Anytime Intention Recognition via Incremental Bayesian Network Reconstruction

DI Group Author

Anytime Intention Recognition via Incremental Bayesian Network Reconstruction

DI Group Author

DI - Departamento de Informática

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

2 Citations (Scopus)

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	Unknown
Title of host publication	AAAI Fall Symposium Series
Editors	AAAI
Place of Publication	http://www.aaai.org/home.html
Publisher	AAAI
Pages	20-25
Volume	http://www.aaai.org/ocs/index.php/FSS/FSS10/schedConf/presentati
Publication status	Published - 1 Jan 2010
Event	AAAI Fall Symposium Series - Duration: 1 Jan 2010 → …

Conference

Conference	AAAI Fall Symposium Series
Period	1/01/10 → …

Access to Document

http://www.cs.cmu.edu/afs/cs/project/ita-proj-10/www/pia-2010/about/about.html

Cite this

@inproceedings{b39be651769e4963896ef899bc9c4bab,

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.",

keywords = "Incremental Bayesian Network, Intention Recognition",

author = "{DI Group Author}",

note = "Proactive Assistant Agents (PAA 2010) workshop at AAAI Fall Symposium http://www.cs.cmu.edu/afs/cs/project/ita-proj-10/www/pia-2010/about/about.html; null ; Conference date: 01-01-2010",

year = "2010",

month = jan,

day = "1",

language = "Unknown",

volume = "http://www.aaai.org/ocs/index.php/FSS/FSS10/schedConf/presentati",

pages = "20--25",

editor = "AAAI",

booktitle = "AAAI Fall Symposium Series",

publisher = "AAAI",

}

DI Group Author 2010, Anytime Intention Recognition via Incremental Bayesian Network Reconstruction. in AAAI (ed.), AAAI Fall Symposium Series. vol. http://www.aaai.org/ocs/index.php/FSS/FSS10/schedConf/presentati, AAAI, http://www.aaai.org/home.html, pp. 20-25, AAAI Fall Symposium Series, 1/01/10. <http://www.cs.cmu.edu/afs/cs/project/ita-proj-10/www/pia-2010/about/about.html>

TY - GEN

T1 - Anytime Intention Recognition via Incremental Bayesian Network Reconstruction

AU - DI Group Author

N1 - Proactive Assistant Agents (PAA 2010) workshop at AAAI Fall Symposium http://www.cs.cmu.edu/afs/cs/project/ita-proj-10/www/pia-2010/about/about.html

PY - 2010/1/1

Y1 - 2010/1/1

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.

KW - Incremental Bayesian Network

KW - Intention Recognition

M3 - Conference contribution

VL - http://www.aaai.org/ocs/index.php/FSS/FSS10/schedConf/presentati

SP - 20

EP - 25

BT - AAAI Fall Symposium Series

A2 - AAAI, null

PB - AAAI

CY - http://www.aaai.org/home.html

Y2 - 1 January 2010

ER -