Title

Hypothesis Pruning And Ranking For Large Plan Recognition Problems

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. Copyright © 2008, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

Publication Date

12-24-2008

Publication Title

Proceedings of the National Conference on Artificial Intelligence

Volume

2

Number of Pages

998-1003

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

57749185874 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/57749185874

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