Anytime intention recognition via incremental bayesian network reconstruction

Theanh Anh Han, Luís Moniz Pereira

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)


This paper presents an anytime algorithm for incremental intention recognition in a changing world. The algorithm is performed by dynamically constructing the intention recognition model on top of a prior domain knowledge base. The model is occasionally reconfigured by situating itself in the changing world and removing newly found out irrelevant intentions. We also discuss some approaches to knowledge base representation for supporting situation-dependent model construction. Reconfigurable Bayesian Networks are employed to produce the intention recognition model.

Original languageEnglish
Title of host publicationProactive Assistant Agents
Subtitle of host publicationPapers from the AAAI Fall Symposium, Technical Report
EditorsFelipe Meneguzzi, Jean Oh
Place of PublicationMenlo Park
PublisherAI Access Foundation
Number of pages6
ISBN (Print)9781577354895
Publication statusPublished - 2010
Event2010 AAAI Fall Symposium - Arlington, United States
Duration: 11 Nov 201013 Nov 2010

Publication series

NameAAAI Fall Symposium - Technical Report


Conference2010 AAAI Fall Symposium
Country/TerritoryUnited States
Internet address


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