Logic programs with classical negation
Logic programming
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
Reasoning agents in dynamic domains
Logic-based artificial intelligence
Knowledge Representation, Reasoning, and Declarative Problem Solving
Knowledge Representation, Reasoning, and Declarative Problem Solving
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
Probabilistic State-Dependent Grammars for Plan Recognition
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Fast hierarchical goal schema recognition
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Reasoning about intended actions
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Policy recognition in the abstract hidden Markov model
Journal of Artificial Intelligence Research
Fast and complete symbolic plan recognition
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Reasoning about the intentions of agents
Logic Programs, Norms and Action
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The following activity recognition problem is considered: a description of the action capabilities of an agent being observed is given. This includes the preconditions and effects of atomic actions and of the activities (sequences of actions) the agent may execute. Given this description and a set of propositions, called history, about action occurrences, intended actions and properties of the world all at various points in time, the problem is to complete the picture as much as possible and determine what has already happened, what the intentions of the agent are, and what may happen as a result of the agent acting on those intentions. We present a framework to solve these activity recognition problems based on a formal language for reasoning about actions that includes a notion of intended actions, and a corresponding formalization in answer set programming.