TY - GEN
T1 - Hybrid symbolic-probabilistic plan recognizer
T2 - 2006 AAAI Workshop
AU - Avrahami-Zilberbrand, Dorit
AU - Kaminka, Gal A.
PY - 2006
Y1 - 2006
N2 - It is important for agents to model other agents' unobserved plans and goals, based on their observable actions, a process known as plan recognition. Plan recognition often takes the form of matching observations of an agent's actions to a plan-library, a model of possible plans selected by the agent. In this paper, we present efficient algorithms that handle a number of key capabilities implied by plan recognition applications, in the context of hybrid symbolic-probabilistic recognizer. The central idea behind the hybrid approach is to combine the symbolic approach with probabilistic inference: the symbolic recognizer efficiently filters inconsistent hypotheses, passing only the consistent hypotheses to a probabilistic inference engine. There are few investigations that utilize an hybrid symbolic-probabilistic approach. The advantage of this kind of inference is potentially enormous. First, it can be highly efficient. Second, it can efficiently deal with richer class of plan recognition challenges, such as recognition based on duration of behaviors, recognition despite intermittently lost observations, and recognition of interleaved plans.
AB - It is important for agents to model other agents' unobserved plans and goals, based on their observable actions, a process known as plan recognition. Plan recognition often takes the form of matching observations of an agent's actions to a plan-library, a model of possible plans selected by the agent. In this paper, we present efficient algorithms that handle a number of key capabilities implied by plan recognition applications, in the context of hybrid symbolic-probabilistic recognizer. The central idea behind the hybrid approach is to combine the symbolic approach with probabilistic inference: the symbolic recognizer efficiently filters inconsistent hypotheses, passing only the consistent hypotheses to a probabilistic inference engine. There are few investigations that utilize an hybrid symbolic-probabilistic approach. The advantage of this kind of inference is potentially enormous. First, it can be highly efficient. Second, it can efficiently deal with richer class of plan recognition challenges, such as recognition based on duration of behaviors, recognition despite intermittently lost observations, and recognition of interleaved plans.
UR - http://www.scopus.com/inward/record.url?scp=33847672650&partnerID=8YFLogxK
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AN - SCOPUS:33847672650
SN - 1577352955
SN - 9781577352952
T3 - AAAI Workshop - Technical Report
SP - 1
EP - 7
BT - Modeling Others from Observation - Papers from the AAAI Workshop, Technical Report
Y2 - 16 July 2006 through 17 July 2006
ER -