Goal recognition over POMDPs: inferring the intention of a POMDP agent


Autoria(s): Ramírez Jávega, Miquel; Geffner, Hector
Contribuinte(s)

Universitat Pompeu Fabra

Data(s)

02/07/2013

Resumo

Plan recognition is the problem of inferring the goals and plans of an agent from partial observations of her behavior. Recently, it has been shown that the problem can be formulated and solved usingplanners, reducing plan recognition to plan generation.In this work, we extend this model-basedapproach to plan recognition to the POMDP setting, where actions are stochastic and states are partially observable. The task is to infer a probability distribution over the possible goals of an agent whose behavior results from a POMDP model. The POMDP model is shared between agent and observer except for the true goal of the agent that is hidden to the observer. The observations are action sequences O that may contain gaps as some or even most of the actions done by the agent may not be observed. We show that the posterior goal distribution P(GjO) can be computed from the value function VG(b) over beliefs b generated by the POMDPplanner for each possible goal G. Some extensionsof the basic framework are discussed, and a numberof experiments are reported.

H. Geffner is partially supported by grants TIN2009-10232, MICINN, Spain, and EC-7PM-SpaceBook.

Identificador

http://hdl.handle.net/10230/19943

Idioma(s)

eng

Publicador

Association for the Advancement of Artificial Intelligence (AAAI)

Relação

info:eu-repo/grantAgreement/EC/FP7/257593

Direitos

© [2011], Association for the Advancement of Artificial Intelligence (www.aaai.org)

info:eu-repo/semantics/openAccess

Palavras-Chave #Planificació -- Informàtica #Intel·ligència artificial
Tipo

info:eu-repo/semantics/conferenceObject

info:eu-repo/semantics/acceptedVersion