Abstract
This paper proposes a method allowing an agent to perform in a socially fair way by considering other agents’ preferences. Such a balanced action selection process is based on declarative diagnosis, which enables the removal of contradictions arising as all agents’ preferences are confronted within the deciding agent. Agents can be negatively affected when some of their preferences are not respected for the sake of a global compromise. The set of preferences to be yielded by agents in order to remove all contradictions in a balanced way (i.e. the diagnosis that better manages how each agent is to be affected) is determined by minimising a cost function computed over application independent features. By respecting the resulting non-contradictory preferences set, the deciding agent acts cooperatively.
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Santana, P., Pereira, L.M. (2006). Emergence of Cooperation Through Mutual Preference Revision. In: Ali, M., Dapoigny, R. (eds) Advances in Applied Artificial Intelligence. IEA/AIE 2006. Lecture Notes in Computer Science(), vol 4031. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11779568_11
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DOI: https://doi.org/10.1007/11779568_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35453-6
Online ISBN: 978-3-540-35454-3
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