Hypothesis Pruning and Ranking for Large Plan Recognition Problems

Gita Sukthankar and Katia Sycara
Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence (AAAI-08), July, 2008.


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Abstract
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.

Keywords
multi-agent team plan recognition

Notes
Sponsor: Army Research Labs
Associated Center(s) / Consortia: Center for Integrated Manfacturing Decision Systems
Associated Lab(s) / Group(s): Advanced Agent - Robotics Technology Lab
Associated Project(s): IBM ITA: Human-Agent Teamwork Models and AFOSR PRET: Information Fusion for Command and Control: The Translation of Raw Data To Actionable Knowledge and Decision
Number of pages: 6

Text Reference
Gita Sukthankar and Katia Sycara, "Hypothesis Pruning and Ranking for Large Plan Recognition Problems," Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence (AAAI-08), July, 2008.

BibTeX Reference
@inproceedings{Sukthankar_2008_6133,
   author = "Gita Sukthankar and Katia Sycara",
   editor = "Association for the Advancement of Artificial Intelligence",
   title = "Hypothesis Pruning and Ranking for Large Plan Recognition Problems",
   booktitle = "Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence (AAAI-08)",
   month = "July",
   year = "2008",
}