Planning and acting in partially observable stochastic domains
Artificial Intelligence
A framework for sequential planning in multi-agent settings
Journal of Artificial Intelligence Research
Graphical models for online solutions to interactive POMDPs
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Graphical models for interactive POMDPs: representations and solutions
Autonomous Agents and Multi-Agent Systems
A PGM framework for recursive modeling of players in simple sequential Bayesian games
International Journal of Approximate Reasoning
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Partially Observable Markov Decision Processes (POMDPs) emerged as the primary framework for decision-theoretic planning in single agent settings. Solutions to POMDPs are optimal plans which are conditional on future observations. Dynamic Influence Diagrams (DIDs) are computational representations of POMDPs which compute solutions for finite time horizons in an on-line fashion. Interactive POMDPs (I-POMDPs) [5] generalize POMDPs to multi-agent settings by including models of other agents in the state space. Interactive DIDs (I-DIDs), presented in this paper, are computational representations of I-POMDPs, and thus generalizations of DIDs. DIDs are themselves temporal generalizations of influence diagrams [6].