• Sorted by Date • Classified by Publication Type • Classified by Topic • Grouped by Student (current) • Grouped by Former Students •
Dorit Avrahami-Zilberbrand and
Gal A. Kaminka. Hybrid Symbolic-Probabilistic Plan Recognizer: Initial steps. In Proceedings of the AAAI
Workshop on Modeling Others from Observations (MOO-06), 2006.
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.
@InProceedings{moo06dorit,
author = {Dorit Avrahami-Zilberbrand and Gal A. Kaminka},
title = {Hybrid Symbolic-Probabilistic Plan Recognizer: Initial steps},
OPTcrossref = {},
OPTkey = {},
booktitle = MOO-06,
OPTpages = {},
year = {2006},
abstract = {
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.
},
wwwnote = {},
OPTeditor = {},
OPTvolume = {},
OPTnumber = {},
OPTseries = {},
OPTaddress = {},
OPTmonth = {},
OPTorganization = {},
OPTpublisher = {},
OPTnote = {},
OPTannote = {}
}
Generated by bib2html.pl (written by Patrick Riley ) on Fri Apr 19, 2024 19:01:33