A probabilistic plan recognition algorithm based on plan tree grammars
Artificial Intelligence
Exploiting Cycle Structures in Max-SAT
SAT '09 Proceedings of the 12th International Conference on Theory and Applications of Satisfiability Testing
Hypothesis pruning and ranking for large plan recognition problems
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
A general model for online probabilistic plan recognition
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Efficient context free parsing of multi-agent activities for team and plan recognition
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Evaluating the robustness of activity recognition using computational causal behavior models
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
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Multi-Agent Plan Recognition (MAPR) seeks to identify the dynamic team structures and team behaviors from the observed activity sequences (team traces) of a set of intelligent agents, based on a library of known team activity sequences (team plans). Previous MAPR systems require that team traces and team plans are fully observed. In this paper we relax this constraint, i.e., team traces and team plans are allowed to be partial. This is an important task in applying MAPR to real-world domains, since in many applications it is often difficult to collect full team traces or team plans due to environment limitations, e.g., military operation. This is also a hard problem since the information available is limited. We propose a novel approach to recognizing team plans from partial team traces and team plans. We encode the MAPR problem as a satisfaction problem and solve the problem using a state-of-the-art weighted MAX-SAT solver. We empirically show that our algorithm is both effective and efficient.