Reasoning from Incomplete Knowledge in a Procedural Deduction System

Title: en_US Reasoning from Incomplete Knowledge in a Procedural Deduction System
Creator: en_US Moore, Robert Carter
Date: 2004-10-20T20:05:41Z
Date Available: 2004-10-20T20:05:41Z
Date Issued: en_US 1975-12-01
Identifier: en_US AITR-347
uri: http://hdl.handle.net/1721.1/6898
Abstract: en_US One very useful idea in AI research has been the notion of an explicit model of a problem situation. Procedural deduction languages, such as PLANNER, have been valuable tools for building these models. But PLANNER and its relatives are very limited in their ability to describe situations which are only partially specified. This thesis explores methods of increasing the ability of procedural deduction systems to deal with incomplete knowledge. The thesis examines in detail, problems involving negation, implication, disjunction, quantification, and equality. Control structure issues and the problem of modelling change under incomplete knowledge are also considered. Extensive comparisons are also made with systems for mechanica theorem proving.
Extent: 10580006 bytes; 8308773 bytes
Format: application/postscript; application/pdf
Language: en_US
Relation: en_US AITR-347