Operations Research
Learning models of other agents using influence diagrams
UM '99 Proceedings of the seventh international conference on User modeling
A language for modeling agents' decision making processes in games
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
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)
Graphical models for online solutions to interactive POMDPs
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
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
Tank War Using Online Reinforcement Learning
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
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Interactive influence diagrams (I-IDs) offer a transparent and semantically clear representation for the decision-making problem in multiagent settings. They ascribe procedural models such as IDs and I-IDs to the behavior of other agents. Procedural models offer the benefit of understanding how others arrive at their behaviors. However, as model spaces are often bounded, the true models of others may not be present in the model space. In addition to considering the case when the true model is within the model space, we investigate the realistic case when the true model may fall outside the 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 in two repeated games and provide results in support.