Edward H. Shortliffe Chapter 6 Details of the Revised Therapy Algorithm

This paper describes some work on automatically generating finite counterexamples in topology, and the use of counterexamples to speed up proof discovery in intermediate analysis, and gives some examples theorems where human provers are aided in proof discovery by the use of examples.

In recent years there has been a great deal of interest in the commercial applications of knowledge-based (KB) systems (commonly called expert systems). Interest in KB systems was spurred on by the development of programs that can solve complex tasks at an expert level.

Current subprojects include applications of the program Gerring.

THE STANFORD Artificial Intelligence Project, later reports to attempt to place each person, or report, in context.

The first computers were numerical calculators that did not appear to embody much intelligence at all. But before these machines were even designed.

Search 4. Methods for assigning credit and blame--obtaining a local Performance Standard

The following maxims represent a distillat:on of some of these intuitions and heuristics. They are not necessarily full of great insight. In many ways, they are similar to well-known guidelines for building other types of software. But we give them here with the hope that they will be helpful to future knowledge engineers.

The Most Important Gain: New Knowledge 18 10 Problems of Knowledge Engineering 19 10.1 The Lack of Adequate and Appropriate Hardware 19 10.2 Lack of Cumulation of Al Methods and Techniques 19 10.3 Shortage of Trained Knowledge Engineers 20 10.4 The Problem of Knowledge Acquisition 21 10.5 The Development Gap 21 11 Acknowledgments 22 1 1 Introduction: Symbolic Computation and Inference This paper will discuss the applied artificial intelligence work that is sometimes called "knowledge engineering". The work is based on computer programs that do symbolic manipulations and symbolic inference, not calculation. The programs I will discuss do essentially no numerical calculation. They discover qualitative lines-of-reasoning leading to solutions to problems stated symbolically.

This article is concerned with the general question of how to represent biological knowledge in computers such that it may be used in multiple problem solving tasks. In particular, I present a model of a bacterial gene regulation system that is used by a program that simulates gene regulation experiments, and by a second program that formulates hypotheses to account for errors in predicted experiment outcomes. This article focuses on the issues of representation and simulation; for more information on the hypothesis formation task see (Karp, 1989; Karp, 1990). The bacterial gene regulation system of interest is the tryptophan (trp) operon of E. coli (Yanofsky, 1981). The genes that it contains code for enzymes that synthesize the amino acid tryptophan.