Anytime intention recognition via incremental bayesian network reconstruction

The Anh Han; Luís Moniz Pereira

Anytime intention recognition via incremental bayesian network reconstruction

The Anh Han, Luís Moniz Pereira

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

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 language	English
Title of host publication	Proactive Assistant Agents - Papers from the AAAI Fall Symposium, Technical Report
Pages	20-25
Number of pages	6
Volume	FS-10-07
Publication status	Published - 1 Dec 2010
Event	2010 AAAI Fall Symposium - Arlington, VA, United States Duration: 11 Nov 2010 → 13 Nov 2010

Conference

Conference	2010 AAAI Fall Symposium
Country/Territory	United States
City	Arlington, VA
Period	11/11/10 → 13/11/10

Cite this

@inproceedings{4c3c5a11d8054560bfc56f5e37b1f817,

title = "Anytime intention recognition via incremental bayesian network reconstruction",

abstract = "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.",

author = "Han, {The Anh} and Pereira, {Lu{\'i}s Moniz}",

year = "2010",

month = dec,

day = "1",

language = "English",

isbn = "9781577354895",

volume = "FS-10-07",

pages = "20--25",

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note = "2010 AAAI Fall Symposium ; Conference date: 11-11-2010 Through 13-11-2010",

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T1 - Anytime intention recognition via incremental bayesian network reconstruction

AU - Han, The Anh

AU - Pereira, Luís Moniz

PY - 2010/12/1

Y1 - 2010/12/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=79960123169&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:79960123169

SN - 9781577354895

VL - FS-10-07

SP - 20

EP - 25

BT - Proactive Assistant Agents - Papers from the AAAI Fall Symposium, Technical Report

T2 - 2010 AAAI Fall Symposium

Y2 - 11 November 2010 through 13 November 2010

ER -

Anytime intention recognition via incremental bayesian network reconstruction

Abstract

Conference

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