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The application of reinforcement learning algorithms to Partially Observable Stochastic Games (POSG) is challenging since each agent does not have access to the whole state information and, in case of concurrent learners, the environment has non-stationary dynamics. These problems could be partially overcome if the policies followed by the other agents were known, and, for this reason, many app...

This work offers a general framework of fuzzy constraint-based agent negotiation with opponent learning. The proposed approach via fuzzy probability constraint clusters the opponent’s information in negotiation process as proximate regularities to increase the efficiency on the convergence of behavior patterns, and eliminates the bulk of false hypotheses or beliefs to improves the effectiveness...

The application of reinforcement learning algorithms to Partially Observable Stochastic Games (POSG) is challenging since each agent does not have access to the whole state information and, in case of concurrent learners, the environment has non-stationary dynamics. These problems could be partially overcome if the policies followed by the other agents were known, and, for this reason, many app...

Since Emile Borel’s study in 1938, the game of poker has resurfaced every decade as a test bed for research in mathematics, economics, game theory, and now a variety of computer science subfields. Poker is an excellent domain for AI research because it is a game of imperfect information and a game where opponent modeling can yield virtually unlimited complexity. Recent strides in poker research...