David Carmel and Shaul Markovitch. Learning Models of Intelligent Agents. In *Proceedings of the Thirteenth National Conference on Artificial Intelligence*, 62-67 Portland, Oregon, 1996.

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.

@inproceedings{Carmel:1996:LMIa, Author = {David Carmel and Shaul Markovitch}, Title = {Learning Models of Intelligent Agents}, Year = {1996}, Booktitle = {Proceedings of the Thirteenth National Conference on Artificial Intelligence}, Pages = {62--67}, Address = {Portland, Oregon}, Url = {http://www.cs.technion.ac.il/~shaulm/papers/pdf/Carmel-Markovitch-OMM-aaai1996.pdf}, Keywords = {Opponent Modeling, Repeated Games, Multi-Agent Systems, Learning DFA, Games}, Abstract = { 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. } }