Reasoning from Incomplete Knowledge in a Procedural Deduction System.
MASSACHUSETTS INST OF TECH CAMBRIDGE ARTIFICIAL INTELLIGENCE LAB
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Procedural deduction languages, such as PLANNER, have been valuable tools for building models involving the notion of an explicit model of a problem situation. This report explores methods of increasing the ability of procedural deduction systems to deal with incomplete knowledge. The report 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.
- Computer Programming and Software