Accession Number:

ADA172502

Title:

Controlling Inference.

Descriptive Note:

Doctoral thesis,

Corporate Author:

STANFORD UNIV CA DEPT OF COMPUTER SCIENCE

Personal Author(s):

Report Date:

1986-04-01

Pagination or Media Count:

197.0

Abstract:

Effective control of inference is a fundamental problem in Artificial Intelligence. Unguided inference leads to a combinatoral explosion of facts or subgoals for even simple domains. To overcome this problem, expert systems have used powerful domain-dependent control information in conjunction with syntactic domain-independent methods like depth-first backward chaining. While this is possible for some applications, it is not always feasible or appropriate for problem solvers that must solve a wide variety of different problems. In this dissertation I argue that a kind of semi-independent control is essential for problem solvers that must face a wide variety of different problems. Semi-independent control is based on the idea that there is underlying domain-independent rationale behind any good control decision. This rationale takes the form of simple utility theory applied to the expected cost and probability of success of different inference steps and strategies. These basic principles are domain-independent, but their application to any particular problem relies on global information about the nature and extent of facts and rules in the problem solvers database. Author

Subject Categories:

Distribution Statement:

APPROVED FOR PUBLIC RELEASE