Models of human behaviours used in multi-agent simulations are limited by the ability of introspection of the social actors: some of their knowledge (reflexes, habits, non-formalized expertise) cannot be extracted through interviews. In this paper, we propose an artificial maieutic approach to extract such pieces of knowledge, by helping the actors to better understand, and sometimes formulate, their own behaviours. We present here the first results using two complementary works in social simulations, one in the domain of air traffic control and one in the domain of common-pool resources sharing.
Differential games (DGs), considered as a typical model of game with continuous states and non-linear dynamics, play an important role in control and optimization. Finding optimal/approximate solutions for these game in the imperfect information setting is currently a challenge for mathematicians and computer scientists. This article presents a multi-agent learning approach to this problem. We hence propose a method called resolution-based policy search, which uses a limited non-uniform discretization of a perfect information game version to parameterize policies to learn. We then study the application of this method to an imperfect information zero-sum pursuit-evasion game (PEG). Experimental results demonstrate strong performance of our method and show that it gives better solutions than those given by traditional analytical methods.
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