TY - GEN
T1 - Probabilistic plan recognition in multiagent systems
AU - Saria, Suchi
AU - Mahadevan, Sridhar
PY - 2004
Y1 - 2004
N2 - We present a theoretical framework for online probabilistic plan recognition in cooperative multiagent systems. Our model extends the Abstract Hidden Markov Model (AHMM) (Bui, Venkatesh, & West 2002), and consists of a hierarchical dynamic Bayes network that allows reasoning about the interaction among multiple cooperating agents. We provide an in-depth analysis of two different policy termination schemes, T all and T any for concurrent action introduced in (Rohanimanesh & Mahadevan 2003). In the T all scheme, a joint policy terminates only when all agents have terminated executing their individual policies. In the T any scheme, a joint policy terminates as soon as any of the agents terminates executing its individual policy. Since exact inference is intractable, we describe an approximate algorithm using Rao-Blackwellized particle filtering. Our approximate inference procedure reduces the complexity from exponential time in N, the number of agents and K, the number of levels, to time linear in both N and K̂ ≤ K (the lowest-level of plan coordination) for the T all termination scheme and O(N log N) and linear in K̂ for the T any termination scheme.
AB - We present a theoretical framework for online probabilistic plan recognition in cooperative multiagent systems. Our model extends the Abstract Hidden Markov Model (AHMM) (Bui, Venkatesh, & West 2002), and consists of a hierarchical dynamic Bayes network that allows reasoning about the interaction among multiple cooperating agents. We provide an in-depth analysis of two different policy termination schemes, T all and T any for concurrent action introduced in (Rohanimanesh & Mahadevan 2003). In the T all scheme, a joint policy terminates only when all agents have terminated executing their individual policies. In the T any scheme, a joint policy terminates as soon as any of the agents terminates executing its individual policy. Since exact inference is intractable, we describe an approximate algorithm using Rao-Blackwellized particle filtering. Our approximate inference procedure reduces the complexity from exponential time in N, the number of agents and K, the number of levels, to time linear in both N and K̂ ≤ K (the lowest-level of plan coordination) for the T all termination scheme and O(N log N) and linear in K̂ for the T any termination scheme.
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M3 - Conference contribution
AN - SCOPUS:13444261465
SN - 1577352009
SN - 9781577352006
T3 - Proceedings of the 14th International Conference on Automated Planning and Scheduling, ICAPS 2004
SP - 287
EP - 296
BT - Proceedings of the 14th International Conference on Automated Planning and Scheduling, ICAPS 2004
A2 - Zilberstein, S.
A2 - Koehler, J.
A2 - Koenig, S.
T2 - Proceedings of the 14th International Conference on Automated Planning and Scheduling, ICAPS 2004
Y2 - 3 June 2004 through 7 June 2004
ER -