Finding a Consistent Scenario in a Probabilistic Temporal Interval Network

V. Ryabov (Finland) and A. Trudel (Canada)


Temporal reasoning, constraints, probabilistic reasoning.


A Probabilistic Temporal Interval Network is a constraint satisfaction problem where the nodes are temporal intervals and the edges are uncertain interval relations. Using probabilistic versions of inversion, composition, addition, and path consistency, we propose an approach to finding consistent scenarios using a backtracking algorithm. We also discuss heuristic methods for optimizing the performance of the backtracking algorithm. Finally, we show how to estimate the probability of a consistent scenario.

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