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Intention Recognition with Evolution Prospection and Causal Bayesian Networks
November 2009
int-recog-cbn
We describe a novel approach to tackle intention recognition, by combining dynamically configurable and situation-sensitive Causal Bayes Networks plus plan generation techniques. Given some situation, such networks enable the recognizing agent to come up with the most likely intentions of the intending agent and, in case of having to make a quick decision, focus on the important ones. The combination with plan generation provides a significant method to guide the recognition process with respect to hidden actions and unobservable effects, to confirm or disconfirm likely intentions. The absence of this articulation is a main drawback of the approaches using Bayes Networks solely, due to the combinatorial problem they encounter. We explore and exemplify its application, in the Elder Care context, of the ability to perform Intention Recognition and of wielding Evolution Prospection methods to help the Elder achieve its intentions.
In proceedings
Luís Moniz Pereira, Han The Anh
Zita Vale, Ana Madureira
Procs. Intl. Symp. on Computational Intelligence for Engineering Systems
CD-ROM
ISEP - Instituto Superior de Engenharia do Porto
Porto
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-
978-9728688-62-2
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Invited Talk http://centria.di.fct.unl.pt/~lmp/publications/online-papers/IR-EPA-CBNs.pdf
http://www.gecad.isep.ipp.pt/iscies09/intro.htm
Export formats
Luís Moniz Pereira and Han The Anh, Intention Recognition with Evolution Prospection and Causal Bayesian Networks, in: Zita Vale and Ana Madureira (eds), Procs. Intl. Symp. on Computational Intelligence for Engineering Systems, CD-ROM, ISEP - Instituto Superior de Engenharia do Porto, Porto, ISBN 978-9728688-62-2, (http://www.gecad.isep.ipp.pt/iscies09/intro.htm), Invited Talk http://centria.di.fct.unl.pt/~lmp/publications/online-papers/IR-EPA-CBNs.pdf, November 2009.
<a href="/people/members/view.php?code=6175f826202ff877fba2ad77784cb9cb" class="author">Luís Moniz Pereira</a> and <a href="/people/members/view.php?code=cdc7090d1f84f56c0671baa36e87bd77" class="author">Han The Anh</a>, <b>Intention Recognition with Evolution Prospection and Causal Bayesian Networks</b>, in: Zita Vale and Ana Madureira (eds), <u>Procs. Intl. Symp. on Computational Intelligence for Engineering Systems</u>, CD-ROM, ISEP - Instituto Superior de Engenharia do Porto, Porto, ISBN 978-9728688-62-2, (<a href="http://www.gecad.isep.ipp.pt/iscies09/intro.htm" target="_blank">url</a>), <i>Invited Talk http://centria.di.fct.unl.pt/~lmp/publications/online-papers/IR-EPA-CBNs.pdf</i>, November 2009.
@inproceedings {int-recog-cbn, author = {Lu\'{\i}s Moniz Pereira and Han The Anh}, editor = {Zita Vale and Ana Madureira}, title = {Intention Recognition with Evolution Prospection and Causal Bayesian Networks}, booktitle = {Procs. Intl. Symp. on Computational Intelligence for Engineering Systems}, series = {CD-ROM}, publisher = {ISEP - Instituto Superior de Engenharia do Porto}, address = {Porto}, isbn = {978-9728688-62-2}, note = {Invited Talk http://centria.di.fct.unl.pt/~lmp/publications/online-papers/IR-EPA-CBNs.pdf}, url = {http://www.gecad.isep.ipp.pt/iscies09/intro.htm}, abstract = {We describe a novel approach to tackle intention recognition, by combining dynamically configurable and situation-sensitive Causal Bayes Networks plus plan generation techniques. Given some situation, such networks enable the recognizing agent to come up with the most likely intentions of the intending agent and, in case of having to make a quick decision, focus on the important ones. The combination with plan generation provides a significant method to guide the recognition process with respect to hidden actions and unobservable effects, to confirm or disconfirm likely intentions. The absence of this articulation is a main drawback of the approaches using Bayes Networks solely, due to the combinatorial problem they encounter. We explore and exemplify its application, in the Elder Care context, of the ability to perform Intention Recognition and of wielding Evolution Prospection methods to help the Elder achieve its intentions.}, keywords = {Intention recognition, Elder Care, Causal Bayes Networks, Plan generation, Evolution Prospection, Preferences, Logic Programming}, month = {November}, year = {2009}, }
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