Expectation: Personalized Explainable Artificial Intelligence for Decentralized Agents with Heterogeneous Knowledge

by Davide Calvaresi, Giovanni Ciatto, Amro Najjar, Reyhan Aydoğan, Leon Van der Torre, Andrea Omicini, and Michael I. Schumacher Abstract Explainable AI (XAI) has emerged in recent years as a set of techniques and methodologies to interpret and explain machine learning (ML) predictors. To date, many initiatives have been proposed. Nevertheless, current research efforts mainly focus on methods tailored to specific ML tasks and algorithms, such as image classification and sentiment analysis.
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On Explainable Negotiations via Argumentation

by Contreras, Victor and Aydoğan, Reyhan and Najjar, Amro and Calvaresi, Davide Abstract TBD How to access URL: http://publications.hevs.ch/index.php/publications/show/2883 How to cite Bibtex @incollection{canc-bnaic-2021-explanable-negotiations, address = {}, author = {Contreras, Victor and Aydoğan, Reyhan and Najjar, Amro and Calvaresi, Davide}, booktitle = {Proceedings of BNAIC 2021}, doi = {}, editor = {}, isbn = {}, isbn-online = {}, issn = {}, keywords = {explainable negotiation}, pages = {}, publisher = {ACM}, series = {}, subseries = {}, title = {On Explainable Negotiations via Argumentation}, url = {http://publications.
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Explanation-Based Negotiation Protocol for Nutrition Virtual Coaching

by Berk Buzcu, Vanitha Varadhajaran, Igor Tchappi, Amro Najjar, Davide Calvaresi and Reyhan Aydoğan Abstract People’s awareness about the importance of healthy lifestyles is rising. This opens new possibilities for personalized intelligent health and coaching applications. In particular, there is a need for more than simple recommendations and mechanistic interactions. Recent studies have identified nutrition virtual coaching systems (NVC) as a technological solution, possibly bridging technologies such as recommender, informative, persuasive, and argumentation systems.
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Bidding Support by the Pocket Negotiator Improves Negotiation Outcomes

by Reyhan Aydoğan, and Catholijn M. Jonker Abstract This paper presents the negotiation support mechanisms provided by the Pocket Negotiator (PN) and an elaborate empirical evaluation of the economic decision support (EDS) mechanisms during the bidding phase of negotiations as provided by the PN. Some of these support mechanisms are offered actively, some passively. With passive support we mean that the user only gets that support by clicking a button, whereas active support is provided without prompting.
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A Survey of Decision Support Mechanisms for Negotiation

by Reyhan Aydoğan, and Catholijn M. Jonker Abstract This paper introduces a dependency analysis and a categorization of conceptualized and existing economic decision support mechanisms for negotiation. The focus of our survey is on economic decision support mechanisms, although some behavioural support mechanisms were included, to recognize the important work in that area. We categorize support mechanisms from four different aspects: (i) economic versus behavioral decision support, (ii) analytical versus strategical support, (iii) active versus passive support and (iv) implicit versus explicit support.
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Symbolic Knowledge Extraction for Explainable Nutritional Recommenders

by Matteo Magnini, Giovanni Ciatto, Furkan Canturk, Reyhan Aydoğan, and Andrea Omicini Abstract Background and objective This paper focuses on nutritional recommendation systems (RS), i.e. AI-powered automatic systems providing users with suggestions about what to eat to pursue their weight/body shape goals. A trade-off among (potentially) conflictual requirements must be taken into account when designing these kinds of systems, there including: (i) adherence to experts’ prescriptions, (ii) adherence to users’ tastes and preferences, (iii) explainability of the whole recommendation process.
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A General-Purpose Protocol for Multi-Agent based Explanations

by Giovanni Ciatto, Matteo Magnini, Berk Buzcu, Reyhan Aydoğan, and Andrea Omicini Abstract Building on prior works on explanation negotiation protocols, this paper proposes a general-purpose protocol for multi-agent systems where recommender agents may need to provide explanations for their recommendations. The protocol specifies the roles and responsibilities of the explainee and the explainer agent and the types of information that should be exchanged between them to ensure a clear and effective explanation.
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Metrics for Evaluating Explainable Recommender Systems

by Joris Hulstijn, Igor Tchappi, Amro Najjar, and Reyhan Aydoğan Abstract Recommender systems aim to support their users by reducing information overload so that they can make better decisions. Recommender systems must be transparent, so users can form mental models about the system’s goals, internal state, and capabilities, that are in line with their actual design. Explanations and transparent behaviour of the system should inspire trust and, ultimately, lead to more persuasive recommendations.
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Conflict-based negotiation strategy for human-agent negotiation

by Mehmet Onur Keskin, Berk Buzcu, and Reyhan Aydoğan Abstract Day by day, human-agent negotiation becomes more and more vital to reach a socially beneficial agreement when stakeholders need to make a joint decision together. Developing agents who understand not only human preferences but also attitudes is a significant prerequisite for this kind of interaction. Studies on opponent modeling are predominantly based on automated negotiation and may yield good predictions after exchanging hundreds of offers.
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A Survey of Decision Support Mechanisms for Negotiation

by Berk Buzcu, Melissa Tessa, Igor Tchappi, Amro Najjar, Joris Hulstijn, Davide Calvaresi, and Reyhan Aydoğan Abstract The awareness about healthy lifestyles is increasing, opening to personalized intelligent health coaching applications. A demand for more than mere suggestions and mechanistic interactions has driven attention to nutrition virtual coaching systems (NVC) as a bridge between human–machine interaction and recommender, informative, persuasive, and argumentation systems. NVC can rely on data-driven opaque mechanisms.
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Conflict-based negotiation strategy for human-agent negotiation

by Mehmet Onur Keskin, Berk Buzcu, and Reyhan Aydoğan Abstract Day by day, human-agent negotiation becomes more and more vital to reach a socially beneficial agreement when stakeholders need to make a joint decision together. Developing agents who understand not only human preferences but also attitudes is a significant prerequisite for this kind of interaction. Studies on opponent modeling are predominantly based on automated negotiation and may yield good predictions after exchanging hundreds of offers.
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