Understanding agent systems
Markov Decision Processes: Discrete Stochastic Dynamic Programming
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Introduction to Multiagent Systems
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Artificial Intelligence: A Modern Approach
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A system of exchange values to support social interactions in artificial societies
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Methods and Applications of Interval Analysis (SIAM Studies in Applied and Numerical Mathematics) (Siam Studies in Applied Mathematics, 2.)
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Centralized Regulation of Social Exchanges Between Personality-Based Agents
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On interval fuzzy S-implications
Information Sciences: an International Journal
Semantical concepts for a formal structural dynamics of situated multiagent systems
COIN'07 Proceedings of the 2007 international conference on Coordination, organizations, institutions, and norms in agent systems III
ESOA'05 Proceedings of the Third international conference on Engineering Self-Organising Systems
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This work presents a model for Markov Decision Processes applied to the problem of keeping two agents in equilibrium with respect to the values they exchange when they interact. Interval mathematics is used to model the qualitative values involved in interactions. The optimal policy is constrained by the adopted model of social interactions. The MDP is assigned to a supervisor, that monitors the agents' actions and makes recommendations to keep them in equilibrium. The agents are autonomous and allowed to not follow the recommendations. Due to the qualitative nature of the exchange values, even when agents follow the recommendations, the decision process is non-trivial.