Operations Research
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Planning and acting in partially observable stochastic domains
Artificial Intelligence
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
Sequential Optimality and Coordination in Multiagent Systems
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Evaluating Influence Diagrams using LIMIDs
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
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
Approximating state estimation in multiagent settings using particle filters
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Exact solutions of interactive POMDPs using behavioral equivalence
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Interactive dynamic influence diagrams
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Formal models and algorithms for decentralized decision making under uncertainty
Autonomous Agents and Multi-Agent Systems
A particle filtering based approach to approximating interactive POMDPs
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
A framework for sequential planning in multi-agent settings
Journal of Artificial Intelligence Research
Anytime point-based approximations for large POMDPs
Journal of Artificial Intelligence Research
Taming decentralized POMDPs: towards efficient policy computation for multiagent settings
IJCAI'03 Proceedings of the 18th international joint conference on 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
Computing optimal policies for partially observable decision processes using compact representations
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
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
Speeding up exact solutions of interactive dynamic influence diagrams using action equivalence
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
A PGM framework for recursive modeling of players in simple sequential Bayesian games
International Journal of Approximate Reasoning
Model identification in interactive influence diagrams using mutual information
Web Intelligence and Agent Systems
An influence diagram approach for multiagent time-critical dynamic decision modeling
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Approximating behavioral equivalence of models using top-k policy paths
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Approximating Model Equivalence in Interactive Dynamic Influence Diagrams Using Top K Policy Paths
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Continuous time planning for multiagent teams with temporal constraints
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Improved use of partial policies for identifying behavioral equivalence
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Exploiting model equivalences for solving interactive dynamic influence diagrams
Journal of Artificial Intelligence Research
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
Incremental clustering and expansion for faster optimal planning in decentralized POMDPs
Journal of Artificial Intelligence Research
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We develop new graphical representations for the problem of sequential decision making in partially observable multiagent environments, as formalized by interactive partially observable Markov decision processes (I-POMDPs). The graphical models called interactive influence diagrams (I-IDs) and their dynamic counterparts, interactive dynamic influence diagrams (I-DIDs), seek to explicitly model the structure that is often present in real-world problems by decomposing the situation into chance and decision variables, and the dependencies between the variables. I-DIDs generalize DIDs, which may be viewed as graphical representations of POMDPs, to multiagent settings in the same way that I-POMDPs generalize POMDPs. I-DIDs may be used to compute the policy of an agent given its belief as the agent acts and observes in a setting that is populated by other interacting agents. Using several examples, we show how I-IDs and I-DIDs may be applied and demonstrate their usefulness. We also show how the models may be solved using the standard algorithms that are applicable to DIDs. Solving I-DIDs exactly involves knowing the solutions of possible models of the other agents. The space of models grows exponentially with the number of time steps. We present a method of solving I-DIDs approximately by limiting the number of other agents' candidate models at each time step to a constant. We do this by clustering models that are likely to be behaviorally equivalent and selecting a representative set from the clusters. We discuss the error bound of the approximation technique and demonstrate its empirical performance.