Learning Models of Intelligent Agents

Agents that operate in a multi-agent system need an efficient strategy to handle their encounters with other agents involved. Searching for an optimal interactive strategy is a hard problem because it depends mostly on the behavior of the others. In this work, interaction among agents is represented as a repeated two-player game, where the agents ’ objective is to look for a strategy that maximizes their expected sum of rewards in the game. We assume that agents ’ strategies can be modeled as finite automata. A model-based approach is presented as a possible method for learning an effective interactive strategy. First, we describe how an agent should find an optimal strategy against a given model. Second, we present an unsupervised algorithm that infers a model of the opponent’s automaton from its input/output behavior. A set of experiments that show the potential merit of the algorithm is reported as well