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Reasoning about Change: Time and Causation from the Standpoint of Artificial Intelligence

机译：变革的推理：从人工智能的角度看时间和因果关系

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Temporal reasoning is a central component of research in artificial intelligence as well as of our everyday reasoning. This dissertation investigates some problems that arise when one wishes to make temporal inferences that are both rigorous and efficient. The particular task with which we are concerned is that of predicting the future. In this context two problems are identified named the qualification problem an the extended prediction problem, which subsume the infamous frame problem. The solution to those problems which is offered is couched in a logical framework. First two related logics of time intervals are introduced. Next a somewhat new, and very simple, approach is introduced to nonmonotonic logics. Then construct a nonmonotonic modal logic which combines elements of the interval logic of chronological ignorance, solves the two problems mentioned above. Furthermore, while in general the logic of chronological ignorance is badly undecidable, a class of theories, called causal theories is identified, about which reasoning can carried out very efficiently. This in turn suggests a new theory of causation and of its central role in commonsense reasoning.

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