A Qualitative Model for Temporal Reasoning with Incomplete Information

Proceedings of the AAAI Conference on Artificial Intelligence, 13

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 13

Handling Uncertainty

We clevelop a qualitative framework for temporal reasoning with incomplete information that features a modeling language based on rules and a semantics based on infinitesimal probabilities. The framework relates logical and probabilistical models, and accommodates in a natural way features that, have been found problematic in other models like non-determinism, action qualifications, parallel actions, and abduction to actions and fluents.