A formal theory of plan recognition and its implementation
Reasoning about plans
A Bayesian model of plan recognition
Artificial Intelligence
Knowlege in action: logical foundations for specifying and implementing dynamical systems
Knowlege in action: logical foundations for specifying and implementing dynamical systems
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
Bayesian Models for Keyhole Plan Recognition in an Adventure Game
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Decision-Theoretic, High-Level Agent Programming in the Situation Calculus
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
On Procedure Recognition in the Situation Calculus
SCCC '02 Proceedings of the XII International Conference of the Chilean Computer Science Society
Game theoretic Golog under partial observability
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Corpus-based, statistical goal recognition
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Incorporating default inferences into plan recognition
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Theories of intentions in the framework of situation calculus
DALT'04 Proceedings of the Second international conference on Declarative Agent Languages and Technologies
Combining Cognitive with Computational Trust Reasoning
Trust in Agent Societies
Programming Organization-Aware Agents
ESAW '09 Proceedings of the 10th International Workshop on Engineering Societies in the Agents World X
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Robotics and Autonomous Systems
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A method to recognize agent's intentions is presented in a framework that combines the logic of Situation Calculus and Probability Theory. The method is restricted to contexts where the agent only performs procedures in a given library of procedures, and where the system that intends to recognize the agent's intentions has a complete knowledge of the actions performed by the agent. An original aspect is that the procedures are defined for human agents and not for artificial agents. The consequence is that the procedures may offer the possibility to do any kind of actions between two given actions, and they also may forbid to perform some specific actions. Then, the problem is different and more complex than the standard problem of plan recognition. To select the procedures that partially match the observations we consider the procedures that have the greatest estimated probability. This estimation is based on the application of Bayes' theorem and on specific heuristics. These heuristics depend on the history and not just on the last observation. A PROLOG prototype of the presented method has been implemented.