastic Weight Averaging on a
    Sentiment Analysis Task",
    author = "Khurana, Urja and Nalisnick, Eric and Fokkens,
    Antske",
    booktitle = "Proceedings of the 2nd Workshop on Evaluation and
    Comparison of NLP Systems",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.eval4nlp-1.3",
    pages = "16--31",
    abstract = "Despite their success, modern language models are
    fragile. Even small changes in their training
    pipeline can lead to unexpected results. We study
    this phenomenon by examining the robustness of
    ALBERT (Lan et al., 2020) in combination with
    Stochastic Weight Averaging (SWA){---}a cheap way of
    ensembling{---}on a sentiment analysis task
    (SST-2). In particular, we analyze SWA{'}s stability
    via CheckList criteria (Ribeiro et al., 2020),
    examining the agreement on errors made by models
    differing only in their random seed. We hypothesize
    that SWA is more stable because it ensembles model
    snapshots taken along the gradient descent
    trajectory. We quantify stability by comparing the
    models{'} mistakes with Fleiss{'} Kappa (Fleiss,
    1971) and overlap ratio scores. We find that SWA
    reduces error rates in general; yet the models still
    suffer from their own distinct biases (according to
    CheckList).",
    }

  • D. W. Romero, A. Kuzina, E. J. Bekkers, J. M. Tomczak, and M. Hoogendoorn, “CKConv: Continuous Kernel Convolution For Sequential Data,” CoRR, vol. abs/2102.02611, 2021.
    [BibTeX] [Abstract] [Download PDF]

    Conventional neural architectures for sequential data present important limitations. Recurrent networks suffer from exploding and vanishing gradients, small effective memory horizons, and must be trained sequentially. Convolutional networks are unable to handle sequences of unknown size and their memory horizon must be defined a priori. In this work, we show that all these problems can be solved by formulating convolutional kernels in CNNs as continuous functions. The resulting Continuous Kernel Convolution (CKConv) allows us to model arbitrarily long sequences in a parallel manner, within a single operation, and without relying on any form of recurrence. We show that Continuous Kernel Convolutional Networks (CKCNNs) obtain state-of-the-art results in multiple datasets, e.g., permuted MNIST, and, thanks to their continuous nature, are able to handle non-uniformly sampled datasets and irregularly-sampled data natively. CKCNNs match or perform better than neural ODEs designed for these purposes in a faster and simpler manner.

    @article{DBLP:journals/corr/abs-2102-02611,
    author = {David W. Romero and
    Anna Kuzina and
    Erik J. Bekkers and
    Jakub M. Tomczak and
    Mark Hoogendoorn},
    title = {CKConv: Continuous Kernel Convolution For Sequential Data},
    journal = {CoRR},
    volume = {abs/2102.02611},
    year = {2021},
    url = {https://arxiv.org/abs/2102.02611},
    abstract = {Conventional neural architectures for sequential data present important limitations. Recurrent networks suffer from exploding and vanishing gradients, small effective memory horizons, and must be trained sequentially. Convolutional networks are unable to handle sequences of unknown size and their memory horizon must be defined a priori. In this work, we show that all these problems can be solved by formulating convolutional kernels in CNNs as continuous functions. The resulting Continuous Kernel Convolution (CKConv) allows us to model arbitrarily long sequences in a parallel manner, within a single operation, and without relying on any form of recurrence. We show that Continuous Kernel Convolutional Networks (CKCNNs) obtain state-of-the-art results in multiple datasets, e.g., permuted MNIST, and, thanks to their continuous nature, are able to handle non-uniformly sampled datasets and irregularly-sampled data natively. CKCNNs match or perform better than neural ODEs designed for these purposes in a faster and simpler manner.}
    }

  • M. B. Vessies, S. P. Vadgama, R. R. van de Leur, P. A. F. M. Doevendans, R. J. Hassink, E. Bekkers, and R. van Es, “Interpretable ECG classification via a query-based latent space traversal (qLST),” CoRR, vol. abs/2111.07386, 2021.
    [BibTeX] [Download PDF]
    @article{DBLP:journals/corr/abs-2111-07386,
    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
    Ren{\'{e}} van Es},
    title = {Interpretable {ECG} classification via a query-based latent space
    traversal (qLST)},
    journal = {CoRR},
    volume = {abs/2111.07386},
    year = {2021},
    url = {https://arxiv.org/abs/2111.07386},
    abstact = {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.}
    }

  • B. Dudzik and J. Broekens, “A Valid Self-Report is Never Late, Nor is it Early: On Considering the Right Temporal Distance for Assessing Emotional Experience,” , 1, 2021.
    [BibTeX]
    @techreport{Dudzik2021,
    author = {Dudzik, Bernd and Broekens, Joost},
    booktitle = {Momentary Emotion Elicitation and Capture Workshop
    (MEEC'21), May 9, 2021, Yokohama, Japan},
    number = {1},
    publisher = {Association for Computing Machinery},
    title = {{A Valid Self-Report is Never Late, Nor is it Early:
    On Considering the Right Temporal Distance for
    Assessing Emotional Experience}},
    volume = {1},
    year = {2021}
    }

  • B. Dudzik, S. Columbus, T. M. Hrkalovic, D. Balliet, and H. Hung, “Recognizing Perceived Interdependence in Face-to-Face Negotiations through Multimodal Analysis of Nonverbal Behavior,” in Proceedings of the 2021 International Conference on Multimodal Interaction, New York, NY, USA: Association for Computing Machinery, 2021, p. 121–130. doi:10.1145/3462244.3479935
    [BibTeX] [Abstract] [Download PDF]

    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.

    @inbook{10.1145/3462244.3479935,
    author = {Dudzik, Bernd and Columbus, Simon and Hrkalovic,
    Tiffany Matej and Balliet, Daniel and Hung, Hayley},
    title = {Recognizing Perceived Interdependence in
    Face-to-Face Negotiations through Multimodal
    Analysis of Nonverbal Behavior},
    year = {2021},
    isbn = {9781450384810},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    booktitle = {Proceedings of the 2021 International Conference on
    Multimodal Interaction},
    pages = {121–130},
    numpages = {10},
    doi = {10.1145/3462244.3479935},
    url =
    {https://research.tudelft.nl/en/publications/recognizing-perceived-interdependence-in-face-to-face-negotiation},
    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.}
    }

  • C. Steging, S. Renooij, and B. Verheij, “Rationale Discovery and Explainable AI,” in Legal Knowledge and Information Systems – JURIX 2021: The Thirty-fourth Annual Conference, Vilnius, Lithuania, 8-10 December 2021, 2021, p. 225–234. doi:10.3233/FAIA210341
    [BibTeX] [Abstract] [Download PDF]

    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.

    @inproceedings{DBLP:conf/jurix/StegingRV21,
    author = {Cor Steging and
    Silja Renooij and
    Bart Verheij},
    editor = {Schweighofer Erich},
    title = {Rationale Discovery and Explainable {AI}},
    booktitle = {Legal Knowledge and Information Systems - {JURIX} 2021: The Thirty-fourth
    Annual Conference, Vilnius, Lithuania, 8-10 December 2021},
    series = {Frontiers in Artificial Intelligence and Applications},
    volume = {346},
    pages = {225--234},
    publisher = {{IOS} Press},
    year = {2021},
    url = {https://doi.org/10.3233/FAIA210341},
    doi = {10.3233/FAIA210341},
    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.}
    }

  • H. Loan, “Knowledge Representation Formalisms for Hybrid Intelligence,” in Doctoral consortium of the Knowledge Representation Conference, KR2021, online, 2021.
    [BibTeX] [Abstract] [Download PDF]

    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 to allow artificial intelligence systems adapt and work with complex, conflicting, dynamic and contextualized knowledge.

    @inproceedings{Loan2021,
    address = {online},
    author = {Loan, Ho},
    publisher = {KR Conference},
    title = {Knowledge Representation Formalisms for Hybrid
    Intelligence},
    year = {2021},
    booktitle = {Doctoral consortium of the Knowledge Representation Conference, {KR}2021},
    url = "https://sites.google.com/view/kr2021dc",
    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 to allow
    artificial intelligence systems adapt and work with
    complex, conflicting, dynamic and contextualized
    knowledge."
    }

  • B. H. Kargar, K. Miras, and A. Eiben, “The effect of selecting for different behavioral traits on the evolved gaits of modular robots,” in ALIFE 2021: The 2021 Conference on Artificial Life, 2021.
    [BibTeX] [Abstract] [Download PDF]

    Moving around in the environment is a fundamental skill for mobile robots. This makes the evolution of an appropriate gait, a pivotal problem in evolutionary robotics. Whereas the majority of the related studies concern robots with predefined modular or legged morphologies and locomotion speed as the optimization objective, here we investigate robots with evolvable morphologies and behavioral traits included in the fitness function. To analyze the effects we consider morphological as well as behavioral features of the evolved robots. To this end, we introduce novel behavioral measures that describe how the robot locomotes and look into the trade-off between them. Our main goal is to gain insights into differences in possible gaits of modular robots and to provide tools to steer evolution towards objectives beyond ‘simple’ speed.

    @inproceedings{kargar2021effect,
    title = {The effect of selecting for different behavioral
    traits on the evolved gaits of modular robots},
    author = {Kargar, Babak H and Miras, Karine and Eiben, AE},
    booktitle = {ALIFE 2021: The 2021 Conference on Artificial Life},
    year = {2021},
    organization = {MIT Press},
    url =
    {https://direct.mit.edu/isal/proceedings/isal/33/26/102968},
    abstract = {Moving around in the environment is a fundamental
    skill for mobile robots. This makes the evolution of
    an appropriate gait, a pivotal problem in
    evolutionary robotics. Whereas the majority of the
    related studies concern robots with predefined
    modular or legged morphologies and locomotion speed
    as the optimization objective, here we investigate
    robots with evolvable morphologies and behavioral
    traits included in the fitness function. To analyze
    the effects we consider morphological as well as
    behavioral features of the evolved robots. To this
    end, we introduce novel behavioral measures that
    describe how the robot locomotes and look into the
    trade-off between them. Our main goal is to gain
    insights into differences in possible gaits of
    modular robots and to provide tools to steer
    evolution towards objectives beyond ‘simple’ speed.}
    }

  • G. Boomgaard, S. Santamaría, I. Tiddi, R. J. Sips, and Z. Szlávik, “Learning profile-based recommendations for medical search auto-complete,” in AAAI-MAKE 2021 Combining Machine Learning and Knowledge Engineering, 2021, p. 1–13.
    [BibTeX] [Abstract] [Download PDF]

    Query popularity is a main feature in web-search auto-completion. Several personalization features have been proposed to support specific users’ searches, but often do not meet the privacy requirements of a medical environment (e.g. clinical trial search). Furthermore, in such specialized domains, the differences in user expertise and the domain-specific language users employ are far more widespread than in web-search. We propose a query auto-completion method based on different relevancy and diversity features, which can appropriately meet different user needs. Our method incorporates indirect popularity measures, along with graph topology and semantic features. An evolutionary algorithm optimizes relevance, diversity, and coverage to return a top-k list of query completions to the user. We evaluated our approach quantitatively and qualitatively using query log data from a clinical trial search engine, comparing the effects of different relevancy and diversity settings using domain experts. We found that syntax-based diversity has more impact on effectiveness and efficiency, graph-based diversity shows a more compact list of results, and relevancy the most effect on indicated preferences.

    @inproceedings{boomgaard-etal-2021-learning,
    title = "Learning profile-based recommendations for medical
    search auto-complete",
    author = "Guusje Boomgaard and Selene
    Báez Santamaría and Ilaria Tiddi and Robert Jan Sips
    and Zoltán Szlávik",
    keywords = "Knowledge graphs, Medical information retrieval,
    Professional search, Query auto-Completion",
    year = "2021",
    month = apr,
    day = "10",
    language = "English",
    series = "CEUR Workshop Proceedings",
    publisher = "CEUR-WS",
    pages = "1--13",
    editor = "Andreas Martin and Knut Hinkelmann and Hans-Georg
    Fill and Aurona Gerber and Doug Lenat and Reinhard
    Stolle and {van Harmelen}, Frank",
    booktitle = "AAAI-MAKE 2021 Combining Machine Learning and
    Knowledge Engineering",
    Url = "http://ceur-ws.org/Vol-2846/paper34.pdf",
    abstract = "Query popularity is a main feature in web-search
    auto-completion. Several personalization features
    have been proposed to support specific users'
    searches, but often do not meet the privacy
    requirements of a medical environment (e.g. clinical
    trial search). Furthermore, in such specialized
    domains, the differences in user expertise and the
    domain-specific language users employ are far more
    widespread than in web-search. We propose a query
    auto-completion method based on different relevancy
    and diversity features, which can appropriately meet
    different user needs. Our method incorporates
    indirect popularity measures, along with graph
    topology and semantic features. An evolutionary
    algorithm optimizes relevance, diversity, and
    coverage to return a top-k list of query completions
    to the user. We evaluated our approach
    quantitatively and qualitatively using query log
    data from a clinical trial search engine, comparing
    the effects of different relevancy and diversity
    settings using domain experts. We found that
    syntax-based diversity has more impact on
    effectiveness and efficiency, graph-based diversity
    shows a more compact list of results, and relevancy
    the most effect on indicated preferences.",
    }

  • S. Baez Santamaria, T. Baier, T. Kim, L. Krause, J. Kruijt, and P. Vossen, “EMISSOR: A platform for capturing multimodal interactions as Episodic Memories and Interpretations with Situated Scenario-based Ontological References,” in Proceedings of the 1st Workshop on Multimodal Semantic Representations (MMSR), Groningen, Netherlands (Online), 2021, p. 56–77.
    [BibTeX] [Abstract] [Download PDF]

    We present EMISSOR: a platform to capture multimodal interactions as recordings of episodic experiences with explicit referential interpretations that also yield an episodic Knowledge Graph (eKG). The platform stores streams of multiple modalities as parallel signals. Each signal is segmented and annotated independently with interpretation. Annotations are eventually mapped to explicit identities and relations in the eKG. As we ground signal segments from different modalities to the same instance representations, we also ground different modalities across each other. Unique to our eKG is that it accepts different interpretations across modalities, sources and experiences and supports reasoning over conflicting information and uncertainties that may result from multimodal experiences. EMISSOR can record and annotate experiments in virtual and real-world, combine data, evaluate system behavior and their performance for preset goals but also model the accumulation of knowledge and interpretations in the Knowledge Graph as a result of these episodic experiences.

    @inproceedings{baez-santamaria-etal-2021-emissor,
    title = "{EMISSOR}: A platform for capturing multimodal
    interactions as Episodic Memories and
    Interpretations with Situated Scenario-based
    Ontological References",
    author = "Baez Santamaria, Selene and Baier, Thomas and Kim,
    Taewoon and Krause, Lea and Kruijt, Jaap and Vossen,
    Piek",
    booktitle = "Proceedings of the 1st Workshop on Multimodal
    Semantic Representations (MMSR)",
    month = jun,
    year = "2021",
    address = "Groningen, Netherlands (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.mmsr-1.6",
    pages = "56--77",
    abstract = "We present EMISSOR: a platform to capture multimodal
    interactions as recordings of episodic experiences
    with explicit referential interpretations that also
    yield an episodic Knowledge Graph (eKG). The
    platform stores streams of multiple modalities as
    parallel signals. Each signal is segmented and
    annotated independently with
    interpretation. Annotations are eventually mapped to
    explicit identities and relations in the eKG. As we
    ground signal segments from different modalities to
    the same instance representations, we also ground
    different modalities across each other. Unique to
    our eKG is that it accepts different interpretations
    across modalities, sources and experiences and
    supports reasoning over conflicting information and
    uncertainties that may result from multimodal
    experiences. EMISSOR can record and annotate
    experiments in virtual and real-world, combine data,
    evaluate system behavior and their performance for
    preset goals but also model the accumulation of
    knowledge and interpretations in the Knowledge Graph
    as a result of these episodic experiences.",
    }

  • B. M. Renting, H. H. Hoos, and C. M. Jonker, Automated Configuration and Usage of Strategy Portfolios for Bargaining, 2021.
    [BibTeX] [Abstract] [Download PDF]

    Bargaining can be used to resolve mixed-motive games in multi-agent systems. Although there is an abundance of negotiation strategies implemented in automated negotiating agents, most agents are based on single fixed strategies, while it is widely acknowledged that there is no single best-performing strategy for all negotiation settings. In this paper, we focus on bargaining settings where opponents are repeatedly encountered, but the bargaining problems change. We introduce a novel method that automatically creates and deploys a portfolio of complementary negotiation strategies using a training set and optimise pay-off in never-before-seen bargaining settings through per-setting strategy selection. Our method relies on the following contributions. We introduce a feature representation that captures characteristics for both the opponent and the bargaining problem. We model the behaviour of an opponent during a negotiation based on its actions, which is indicative of its negotiation strategy, in order to be more effective in future encounters. Our combination of feature-based methods generalises to new negotiation settings, as in practice, over time, it selects effective counter strategies in future encounters. Our approach is tested in an Automated Negotiating Agents Competition (ANAC)-like tournament, and we show that we are capable of winning such a tournament with a 5.6% increase in pay-off compared to the runner-up agent.

    @unpublished{Renting2021AutomatedBargaining,
    title = {Automated Configuration and Usage of Strategy
    Portfolios for Bargaining},
    author = {Renting, Bram M. and Hoos, Holger H. and Jonker,
    Catholijn M.},
    year = {2021},
    month = dec,
    booktitle = {NeurIPS 2021 Workshop on Cooperative AI},
    abstract = {Bargaining can be used to resolve mixed-motive games
    in multi-agent systems. Although there is an
    abundance of negotiation strategies implemented in
    automated negotiating agents, most agents are based
    on single fixed strategies, while it is widely
    acknowledged that there is no single best-performing
    strategy for all negotiation settings. In this
    paper, we focus on bargaining settings where
    opponents are repeatedly encountered, but the
    bargaining problems change. We introduce a novel
    method that automatically creates and deploys a
    portfolio of complementary negotiation strategies
    using a training set and optimise pay-off in
    never-before-seen bargaining settings through
    per-setting strategy selection. Our method relies on
    the following contributions. We introduce a feature
    representation that captures characteristics for
    both the opponent and the bargaining problem. We
    model the behaviour of an opponent during a
    negotiation based on its actions, which is
    indicative of its negotiation strategy, in order to
    be more effective in future encounters. Our
    combination of feature-based methods generalises to
    new negotiation settings, as in practice, over time,
    it selects effective counter strategies in future
    encounters. Our approach is tested in an Automated
    Negotiating Agents Competition (ANAC)-like
    tournament, and we show that we are capable of
    winning such a tournament with a 5.6% increase in
    pay-off compared to the runner-up agent.},
    url = {https://www.cooperativeai.com/neurips-2021/workshop-papers},
    }

2020

  • B. Verheij, “Artificial intelligence as law,” Artif. Intell. Law, vol. 28, iss. 2, p. 181–206, 2020. doi:10.1007/s10506-020-09266-0
    [BibTeX] [Download PDF]
    @article{Verheij20,
    author = {Bart Verheij},
    title = {Artificial intelligence as law},
    journal = {Artif. Intell. Law},
    volume = {28},
    number = {2},
    pages = {181--206},
    year = {2020},
    url = {https://doi.org/10.1007/s10506-020-09266-0},
    doi = {10.1007/s10506-020-09266-0},
    timestamp = {Fri, 05 Jun 2020 17:08:42 +0200},
    biburl = {https://dblp.org/rec/journals/ail/Verheij20.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
    }

  • N. Kökciyan and P. Yolum, “TURP: Managing Trust for Regulating Privacy in Internet of Things,” IEEE Internet Computing, vol. 24, iss. 6, pp. 9-16, 2020. doi:https://doi.org/10.1109/MIC.2020.3020006
    [BibTeX] [Abstract] [Download PDF]

    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.

    @ARTICLE{turp-ic-2020,
    author={K\"okciyan, Nadin and Yolum, P{\i}nar},
    journal={IEEE Internet Computing},
    title={TURP: Managing Trust for Regulating Privacy in Internet of Things},
    year={2020},
    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.},
    url = {https://webspace.science.uu.nl/~yolum001/papers/InternetComputing-20-TURP.pdf},
    doi = {https://doi.org/10.1109/MIC.2020.3020006}
    }

  • O. Ulusoy and P. Yolum, “Agents for Preserving Privacy: Learning and Decision Making Collaboratively,” in Multi-Agent Systems and Agreement Technologies, 2020, p. 116–131. doi:https://doi.org/10.1007/978-3-030-66412-1_8
    [BibTeX] [Abstract] [Download PDF]

    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.

    @InProceedings{ulusoy-yolum-20,
    title = "Agents for Preserving Privacy: Learning and Decision
    Making Collaboratively",
    author = "Ulusoy, Onuralp and Yolum, P{\i}nar",
    editor = "Bassiliades, Nick and Chalkiadakis, Georgios and de
    Jonge, Dave",
    booktitle = "Multi-Agent Systems and Agreement Technologies",
    year = "2020",
    publisher = "Springer International Publishing",
    pages = "116--131",
    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.",
    isbn = "978-3-030-66412-1",
    doi = {https://doi.org/10.1007/978-3-030-66412-1_8},
    url =
    {https://webspace.science.uu.nl/~yolum001/papers/ulusoy-yolum-20.pdf}
    }

  • L. Krause and P. Vossen, “When to explain: Identifying explanation triggers in human-agent interaction,” in 2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence, Dublin, Ireland, 2020, p. 55–60.
    [BibTeX] [Abstract] [Download PDF]

    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.

    @inproceedings{krause-vossen-2020-explain,
    title = "When to explain: Identifying explanation triggers in human-agent interaction",
    author = "Krause, Lea and
    Vossen, Piek",
    booktitle = "2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence",
    month = nov,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nl4xai-1.12",
    pages = "55--60",
    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.",
    }

  • P. K. Murukannaiah, N. Ajmeri, C. M. Jonker, and M. P. Singh, “New Foundations of Ethical Multiagent Systems,” in Proceedings of the 19th Conference on Autonomous Agents and MultiAgent Systems, Auckland, 2020, p. 1706–1710.
    [BibTeX] [Abstract] [Download PDF]

    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.

    @inproceedings{Murukannaiah-2020-AAMASBlueSky-EthicalMAS,
    author = {Pradeep K. Murukannaiah and Nirav Ajmeri and
    Catholijn M. Jonker and Munindar P. Singh},
    title = {New Foundations of Ethical Multiagent Systems},
    booktitle = {Proceedings of the 19th Conference on Autonomous
    Agents and MultiAgent Systems},
    series = {AAMAS '20},
    year = {2020},
    address = {Auckland},
    pages = {1706--1710},
    numpages = {5},
    keywords = {Agents, ethics},
    url =
    {https://ii.tudelft.nl/~pradeep/doc/Murukannaiah-2020-AAMASBlueSky-EthicalMAS.pdf},
    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.}
    }

  • Z. Akata, D. Balliet, M. de Rijke, F. Dignum, V. Dignum, G. Eiben, A. Fokkens, D. Grossi, K. Hindriks, H. Hoos, H. Hung, C. Jonker, C. Monz, M. Neerincx, F. Oliehoek, H. Prakken, S. Schlobach, L. van der Gaag, F. van Harmelen, H. van Hoof, B. van Riemsdijk, A. van Wynsberghe, R. Verbrugge, B. Verheij, P. Vossen, and M. Welling, “A Research Agenda for Hybrid Intelligence: Augmenting Human Intellect With Collaborative, Adaptive, Responsible, and Explainable Artificial Intelligence,” IEEE Computer, vol. 53, iss. 08, pp. 18-28, 2020. doi:10.1109/MC.2020.2996587
    [BibTeX] [Abstract] [Download PDF]

    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.

    @ARTICLE {9153877,
    author = {Z. Akata and D. Balliet and M. de Rijke and
    F. Dignum and V. Dignum and G. Eiben and A. Fokkens
    and D. Grossi and K. Hindriks and H. Hoos and
    H. Hung and C. Jonker and C. Monz and M. Neerincx
    and F. Oliehoek and H. Prakken and S. Schlobach and
    L. van der Gaag and F. van Harmelen and H. van Hoof
    and B. van Riemsdijk and A. van Wynsberghe and
    R. Verbrugge and B. Verheij and P. Vossen and
    M. Welling},
    journal = {IEEE Computer},
    title = {A Research Agenda for Hybrid Intelligence:
    Augmenting Human Intellect With Collaborative,
    Adaptive, Responsible, and Explainable Artificial
    Intelligence},
    year = {2020},
    volume = {53},
    number = {08},
    issn = {1558-0814},
    pages = {18-28},
    doi = {10.1109/MC.2020.2996587},
    publisher = {IEEE Computer Society},
    address = {Los Alamitos, CA, USA},
    month = {aug},
    url =
    "http://www.cs.vu.nl/~frankh/postscript/IEEEComputer2020.pdf",
    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."
    }

  • B. M. Renting, H. H. Hoos, and C. M. Jonker, “Automated Configuration of Negotiation Strategies,” in Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems, 2020, p. 1116–1124.
    [BibTeX] [Abstract] [Download PDF]

    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% 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%.

    @inproceedings{Renting2020AutomatedStrategies,
    title = {Automated Configuration of Negotiation Strategies},
    booktitle = {Proceedings of the 19th International Conference on
    Autonomous Agents and MultiAgent Systems},
    author = {Renting, Bram M. and Hoos, Holger H. and Jonker,
    Catholijn M.},
    year = {2020},
    month = may,
    series = {AAMAS '20},
    pages = {1116--1124},
    publisher = {International Foundation for Autonomous Agents and
    Multiagent Systems},
    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% 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%.},
    isbn = {978-1-4503-7518-4},
    keywords = {automated algorithm configuration,automated
    negotiation,negotiation strategy},
    url =
    {https://ifaamas.org/Proceedings/aamas2020/pdfs/p1116.pdf}
    }