HTN planning: complexity and expressivity
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
A framework for recognizing multi-agent action from visual evidence
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Learning and inferring transportation routines
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Assessing the complexity of plan recognition
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
High-level goal recognition in a wireless LAN
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Journal of Artificial Intelligence Research
Fast and complete symbolic plan recognition
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Intention-based decision making with evolution prospection
EPIA'11 Proceedings of the 15th Portugese conference on Progress in artificial intelligence
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This paper addresses the problem of plan recognition for multi-agent teams. Complex multi-agent tasks typically require dynamic teams where the team membership changes over time. Teams split into subteams to work in parallel, merge with other teams to tackle more demanding tasks, and disband when plans are completed. We introduce a new multi-agent plan representation that explicitly encodes dynamic team membership and demonstrate the suitability of this formalism for plan recognition. From our multi-agent plan representation, we extract local temporal dependencies that dramatically prune the hypothesis set of potentially-valid team plans. The reduced plan library can be efficiently processed to obtain the team state history. Naive pruning can be inadvisable when low-level observations are unreliable due to sensor noise and classification errors. In such conditions, we eschew pruning in favor of prioritization and show how our scheme can be extended to rank-order the hypotheses. Experiments show that this robust pre-processing approach ranks the correct plan within the top 10%, even under conditions of severe noise.