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Issue title: Multi-agent systems research in the United Kingdom
Guest editors: Stefano V. Albrecht and Michael Woolridge
Article type: Research Article
Authors: Black, Elizabeth | Brandão, Martim | Cocarascu, Oana | De Keijzer, Bart | Du, Yali | Long, Derek | Luck, Michael; * | McBurney, Peter | Meroño-Peñuela, Albert | Miles, Simon | Modgil, Sanjay | Moreau, Luc | Polukarov, Maria | Rodrigues, Odinaldo | Ventre, Carmine
Affiliations: Department of Informatics, King’s College London, Bush House, 30 Aldwych, London WC2B 4BG, United Kingdom
Correspondence: [*] Corresponding author. E-mail: michael.luck@kcl.ac.uk.
Abstract: Current work on multi-agent systems at King’s College London is extensive, though largely based in two research groups within the Department of Informatics: the Distributed Artificial Intelligence (DAI) thematic group and the Reasoning & Planning (RAP) thematic group. DAI combines AI expertise with political and economic theories and data, to explore social and technological contexts of interacting intelligent entities. It develops computational models for analysing social, political and economic phenomena to improve the effectiveness and fairness of policies and regulations, and combines intelligent agent systems, software engineering, norms, trust and reputation, agent-based simulation, communication and provenance of data, knowledge engineering, crowd computing and semantic technologies, and algorithmic game theory and computational social choice, to address problems arising in autonomous systems, financial markets, privacy and security, urban living and health. RAP conducts research in symbolic models for reasoning involving argumentation, knowledge representation, planning, and other related areas, including development of logical models of argumentation-based reasoning and decision-making, and their usage for explainable AI and integration of machine and human reasoning, as well as combining planning and argumentation methodologies for strategic argumentation.
Keywords: Argumentation, norms, agent-based simulation, strategic interaction, multi-agent reinforcement learning, dialogue protocols
DOI: 10.3233/AIC-220133
Journal: AI Communications, vol. 35, no. 4, pp. 309-325, 2022
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