Abstract
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.
This work was partially supported by CTINFO/CNPq and FAPERGS.
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Dimuro, G.P., Costa, A.C.R. (2006). Interval-Based Markov Decision Processes for Regulating Interactions Between Two Agents in Multi-agent Systems. In: Dongarra, J., Madsen, K., Waśniewski, J. (eds) Applied Parallel Computing. State of the Art in Scientific Computing. PARA 2004. Lecture Notes in Computer Science, vol 3732. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11558958_12
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DOI: https://doi.org/10.1007/11558958_12
Publisher Name: Springer, Berlin, Heidelberg
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