Reasoning by Analog with Applications to Heuristic Problem Solving: A Case Study,
Abstract
An information-processing approach to reasoning by analog is developed that promises to increase the efficiency of heuristic deductive problem-solving systems. When a deductive problem-solving system accesses a large set of axioms more than sufficient to solve a particular problem, it will often create many irrelevent deductions that are derived from the unnecessary axioms. These irrelevent deductions may be quite numerous and saturate the memory of the problem solver before it solves the problem. At the current state of the art, the most complex problems solved by automatic procedures require less than two dozen axioms to solve. A data base twice this size is sufficient to render any but the simplest problem unsolvable. In general, there is no decision procedure which can be used to restrict a data base to a set of necessary axioms. Here, any analogy with some previously solved problem and a new unsolved problem is used to restrict the data base to a small set of appropriate axioms. (Author)
Document Details
- Document Type
- Technical Report
- Publication Date
- Aug 01, 1971
- Accession Number
- AD0732457
Entities
People
- Robert Elliot Kling
Organizations
- Stanford University