Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Tabling for non-monotonic programming
Annals of Mathematics and Artificial Intelligence
JELIA '02 Proceedings of the European Conference on Logics in Artificial Intelligence
Smodels - An Implementation of the Stable Model and Well-Founded Semantics for Normal LP
LPNMR '97 Proceedings of the 4th International Conference on Logic Programming and Nonmonotonic Reasoning
A logic programming approach to knowledge-state planning, II: the DLVk system
Artificial Intelligence
Journal of Intelligent and Robotic Systems
Theory and Practice of Logic Programming
Probabilistic reasoning with answer sets
Theory and Practice of Logic Programming
Elder care via intention recognition and evolution prospection
INAP'09 Proceedings of the 18th international conference on Applications of declarative programming and knowledge management
Intention-based decision making with evolution prospection
EPIA'11 Proceedings of the 15th Portugese conference on Progress in artificial intelligence
Annals of Mathematics and Artificial Intelligence
Corpus-based intention recognition in cooperation dilemmas
Artificial Life
State-of-the-art of intention recognition and its use in decision making
AI Communications
Intelligent Decision Technologies
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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.