Reasoning about unpredicted change and explicit time
This addresses the challenge of reasoning about unpredicted changes in dynamic systems, but it appears incremental as it builds on existing model-based diagnosis frameworks.
The paper tackles the problem of explaining time-stamped observations by identifying minimal sets of simple events called surprises, which are changes in truth values of fluents, and proposes a probabilistic approach to minimize these surprises.
Reasoning about unpredicted change consists in explaining observations by events; we propose here an approach for explaining time-stamped observations by surprises, which are simple events consisting in the change of the truth value of a fluent. A framework for dealing with surprises is defined. Minimal sets of surprises are provided together with time intervals where each surprise has occurred, and they are characterized from a model-based diagnosis point of view. Then, a probabilistic approach of surprise minimisation is proposed.