In this paper, 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 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.
|Name||Lecture Notes in Computer Science|
|Conference||Conference on Artificial Intelligence 2009|
|Abbreviated title||EPIA 2009|
|Period||12/10/09 → 15/10/09|