@inproceedings{sharpiecaihu25,

title = {SHARPIE: A Modular Framework for Reinforcement Learning and Human-AI Interaction Experiments},

author = {Hüseyin Ayd{ı}n and Kevin Godin-Dubois and Libio Goncalvez Braz and Floris den Hengst and Kim Baraka and Mustafa Mert Çelikok and Andreas Sauter and Shihan Wang and Frans A. Oliehoek},

url = {https://arxiv.org/abs/2501.19245},

doi = {10.48550/arXiv.2501.19245},

year  = {2025},

date = {2025-02-01},

booktitle = {AAAI Bridge Program Workshop on Collaborative AI and Modeling of Humans (CAIHU)},

address = {Philadelphia, Pennsylvania, USA},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{apeiron2025model,

title = {Model and Mechanisms of Consent for Responsible Autonomy},

author = {Apeiron, Anastasia S. and Dell’Anna, Davide and Murukannaiah, Pradeep K. and Yolum, P{ı}nar},

year  = {2025},

date = {2025-01-01},

booktitle = {24th International Conference on Autonomous Agents and Multiagent Systems},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{onnes2024,

title = {Extending idioms for Bayesian network construction with qualitative 

constraints},

author = {Onnes, Annet and Dastani, Mehdi and Dobbe, Roel and Renooij, Silja}, book title = {Proceedings of the Twentieth International Conference on Information 

Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU)},

url = {https://link.springer.com/chapter/10.1007/978-3-031-74003-9_33},

year  = {2025},

date = {2025-01-01},

volume = {3},

pages = {415–426},

publisher = {Springer},

organization = {Springer},

series = {Lecture Notes in Networks and Systems},

abstract = {Bayesian networks (BNs) are compact representations of probability 

distributions that allow for supporting reasoning and decision making under uncertainty. 

Their interpretable structure and probability parameters allow for integrating human 

knowledge in their construction and explanation. For BN construction, reusable building 

blocks, or idioms, exist that describe the dependencies and reasoning patterns among small 

sets of variables. In this paper we formalise the concept of an idiom, explicitly including 

qualitative constraints that capture the reasoning patterns among variables as stated in the 

informal descriptions that accompany the idioms in literature. Our proposed formalisation 

ensures that idioms can be applied more consistently and reliably, improving the BN’s 

accountability.},

note = {accepted for publication},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{hoepner2025data,

title = {Data Augmentation for Instruction Following Policies via Trajectory Segmentation},

author = {Hoepner, Niklas and Tiddi, Ilaria and van Hoof, Herke},

year  = {2025},

date = {2025-01-01},

booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},

volume = {39},

number = {16},

pages = {17214–17222},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/3708359.3712158,

title = {Technologies Supporting Self-Reflection on Social Interactions: A Systematic Review},

author = {Hao, Chenxu and Matej Hrkalovic, Tiffany and Balliet, Daniel and Hung, Hayley and Dudzik, Bernd},

url = {https://doi.org/10.1145/3708359.3712158},

doi = {10.1145/3708359.3712158},

isbn = {9798400713064},

year  = {2025},

date = {2025-01-01},

booktitle = {Proceedings of the 30th International Conference on Intelligent User Interfaces},

pages = {1354–1365},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

series = {IUI '25},

abstract = {As intelligent technology and applications have become an integral part of nearly all aspects of people’s daily lives, many intelligent systems have been designed to help people navigate the complex space of social interactions. One prominent strategy for such intelligent support is providing meaningful Ad Hoc Interventions (ADI), e.g., through timely notifications. An alternative is Technology-Supported Reflection (TSR), e.g., by offering information about activities in one’s past for personal insights. In contrast to straight-up interventions, the aim of the latter strategy is not to directly augment human skills but instead support learning and personal growth over time. However, while TSR has seen widespread interest in applications in some areas, such as physical fitness and mental health, its use for improving human social interactions has not yet been systematically explored. Concretely, it is currently unclear 1) what forms of self-reflection systems intend to support, 2) how their different technological components (e.g., data collection, information integration) are involved in providing support, and 3) what common limitations and design challenges they face. In this article, we present the results of a systematic literature review focusing on these questions to provide a structured foundation for targeted research. Concretely, we identified and analysed a collection of 23 relevant papers, each describing a system deploying TSR to support humans with elements of social interactions.We constructed a framework with a set of features to comprehensively describe and analyze the systems that support self-reflection, including their application domains, how they fit into the existing design framework, how they facilitate learning through reflection, how adaptive they are to individual users, and how they were evaluated. Finally, we propose a direction for designing systems that support individual’s social interactions through self-reflection in an adaptive manner.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ju2024relevance,

title = {Relevance-aware Diverse Query Generation for Out-of-domain Text Ranking},

author = {Ju, Jia-Huei and Yang, Chao-Han and Fu, Szu-Wei and Tsai, Ming-Feng and Wang, Chuan-Ju},

editor = {Zhao, Chen and Mosbach, Marius and Atanasova, Pepa and Goldfarb-Tarrent, Seraphina and Hase, Peter and Hosseini, Arian and Elbayad, Maha and Pezzelle, Sandro and Mozes, Maximilian},

url = {https://aclanthology.org/2024.repl4nlp-1.3},

year  = {2024},

date = {2024-08-01},

booktitle = {Proceedings of the 9th Workshop on Representation Learning for NLP (RepL4NLP-2024)},

pages = {26–36},

publisher = {Association for Computational Linguistics},

address = {Bangkok, Thailand},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{GodinDubois2024a,

title = {Interactive Embodied Evolution for Socially Adept Artificial General Creatures},

author = {{Godin-Dubois}, Kevin and Weissl, Olivier and Miras, Karine and Kononova, Anna V.},

doi = {10.48550/arXiv.2407.21357},

year  = {2024},

date = {2024-07-01},

booktitle = {Evolution of Things Workshop at the ALife 2024 Conference},

publisher = {arXiv},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Loftin24AAMAS,

title = {Uncoupled Learning of Differential Stackelberg Equilibria with Commitments},

author = {Loftin, Robert and {Ç}elikok, Mustafa Mert and van Hoof, Herke and Kaski, Samuel and Oliehoek, Frans A},

year  = {2024},

date = {2024-05-01},

booktitle = {Proceedings of the Twenty-Third International Conference on Autonomous Agents and Multiagent Systems (AAMAS)},

address = {Auckland, New Zealand},

organization = {IFAAMAS},

abstract = {In multi-agent problems requiring a high degree of cooperation, 

 success often depends on the ability of the agents to adapt to each 

 other’s behavior. A natural solution concept in such settings is the 

 Stackelberg equilibrium, in which the “leader” agent selects the 

 strategy that maximizes its own payoff given that the “follower” 

 agent will choose their best response to this strategy. Recent work 

 has extended this solution concept to two-player differentiable 

 games, such as those arising from multi-agent deep reinforcement 

 learning, in the form of the differential Stackelberg equilibrium. 

 While this previous work has presented learning dynamics which 

 converge to such equilibria, these dynamics are “coupled” in the 

 sense that the learning updates for the leader’s strategy require 

 some information about the follower’s payoff function. As such, 

 these methods cannot be applied to truly decentralised multi-agent 

 settings, particularly ad hoc cooperation, where each agent only 

 has access to its own payoff function. In this work we present 

 “uncoupled” learning dynamics based on zeroth-order gradient es- 

 timators, in which each agent’s strategy update depends only on 

 their observations of the other’s behavior. We analyze the conver- 

 gence of these dynamics in general-sum games, and prove that 

 they converge to differential Stackelberg equilibria under the same 

 conditions as previous coupled methods. Furthermore, we present 

 an online mechanism by which symmetric learners can negotiate 

 leader-follower roles. We conclude with a discussion of the impli- 

 cations of our work for multi-agent reinforcement learning and ad 

 hoc collaboration more generally.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1007/978-981-99-8718-4_4,

title = {Social Robots in the Wild and the Novelty Effect},

author = {Reimann, Merle 

 and van de Graaf, Jesper 

 and van Gulik, Nina 

 and van de Sanden, Stephanie 

 and Verhagen, Tibert 

 and Hindriks, Koen},

editor = {Ali, Abdulaziz Al 

 and Cabibihan, John-John 

 and Meskin, Nader 

 and Rossi, Silvia 

 and Jiang, Wanyue 

 and He, Hongsheng 

 and Ge, Shuzhi Sam},

url = {https://rdcu.be/dtXE5},

isbn = {978-981-99-8718-4},

year  = {2024},

date = {2024-01-01},

booktitle = {Social Robotics},

pages = {38–48},

publisher = {Springer Nature Singapore},

address = {Singapore},

abstract = {We designed a wine recommendation robot and deployed it in a small supermarket. In a study aimed to evaluate our design we found that people with no intent to buy wine were interacting with the robot rather than the intended audience of wine-buying customers. Behavioural data, moreover, suggests a very different evaluation of the robot than the surveys that were completed. We also found that groups were interacting more with the robot than individuals, a finding that has been reported more often in the literature. All of these findings taken together suggest that a novelty effect may have been at play. It also suggests that field studies should take this effect more seriously. The main contribution of our work is in identifying and proposing a set of indicators and thresholds that can be used to identify that a novelty effect is present. We argue that it is important to focus more on measuring attitudes towards robots that may explain behaviour due to novelty effects. Our findings also suggest research should focus more on verifying whether real user needs are met."},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/3613905.3650825,

title = {Practising Appropriate Trust in Human-Centred AI Design},

author = {Degachi, Chadha and Mehrotra, Siddharth and Yurrita, Mireia and Niforatos, Evangelos and Tielman, Myrthe Lotte},

url = {https://doi.org/10.1145/3613905.3650825},

doi = {10.1145/3613905.3650825},

isbn = {9798400703317},

year  = {2024},

date = {2024-01-01},

booktitle = {Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

series = {CHI EA '24},

abstract = {Appropriate trust, trust which aligns with system trustworthiness, in Artificial Intelligence (AI) systems has become an important area of research. However, there remains debate in the community about how to design for appropriate trust. This debate is a result of the complex nature of trust in AI, which can be difficult to understand and evaluate, as well as the lack of holistic approaches to trust. In this paper, we aim to clarify some of this debate by operationalising appropriate trust within the context of the Human-Centred AI Design (HCD) process. To do so, we organised three workshops with 13 participants total from design and development backgrounds. We carried out design activities to stimulate discussion on appropriate trust in the HCD process. This paper aims to help researchers and practitioners understand appropriate trust in AI through a design lens by illustrating how it interacts with the HCD process.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{orzan2024emergent,

title = {Emergent Cooperation under Uncertain Incentive Alignment},

author = {Orzan, Nicole and Acar, Erman and Grossi, Davide and R{ă}dulescu, Roxana},

year  = {2024},

date = {2024-01-01},

booktitle = {Proceedings of the 2024 International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2024)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/ecai/HanD024,

title = {Learning Reward Structure with Subtasks in Reinforcement Learning},

author = {Shuai Han and 

 Mehdi Dastani and 

 Shihan Wang},

url = {https://doi.org/10.3233/FAIA240751},

doi = {10.3233/FAIA240751},

year  = {2024},

date = {2024-01-01},

booktitle = {ECAI 2024 - 27th European Conference on Artificial Intelligence, 

 19-24 October 2024, Santiago de Compostela, Spain - Including 13th 

 Conference on Prestigious Applications of Intelligent Systems (PAIS 

 2024)},

volume = {392},

pages = {2282–2289},

publisher = {IOS Press},

series = {Frontiers in Artificial Intelligence and Applications},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/emnlp/ZhaoND024,

title = {Bootstrapped Policy Learning for Task-oriented Dialogue through Goal 

 Shaping},

author = {Yangyang Zhao and 

 Ben Niu and 

 Mehdi Dastani and 

 Shihan Wang},

url = {https://aclanthology.org/2024.emnlp-main.263},

year  = {2024},

date = {2024-01-01},

booktitle = {Proceedings of the 2024 Conference on Empirical Methods in Natural 

 Language Processing, EMNLP 2024, Miami, FL, USA, November 12-16, 

 2024},

pages = {4566–4580},

publisher = {Association for Computational Linguistics},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/hhai/Chang0KOA24,

title = {Human-Centered AI for Dementia Care: Using Reinforcement Learning 

 for Personalized Interventions Support in Eating and Drinking Scenarios},

author = {Wen{-}Tseng Chang and 

 Shihan Wang and 

 Stephanie Kramer and 

 Michel Oey and 

 Somaya Ben Allouch},

url = {https://doi.org/10.3233/FAIA240185},

doi = {10.3233/FAIA240185},

year  = {2024},

date = {2024-01-01},

booktitle = {HHAI 2024: Hybrid Human AI Systems for the Social Good - Proceedings 

 of the Third International Conference on Hybrid Human-Artificial Intelligence, 

 Malmö, Sweden, 10-14 June 2024},

volume = {386},

pages = {84–93},

publisher = {IOS Press},

series = {Frontiers in Artificial Intelligence and Applications},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/comma/HoS24,

title = {A General Dialogue Framework for Logic-based Argumentation},

author = {Loan Ho and 

 Stefan Schlobach},

editor = {Timotheus Kampik and 

 Kristijonas Cyras and 

 Antonio Rago and 

 Oana Cocarascu},

url = {https://ceur-ws.org/Vol-3768/paper7.pdf},

year  = {2024},

date = {2024-01-01},

booktitle = {Proceedings of the 2nd International Workshop on Argumentation for 

 eXplainable AI co-located with the 10th International Conference 

 on Computational Models of Argument (COMMA 2024), Hagen, Germany, 

 September 16, 2024},

volume = {3768},

pages = {41–55},

publisher = {CEUR-WS.org},

series = {CEUR Workshop Proceedings},

abstract = {There is an extensive body of work in logic-based argumentation, which links logic and argumentation, which is a potential solution to address inconsistencies or conflicting information in knowledge bases (KBs) by offering dialogue games as proof procedures to determine and explain the acceptance of propositions. Most existing work, though, focuses on specific logics (such as description logics, existential rules, defeasible and propositional logics), has limitations of representational aspects, for selected semantics and binary conflicts. In this paper, we generalise this work by introducing G-SAF, which generalises the notions of arguments, dialogues and dialogue trees for more general logical reasoning with inconsistencies, including the most common semantics and to facilitate reasoning with non-binary conflicts using argumentation with collective attacks (SAFs).},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/hri/MalnatskyWHL24,

title = {Shaping Relatable Robots: A Child-Centered Approach to Social Personalization},

author = {Elena Malnatsky and 

 Shenghui Wang and 

 Koen V. Hindriks and 

 Mike E. U. Ligthart},

editor = {Dan Grollman and 

 Elizabeth Broadbent and 

 Wendy Ju and 

 Harold Soh and 

 Tom Williams},

url = {https://doi.org/10.1145/3610978.3638374},

doi = {10.1145/3610978.3638374},

year  = {2024},

date = {2024-01-01},

booktitle = {Companion of the 2024 ACM/IEEE International Conference on Human-Robot 

 Interaction, HRI 2024, Boulder, CO, USA, March 11-15, 2024},

pages = {127–129},

publisher = {ACM},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/atal/ErdoganVY24,

title = {Computational Theory of Mind with Abstractions for Effective Human-Agent 

 Collaboration},

author = {Emre Erdogan and 

 Rineke Verbrugge and 

 Pinar Yolum},

editor = {Mehdi Dastani and 

 Jaime Sim{ã}o Sichman and 

 Natasha Alechina and 

 Virginia Dignum},

url = {https://dl.acm.org/doi/10.5555/3635637.3663123},

doi = {10.5555/3635637.3663123},

year  = {2024},

date = {2024-01-01},

booktitle = {Proceedings of the 23rd International Conference on Autonomous Agents 

 and Multiagent Systems, AAMAS 2024, Auckland, New Zealand, May 6-10, 

 2024},

pages = {2249–2251},

publisher = {International Foundation for Autonomous Agents and Multiagent Systems 

 / ACM},

abstract = {Empowering artificially intelligent agents with capabilities that humans use regularly is crucial to enable effective human-agent collaboration. One of these crucial capabilities is the modeling of Theory of Mind (ToM) reasoning: the human ability to reason about the mental content of others such as their beliefs, desires, and goals. However, it is generally impractical to track all individual mental attitudes of all other individuals and for many practical situations not even necessary. Hence, what is important is to capture enough information to create an approximate model that is effective and flexible. Accordingly, this paper proposes a computational ToM mechanism based on abstracting beliefs and knowledge into higher-level human concepts, called abstractions, similar to the ones that guide humans to effectively interact with each other (e.g., trust). We develop an agent architecture based on epistemic logic to formalize the computational dynamics of ToM reasoning. We identify important challenges regarding effective maintenance of abstractions and accurate use of ToM reasoning and demonstrate how our approach addresses these challenges over multiagent simulations.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/hhai/ErdoganDV24,

title = {Effective Maintenance of Computational Theory of Mind for Human-AI 

 Collaboration},

author = {Emre Erdogan and 

 Frank Dignum and 

 Rineke Verbrugge},

editor = {Fabian Lorig and 

 Jason Tucker and 

 Adam Dahlgren Lindström and 

 Frank Dignum and 

 Pradeep K. Murukannaiah and 

 Andreas Theodorou and 

 Pinar Yolum},

url = {https://doi.org/10.3233/FAIA240188},

doi = {10.3233/FAIA240188},

year  = {2024},

date = {2024-01-01},

booktitle = {HHAI 2024: Hybrid Human AI Systems for the Social Good - Proceedings 

 of the Third International Conference on Hybrid Human-Artificial Intelligence, 

 Malmö, Sweden, 10-14 June 2024},

volume = {386},

pages = {114–123},

publisher = {IOS Press},

series = {Frontiers in Artificial Intelligence and Applications},

abstract = {In order to enhance collaboration between humans and artificially intelligent agents, it is crucial to equip the computational agents with capabilities commonly used by humans. One of these capabilities is called Theory of Mind (ToM) reasoning, which is the human ability to reason about the mental contents of others, such as their beliefs, desires, and goals. For an agent to efficiently benefit from having a functioning computational ToM of its human partner in a collaboration, it needs to be practical in computationally tracking their mental attitudes and it needs to create approximate ToM models that can be effectively maintained. In this paper, we propose a computational ToM mechanism based on abstracting beliefs and knowledge into higher-level human concepts, referred to as abstractions. These abstractions, similar to those guiding human interactions (e.g., trust), form the basis of our modular agent architecture. We address an important challenge related to maintaining abstractions effectively, namely abstraction consistency. We propose different approaches to study this challenge in the context of a scenario inspired by a medical domain and provide an experimental evaluation over agent simulations.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{erdogan2024resolving,

title = {Resolving Multi-user Privacy Conflicts with Computational Theory of Mind},

author = {Erdogan, Emre and Dignum, Frank and Verbrugge, Rineke and Yolum, P{ı}nar},

url = {https://eprints.soton.ac.uk/489958/1/Proceedings_of_the_Second_International_Workshop_on_Citizen-Centric_Multiagent_Systems_2024_C-MAS_2024_.pdf},

year  = {2024},

date = {2024-01-01},

booktitle = {Workshop on Citizen-Centric Multiagent Systems},

pages = {22},

abstract = {Online Social Networks (OSNs) serve as digital platforms for users to share information and build relationships. These networks facilitate the sharing of diverse content, which may disclose personal information about users. Some of these contents pertain to multiple users (such as group pictures), with different privacy expectations. Sharing of such content may lead to multi-user privacy conflicts. Decision-making mechanisms are crucial to managing conflicting privacy preferences among users, reducing their effort in conflict resolution. Various mechanisms are proposed in the literature, most of which demand significant computational resources. We propose a novel approach based on computational modeling of Theory of Mind (ToM), the human ability to understand others’ mental states (e.g., their beliefs, preferences, goals, etc.), to portray users’ privacy expectations. We argue that leveraging computational ToM modeling allows the design of agents capable of accurately capturing users’ behavior and reasoning about other agents’ privacy understanding, making them effective tools in multi-user privacy conflict management. To illustrate our ideas, we consider a content-sharing scenario and point out potential benefits of using our agent-based computational ToM approach in resolution of privacy conflicts.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/egov/AminRD24,

title = {Designing organizational control mechanisms for consequential AI 

 systems: towards a situated methodology (poster)},

author = {Shan Amin and 

 Sander Renes and 

 Roel Dobbe},

editor = {Jolien Ubacht and 

 Joep Crompvoets and 

 Csaba Csáki and 

 Lieselot Danneels and 

 Marijn Janssen and 

 Marius Rohde Johannessen and 

 Thomas J. Lampoltshammer and 

 Habin Lee and 

 Ida Lindgren and 

 Sara Hofmann and 

 Peter Parycek and 

 Gabriela Viale Pereira and 

 Gerhard Schwabe and 

 Iryna Susha and 

 Efthimios Tambouris and 

 Anneke Zuiderwijk},

url = {https://ceur-ws.org/Vol-3737/paper52.pdf},

year  = {2024},

date = {2024-01-01},

booktitle = {Proceedings of Ongoing Research, Practitioners, Posters, Workshops, 

 and Projects of the International Conference EGOV-CeDEM-ePart 2024, 

 Ghent University and KU Leuven, Ghent/Leuven, Belgium, September 

 1-5, 2024},

volume = {3737},

publisher = {CEUR-WS.org},

series = {CEUR Workshop Proceedings},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/naacl/Meer24,

title = {Facilitating Opinion Diversity through Hybrid NLP Approaches},

author = {Michiel van der Meer},

editor = {Yang (Trista) Cao and 

 Isabel Papadimitriou and 

 Anaelia Ovalle and 

 Marcos Zampieri and 

 Francis Ferraro and 

 Swabha Swayamdipta},

url = {https://doi.org/10.18653/v1/2024.naacl-srw.29},

doi = {10.18653/V1/2024.NAACL-SRW.29},

year  = {2024},

date = {2024-01-01},

booktitle = {Proceedings of the 2024 Conference of the North American Chapter of 

 the Association for Computational Linguistics: Human Language Technologies: 

 Student Research Workshop, NAACL 2024, Mexico City, Mexico, June 

 18, 2024},

pages = {272–284},

publisher = {Association for Computational Linguistics},

abstract = {Modern democracies face a critical issue of declining citizen participation in decision-making. Online discussion forums are an important avenue for enhancing citizen participation. This thesis proposal 1) identifies the challenges involved in facilitating large-scale online discussions with Natural Language Processing (NLP), 2) suggests solutions to these challenges by incorporating hybrid human-AI technologies, and 3) investigates what these technologies can reveal about individual perspectives in online discussions. We propose a three-layered hierarchy for representing perspectives that can be obtained by a mixture of human intelligence and large language models. We illustrate how these representations can draw insights into the diversity of perspectives and allow us to investigate interactions in online discussions.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/hhai/MeerVJM24,

title = {Value-Sensitive Disagreement Analysis for Online Deliberation},

author = {Michiel van der Meer and 

 Piek Vossen and 

 Catholijn M. Jonker and 

 Pradeep K. Murukannaiah},

editor = {Fabian Lorig and 

 Jason Tucker and 

 Adam Dahlgren Lindström and 

 Frank Dignum and 

 Pradeep K. Murukannaiah and 

 Andreas Theodorou and 

 Pinar Yolum},

url = {https://doi.org/10.3233/FAIA240231},

doi = {10.3233/FAIA240231},

year  = {2024},

date = {2024-01-01},

booktitle = {HHAI 2024: Hybrid Human AI Systems for the Social Good - Proceedings 

 of the Third International Conference on Hybrid Human-Artificial Intelligence, 

 Malmö, Sweden, 10-14 June 2024},

volume = {386},

pages = {481–484},

publisher = {IOS Press},

series = {Frontiers in Artificial Intelligence and Applications},

abstract = {Disagreements are common in online societal deliberation and may be crucial for effective collaboration, for instance in helping users understand opposing viewpoints. Although there exist automated methods for recognizing disagreement, a deeper understanding of factors that influence disagreement is currently missing. We investigate a hypothesis that differences in personal values influence disagreement in online discussions. Using Large Language Models (LLMs) for estimating both profiles of personal values and disagreement, we conduct a large-scale experiment involving 11.4M user comments. We find that the dissimilarity of value profiles correlates with disagreement only in specific cases, but that incorporating self-reported value profiles changes these results to be more undecided.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/eacl/MeerVJM24,

title = {An Empirical Analysis of Diversity in Argument Summarization},

author = {Michiel van der Meer and 

 Piek Vossen and 

 Catholijn M. Jonker and 

 Pradeep K. Murukannaiah},

editor = {Yvette Graham and 

 Matthew Purver},

url = {https://aclanthology.org/2024.eacl-long.123},

year  = {2024},

date = {2024-01-01},

booktitle = {Proceedings of the 18th Conference of the European Chapter of the 

 Association for Computational Linguistics, EACL 2024 - Volume 1: 

 Long Papers, St. Julian's, Malta, March 17-22, 2024},

pages = {2028–2045},

publisher = {Association for Computational Linguistics},

abstract = {Presenting high-level arguments is a crucial task for fostering participation in online societal discussions. Current argument summarization approaches miss an important facet of this task—capturing textitdiversity—which is important for accommodating multiple perspectives. We introduce three aspects of diversity: those of opinions, annotators, and sources. We evaluate approaches to a popular argument summarization task called Key Point Analysis, which shows how these approaches struggle to (1) represent arguments shared by few people, (2) deal with data from various sources, and (3) align with subjectivity in human-provided annotations. We find that both general-purpose LLMs and dedicated KPA models exhibit this behavior, but have complementary strengths. Further, we observe that diversification of training data may ameliorate generalization in zero-shot cases. Addressing diversity in argument summarization requires a mix of strategies to deal with subjectivity.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/emnlp/MeerFML24,

title = {Annotator-Centric Active Learning for Subjective NLP Tasks},

author = {Michiel van der Meer and 

 Neele Falk and 

 Pradeep K. Murukannaiah and 

 Enrico Liscio},

editor = {Yaser Al{-}Onaizan and 

 Mohit Bansal and 

 Yun{-}Nung Chen},

url = {https://aclanthology.org/2024.emnlp-main.1031},

year  = {2024},

date = {2024-01-01},

booktitle = {Proceedings of the 2024 Conference on Empirical Methods in Natural 

 Language Processing, EMNLP 2024, Miami, FL, USA, November 12-16, 

 2024},

pages = {18537–18555},

publisher = {Association for Computational Linguistics},

abstract = {Active Learning (AL) addresses the high costs of collecting human annotations by strategically annotating the most informative samples. However, for subjective NLP tasks, incorporating a wide range of perspectives in the annotation process is crucial to capture the variability in human judgments. We introduce Annotator-Centric Active Learning (ACAL), which incorporates an annotator selection strategy following data sampling. Our objective is two-fold: (1) to efficiently approximate the full diversity of human judgments, and (2) to assess model performance using annotator-centric metrics, which value minority and majority perspectives equally. We experiment with multiple annotator selection strategies across seven subjective NLP tasks, employing both traditional and novel, human-centered evaluation metrics. Our findings indicate that ACAL improves data efficiency and excels in annotator-centric performance evaluations. However, its success depends on the availability of a sufficiently large and diverse pool of annotators to sample from.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{StegingJURISIN2024,

title = {A hybrid approach to legal textual entailment},

author = {Steging, C. and 

{van Leeuwen}, L.},

url = {https://jurisinformaticscenter.github.io/jurisin2024/jurisin2024local_proceedings.pdf},

year  = {2024},

date = {2024-01-01},

booktitle = {Eighteenth International Workshop on Juris-Informatics (JURISIN 2024)},

address = {Hamamatsu, Japan},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1007/978-3-031-51777-8_6,

title = {Predictive Theory of Mind Models Based on Public Announcement Logic},

author = {Top, Jakob Dirk 

and Jonker, Catholijn 

and Verbrugge, Rineke 

and de Weerd, Harmen},

editor = {Gierasimczuk, Nina 

and Velázquez-Quesada, Fernando R.},

isbn = {978-3-031-51777-8},

year  = {2024},

date = {2024-01-01},

booktitle = {Dynamic Logic. New Trends and Applications},

pages = {85–103},

publisher = {Springer Nature Switzerland},

address = {Cham},

abstract = {Epistemic logic can be used to reason about statements such as `I know that you know that I know that $$backslashvarphi $$φ'. In this logic, and its extensions, it is commonly assumed that agents can reason about epistemic statements of arbitrary nesting depth. In contrast, empirical findings on Theory of Mind, the ability to (recursively) reason about mental states of others, show that human recursive reasoning capability has an upper bound.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/icwe/ZafarghandiQHSC24,

title = {Investigating the Usefulness of Product Reviews Through Bipolar Argumentation Frameworks},

author = {Atefeh {Keshavarzi Zafarghandi} and Ji Qi and Laura Hollink and Erik Tjong Kim Sang and Davide Ceolin},

year  = {2024},

date = {2024-01-01},

booktitle = {ICWE},

publisher = {Springer},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ZafarghandiW24,

title = {Complexity of Semi-Stable Semantics in Abstract Dialectical Frameworks},

author = {Atefeh {Keshavarzi Zafarghandi} and Johannes Peter Wallner},

year  = {2024},

date = {2024-01-01},

booktitle = {Proceedings of the 5th International Conference on Computational Models of Argument (COMMA 2024)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:NMR:ZafarghandiK24,

title = {Using ADFs for Inconsistency-Tolerant Query Answering with Existential Rules},

author = {Atefeh {Keshavarzi} and Patrick Koopmann},

year  = {2024},

date = {2024-01-01},

booktitle = {22th International Workshop on Non-Monotonic Reasoning (NMR)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{park-etal-2024-morality,

title = {Morality is Non-Binary: Building a Pluralist Moral Sentence Embedding Space using Contrastive Learning},

author = {Park, Jeongwoo and Liscio, Enrico and Murukannaiah, Pradeep},

url = {https://aclanthology.org/2024.findings-eacl.45},

year  = {2024},

date = {2024-01-01},

booktitle = {Findings of the Association for Computational Linguistics: EACL 2024},

pages = {654–673},

publisher = {Association for Computational Linguistics},

address = {St. Julian's, Malta},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}