Operations Research
Bayesian learning in negotiation
International Journal of Human-Computer Studies - Evolution and learning in multiagent systems
Learning models of other agents using influence diagrams
UM '99 Proceedings of the seventh international conference on User modeling
Bayesian Update of Recursive Agent Models
User Modeling and User-Adapted Interaction
An Influence Diagram Model for Multi-Agent Negotiation
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Modeling opponent decision in repeated one-shot negotiations
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Robust game play against unknown opponents
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Opponent modelling in automated multi-issue negotiation using Bayesian learning
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Graphical models for interactive POMDPs: representations and solutions
Autonomous Agents and Multi-Agent Systems
On the difficulty of achieving equilibrium in interactive POMDPs
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Particle filtering for dynamic agent modelling in simplified poker
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Journal of Artificial Intelligence Research
Multi-agent influence diagrams for representing and solving games
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Learning models of intelligent agents
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
BPM'11 Proceedings of the 9th international conference on Business process management
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Interactive influence diagrams (I-IDs) offer a transparent and intuitive representation for the decision-making problem in multiagent settings. They ascribe procedural models such as influence diagrams and I-IDs to model the behavior of other agents. Procedural models offer the benefit of understanding how others arrive at their behaviors. Accurate behavioral models of others facilitate optimal decision-making in multiagent settings. However, identifying the true models of other agents is a challenging task. Given the assumption that the true model of the other agent lies within the set of models that we consider, we may utilize standard Bayesian learning to update the likelihood of each model given the observation histories of others' actions. However, as model spaces are often bounded, the true models of others may not be present in the model space. We then seek to identify models that are relevant to the observed behaviors of others and show how the agent may learn to identify these models. We evaluate the performance of our method on three repeated games and provide theoretical and empirical results in support.