The problem of choosing the representation,inference rule,and strategy

This article is the nineteenth of a series of articles discussing various open research problems in automated reasoning. The problem proposed for research asks one to find guidelines for simultaneously choosing the most effective representation, inference rule, and strategy. Since the three areas are tightly coupled, formulation of such guidelines requires understanding the precise nature of this coupling. For evaluating a proposed solution to this research problem, we suggest possible test problems from group theory, Gödel's finite axiomatization of set theory, and program verification.This work was supported by the Applied Mathematical Sciences subprogram of the Office of Energy Research, U.S. Department of Energy, under Contract W-31-109-Eng-38.     

An autonomous multistrategy theorem proving system using knowledge-based techniques

Most general-purpose theorem-proving systems have weak search control. There is no alternative to the use of a large number of heuristics or strategies for search guidance. Choosing appropriate strategies for solving a given problem may require the knowledge of different strategies and may involve a lot of painstaking trial-and-error. To encourage the widespread use of computer reasoning systems, it is important that a theorem prover be usable by those with little knowledge of problem-solving strategies, and that a theorem prover be able to select good strategies for the user. An autonomous multistrategy theorem-proving system is developed, using knowledge-based techniques, to entirely free the user from the necessity of understanding the system or the merits of different strategies. All the user has to do is input his or her problem in first-order logic, and the system solves the problem efficiently for him or her without any manual intervention. The system embodies much of expert knowledge about how to solve problems. The knowledge is represented as metarules in knowledge base which guide a hyperlinking theorem prover to solve problems automatically and efficiently.     

Fuzzy evolutionary computing

 This paper elaborates on a new paradigm of computing embracing fuzzy sets and evolutionary methods (specially genetic algorithms). We discuss conceptual and algorithmic enhancements to the individual methods. Fuzzy sets are geared toward granular information processing. Evolutionary computing are population-based optimization methods. In this way, as being components of any hybrid structure, they naturally complement each other. The study reveals a number of representative symbiotic links between fuzzy and genetic computing and provides with relevant illustrative examples.     

Failure detection processes by an expert system and hybrid pattern recognition

This paper describes the architecture of a failure diagnosis system, as used in automatic testing, automatic imaging inspection, and specific failure detection tasks in electronics. A new knowledge representation scheme is also given, in relation to hybrid pattern recognition rules. Current work on building a knowledge base of diagnostic metarules is mentioned.