Intention Recognition with Evolution Prospection and Causal Bayesian Networks

L. M. Pereira, T. A. Han

Research output: Chapter in Book/Report/Conference proceedingChapter


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, i.e. solve one main issue of intention recognition. And, in case of having to make a quick decision, focus on the most important ones. Furthermore, the combination with plan generation provides a significant method to guide the recognition process with respect to hidden actions and unobservable effects, in order 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. This is achieved by means of an articulate use of a Causal Bayes Network to heuristically gauge probable general intention #x2013; combined with specific generation of plans involving preferences – for checking which such intentions are plausibly being carried out in the specific situation at hand, and suggesting actions to the Elder. The overall approach is formulated within one coherent and general logic programming framework and implemented system. The paper recaps required background and illustrates the approach via an extended application example. KeywordsIntention recognition-Elder Care-Causal Bayes Networks-Plan generation-Evolution Prospection-Preferences-Logic Programming
Original languageEnglish
Title of host publicationComputational Intelligence for Engineering Systems: Emergent Applications
Editors Ana Madureira, Judite Ferreira, Zita Vale
Number of pages33
ISBN (Electronic)9789400700932
Publication statusPublished - 30 Nov 2010


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