David Carmel and Shaul Markovitch. Opponent Modeling in Multi-agent Systems. In Gerhard Weiss and Sandip Sen, Editors, Adaption And Learning In Multi-Agent Systems. Springer-Verlag, 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 a heuristic 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.
@incollection{Carmel:1996:OMM, Author = {David Carmel and Shaul Markovitch}, Title = {Opponent Modeling in Multi-agent Systems}, Year = {1996}, Booktitle = {Adaption And Learning In Multi-Agent Systems}, Volume = {1042}, Editor = {Gerhard Weiss and Sandip Sen}, Url = {http://www.cs.technion.ac.il/~shaulm/papers/pdf/Carmel-Markovitch-lnai1996.pdf}, Keywords = {Opponent Modeling, Multi-Agent Systems, Learning in Games, Games, Repeated Games, Learning DFA}, 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 a heuristic 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. } }