Reasoning about change: time and causation from the standpoint of artificial intelligence
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 of concern is that of predicting the future. In this context, two problems are identified, named the qualification problem and the extended prediction problem which subsume the infamous frame problem. The solution offered to those problems is counched in a logical framework. First introduced are two related logics of time intervals; next, a somewhat new, and very simple,approach to nonmonotonic logs. A nonmonotonic modal logic is then constructed, which combines elements of the interval logic and the modal logic of knowledge. It is shown that this logic, called the 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 is identified, called causal theories, about which reasoning can be carried out very efficiently. This in turn suggests a new theory of causation and of its central role in common-sense reasoning.
- Research Organization:
- Yale Univ., New Haven, CT (USA)
- OSTI ID:
- 6312212
- Resource Relation:
- Other Information: Thesis (Ph. D.)
- Country of Publication:
- United States
- Language:
- English
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