Excerpt from A Logical Framework for Solid Object Physics

The second difficulty is that a useful AI system must degrade well under limitations; it must be able to derive useful partial results from incomplete and impi problem specifications. An AI system must be able to produce a crude analysis even in where exact information as to object shapes or parameters is unavailable or unspecified the middle of a design process) or too complex for effective use. An AI system must alt able to reason generically about classes of varying problems. For example, if we bu chicken fence, we want to reason that no chicken, within the range of chicken shapes sizes, can go through the fence; we do not want to repeat the calculation for each indiv chicken. In this kind of qualitative reasoning, human common sense far excels convent computational techniques. Our task is to create an AI system with the same ability to common sense to these problems.

This paper presents a logical framework for qualitative reasoning about solid ob, We will present the structure of a first order language L in which the physics of solid of can be described; we will define the semantics of L in terms of a formal model; and we demonstrate the usefulness of L by showing that interesting problems can be 5( qualitatively by inference from plausible axioms expressed in L. The language L is l more expressive and supports much richer inferences than any previous represent scheme in this domain.

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