Abstract
There are numerous logical formalisms capable of drawing conclusions using default rules. Such systems, however, do not normally determine where the default rules come from; i.e , what it is that makes Birds fly a good rule, but Birds drive trucks a bad one. Generic sentences such as Birds fly are often used informally to describe default rules. I propose to take this characterization seriously, and claim that a default rule is adequate iff the corresponding generic sentence is true. Thus, if we know that Tweety is a bird, we may conclude by default that Tweety flies, just in case Birds fly is a true sentence. In this paper, a quantificational account of the semantics of generic sentences is presented. It is argued that a generic sentence is evaluated not in isolation, but with respect to a set of relevant alternatives. For example, Mammals bear live young is true because among mammals that bear live young, lay eggs, undergo mitosis, or engage in some alternative form of procreation, the majority bear live young. Since male mammals do not procreate in any form, they do not count. Some properties of alternatives are presented, and their interactions with the phenomena of focus and presupposition is investigated. It is shown how this account of generics can be used to characterize adequate default reasoning systems, and several desirable properties of such systems are proved. The problems of the automatic acquisition of rules from natural language are discussed. Because rules are often explicitly expressed as generics, it is argued that the interpretation of generic sentences plays a crucial role in this endeavor, and it is shown how the theory presented here can facilitate such interpretation.
Original language | English |
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Pages (from-to) | 506-533 |
Number of pages | 28 |
Journal | Computational Intelligence |
Volume | 13 |
Issue number | 4 |
DOIs | |
State | Published - 1 Jan 1997 |
Keywords
- Automatic knowledge acquisition
- Default reasoning
- Generics
- Semantics
ASJC Scopus subject areas
- Computational Mathematics
- Artificial Intelligence