Abstract
Reasoning about the behavior of real-world systems and processes faces problems such as repeating events or subprocesses, evolving component behaviors, and indefinite time horizons. To date, existing representations of time and uncertainty have been unable to fully address such requirements. They often tradeoff assumptions of available knowledge against richness of representing time and semantics for uncertainty. This has made representations either overly simplistic or cumbersome to model with, or both. In this paper, we present a new theoretical representation for reasoning about time and uncertainty extending our ability to better address real-world systems. Called cost-based temporal reasoning, we believe it is simple to understand, is rigorously defined, and allows for strong semantics of time and uncertainty. This paper formally details the definitions and proofs about the capabilities of cost-based temporal reasoning.
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