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Anytime Intention Recognition via Incremental Bayesian Network Reconstruction
November 2010
AIRIBNR
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.
In proceedings
Han The Anh, Luís Moniz Pereira
AAAI
2010 AAAI Fall Symposium
AAAI Fall Symposium Series
AAAI
http://www.aaai.org/home.html
http://www.aaai.org/ocs/index.php/FSS/FSS10/schedConf/presentati
20-25
-
-
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
http://www.cs.cmu.edu/afs/cs/project/ita-proj-10/www/pia-2010/about/about.html
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Han The Anh and Luís Moniz Pereira, Anytime Intention Recognition via Incremental Bayesian Network Reconstruction, in: AAAI (eds), 2010 AAAI Fall Symposium, AAAI Fall Symposium Series, AAAI, http://www.aaai.org/home.html, Vol. http://www.aaai.org/ocs/index.php/FSS/FSS10/schedConf/presentati, Pag. 20-25, (http://www.cs.cmu.edu/afs/cs/project/ita-proj-10/www/pia-2010/about/about.html), 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, November 2010.
<a href="/people/members/view.php?code=cdc7090d1f84f56c0671baa36e87bd77" class="author">Han The Anh</a> and <a href="/people/members/view.php?code=6175f826202ff877fba2ad77784cb9cb" class="author">Luís Moniz Pereira</a>, <b>Anytime Intention Recognition via Incremental Bayesian Network Reconstruction</b>, in: AAAI (eds), <u>2010 AAAI Fall Symposium</u>, AAAI Fall Symposium Series, AAAI, http://www.aaai.org/home.html, Vol. http://www.aaai.org/ocs/index.php/FSS/FSS10/schedConf/presentati, Pag. 20-25, (<a href="http://www.cs.cmu.edu/afs/cs/project/ita-proj-10/www/pia-2010/about/about.html" target="_blank">url</a>), <i>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</i>, November 2010.
@inproceedings {AIRIBNR, author = {Han The Anh and Lu\'{\i}s Moniz Pereira}, editor = {AAAI}, title = {Anytime Intention Recognition via Incremental Bayesian Network Reconstruction}, booktitle = {2010 AAAI Fall Symposium}, series = {AAAI Fall Symposium Series}, publisher = {AAAI}, address = {http://www.aaai.org/home.html}, volume = {http://www.aaai.org/ocs/index.php/FSS/FSS10/schedConf/presentati}, pages = {20-25}, 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}, url = {http://www.cs.cmu.edu/afs/cs/project/ita-proj-10/www/pia-2010/about/about.html}, 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 = {Intention Recognition, Incremental Bayesian Network}, month = {November}, year = {2010}, }
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