Runtime plan adaptation in structured reactive controllers
AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
Learning Probabilistic Relational Models
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Anchoring Symbols to Sensor Data: Preliminary Report
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Interleaving temporal planning and execution in robotics domains
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Recovery planning for ambiguous cases in perceptual anchoring
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Perceptual anchoring of symbols for action
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Conditional progressive planning under uncertainty
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
BLOG: probabilistic models with unknown objects
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
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We present an approach for recovery from perceptual failures, or more precisely anchoring failures. Anchoring is the problem of connecting symbols representing objects to sensor data corresponding to the same objects. The approach is based on using planning, but our focus is not on the plan generation per se. We focus on the very important aspect of situation assessment and how it is carried out for recovering from anchoring failures. The proposed approach uses background knowledge to create hypotheses about world states and handles uncertainty in terms of probabilistic belief states. This work is relevant both from the perspective of developing the anchoring framework, and as a study in plan-based recovery from epistemic failures in mobile robots. Experiments on a mobile robot are shown to validate the applicability of the proposed approach.