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Information and Computation
Inference of finite automata using homing sequences
Information and Computation
Learning Fallible Deterministic Finite Automata
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Applied Intelligence
Exploration Strategies for Model-based Learning in Multi-agent Systems: Exploration Strategies
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Rational Coordination in Multi-Agent Environments
Autonomous Agents and Multi-Agent Systems
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ATAL '01 Revised Papers from the 8th International Workshop on Intelligent Agents VIII
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Advances in evolutionary computing
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CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
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Artificial Intelligence
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Autonomous Agents and Multi-Agent Systems
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Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
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AI Magazine - Special issue on achieving human-level AI through integrated systems and research
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Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
An Information-Theoretic Approach to Model Identification in Interactive Influence Diagrams
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OMBO: An opponent modeling approach
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AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
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Journal of Artificial Intelligence Research
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IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Stable strategies for sharing information among agents
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Multiagent learning for open systems: a study in opponent classification
Adaptive agents and multi-agent systems
Model identification in interactive influence diagrams using mutual information
Web Intelligence and Agent Systems
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ADMI'10 Proceedings of the 6th international conference on Agents and data mining interaction
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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.