Operations Research
Do the right thing: studies in limited rationality
Do the right thing: studies in limited rationality
Learning models of other agents using influence diagrams
UM '99 Proceedings of the seventh international conference on User modeling
Rational Coordination in Multi-Agent Environments
Autonomous Agents and Multi-Agent Systems
Graphical Models for Game Theory
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Multi-agent influence diagrams for representing and solving games
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Reasoning about Rationality and Beliefs
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
Graphical models for online solutions to interactive POMDPs
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
An Information-Theoretic Approach to Model Identification in Interactive Influence Diagrams
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Graphical models for interactive POMDPs: representations and solutions
Autonomous Agents and Multi-Agent Systems
Improved approximation of interactive dynamic influence diagrams using discriminative model updates
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Approximate solutions of interactive dynamic influence diagrams using model clustering
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Journal of Artificial Intelligence Research
Speeding up exact solutions of interactive dynamic influence diagrams using action equivalence
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Hypotheses about Typical General Human Strategic Behavior in a Concrete Case
AI*IA '09: Proceedings of the XIth International Conference of the Italian Association for Artificial Intelligence Reggio Emilia on Emergent Perspectives in Artificial Intelligence
A PGM framework for recursive modeling of players in simple sequential Bayesian games
International Journal of Approximate Reasoning
Computing equilibria using interval constraints
CSCLP'04 Proceedings of the 2004 joint ERCIM/CoLOGNET international conference on Recent Advances in Constraints
Improved use of partial policies for identifying behavioral equivalence
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Decision as Choice of Potential Intentions
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Learning Communication in Interactive Dynamic Influence Diagrams
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Decision as choice of potential intentions
Web Intelligence and Agent Systems
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Multi-agent systems that use game-theoretic analysis for decision making traditionally take a normative approach, in which agents' decisions are derived rationally from the game description. This approach is insufficient to capture the decision making processes of real-life agents. Such agents may be partially irrational, they may use models other than the real world to make decisions, and they may be uncertain about their opponents' decision making processes. We present Networks of Influence Diagrams (NIDs), a language for descriptive decision and game theory that is based on graphical models. NIDs provide a framework for computing optimal decisions for agents that operate in an environment characterized by uncertainty, not only over states of knowledge but also over game mechanics and others' decision processes. NIDs allow the modeling of situations in which an agent has an incorrect model of the way the world works, or in which a modeler has uncertainty about the agent's model. One can also recursively model agents' uncertain beliefs about other agents' decision making models. We present an algorithm that computes the actions of agents under the assumption that they are rational with respect to their own model, but not necessarily with respect to the real world. Applications of our language include determining the cost to an agent of using an incorrect model, opponent modeling in games, and modeling bounded rationality.