A Bayesian model of plan recognition
Artificial Intelligence
A framework for sequential planning in multi-agent settings
Journal of Artificial Intelligence Research
Monitoring teams by overhearing: a multi-agent plan-recognition approach
Journal of Artificial Intelligence Research
Goal recognition through goal graph analysis
Journal of Artificial Intelligence Research
PsychSim: modeling theory of mind with decision-theoretic agents
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
A new model of plan recognition
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Proactive Authoring for Interactive Drama: An Author's Assistant
IVA '07 Proceedings of the 7th international conference on Intelligent Virtual Agents
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
Speeding up exact solutions of interactive dynamic influence diagrams using action equivalence
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Towards a unifying characterization for quantifying weak coupling in dec-POMDPs
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
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
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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Agents must form and update mental models about each other in a wide range of domains: team coordination, plan recognition, social simulation, user modeling, games of incomplete information, etc. Existing research typically treats the problem of forming beliefs about other agents as an isolated subproblem, where the modeling agent starts from an initial set of possible models for another agent and then maintains a belief about which of those models applies. This initial set of models is typically a full specification of possible agent types. Although such a rich space gives the modeling agent high accuracy in its beliefs, it will also incur high cost in maintaining those beliefs. In this paper, we demonstrate that by taking this modeling problem out of its isolation and placing it back within the overall decision-making context, the modeling agent can drastically reduce this rich model space without sacrificing any performance. Our approach comprises three methods. The first method clusters models that lead to the same behaviors in the modeling agent's decision-making context. The second method clusters models that may produce different behaviors, but produce equally preferred outcomes with respect to the utility of the modeling agent. The third technique sacrifices a fixed amount of accuracy by clustering models that lead to performance losses that are below a certain threshold. We illustrate our framework using a social simulation domain and demonstrate its value by showing the minimal mental model spaces that it generates.