There has been a good deal of discussion recently about the possibility of standardizing knowledge representation efforts, including the development of an interlingua, or knowledge interchange format (KIF), that would allow developers of declarative knowledge to share their results with other AI researchers. In this article, I examine the practicality of this idea. I present some philosophical arguments against it, describe a straw-man KIF, and suggest specific experiments that would help explore these issues.

In my Ph.D. dissertation (Bhansali 1991), I develop an integrated knowledge-based framework for efficiently synthesizing programs by bringing together ideas from the fields of software engineering (software reuse, domain modeling) and AI (hierarchical planning, analogical reasoning). Based on this framework, I constructed a prototype system, APU, that can synthesize UNIX shell scripts from a high-level specification of problems typically encountered by novice shell programmers. An empirical evaluation of the system's performance points to certain criteria that determine the feasibility of the derivational analogy approach in the automatic programming domain when the cost of detecting analogies and recovering from wrong analogs is considered.

A five-year project, it is aimed at promoting cooperation among research centers, developing state-of-the art training and teaching programs, and demonstrating KBCS solutions to selected socioeconomic problems.

Engineering and scientific education condition us to expect everything, including intelligence, to have a simple, compact explanation. Today, some researchers who seek a simple, compact explanation hope that systems modeled on neural nets or some other connectionist idea will quickly overtake more traditional systems based on symbol manipulation. Others believe that symbol manipulation, with a history that goes back millennia, remains the only viable approach. AI is not like circuit theory and electromagnetism.

It is consistent with much that psychologists have observed in the natural problem solving people do. Psychologists have also observed, however, that people have several problems in doing analogical or case-based reasoning. I present case-based decision aiding as a methodology for building systems in which people and machines work together to solve problems. The case-based decision-aiding system augments the person's memory by providing cases (analogs) for a person to use in solving a problem.

Review of Artificial Intelligence, Simulation, and Modeling

Review of Expert Systems in Business: A Practical Approach

Review of Genetic Algorithms in Search, Optimization, and Machine Learning

In solving a new problem, we rely on past episodes. It assumes a memory model for representing, indexing, and organizing past cases and a process model for retrieving and modifying old cases and assimilating new ones. The research issues for case-based reasoning include the representation of episodic knowledge, memory organization, indexing, case modification, and learning. In this article, I review the history of case-based reasoning, including research conducted at the Yale AI Project and elsewhere.