Summary of Ph.D. dissertation, Computer Science Dept., Stanford University (1979)."TEIRESIAS is a program designed to provide assistance on the task of building knowledge-based systems. It facilitates the interactive transfer of knowledge from a human expert to the system, in a high level dialog conducted in a restricted subset of natural language. This paper explores an example of TEIRESIAS in operation and demonstrates how it guides the acquisition of new inference rules. The concept of meta-level knowledge is described and illustrations given of its utility in knowledge acquisition and its contribution to the more general issues of creating an intelligent program."Also in:Readings in Artificial Intelligence, ed. Webber, Bonnie Lynn and Nils J. Nilsson, Palo Alto, CA: Tioga Publishing Co., 1981.Orig. in IJCAI-77, vol.1, pp. 321 ff. Preprint in Stanford HPP Report #HPP-77-9.See also: Artificial Intelligence, 12[#2]:409-427. Readings in Artificial Intelligence, ed. Webber, Bonnie Lynn and Nils J. Nilsson, Palo Alto, CA: Tioga Publishing Co., 1981

A program called "AM" is described which carries on simple mathematics research,defining and studying new concepts under the guidance of a large body ofheuristic rules. The 250 heuristics communicate via an agenda mechanism, aglobal priority queue of small tasks for the program to perform, and reasons whyeach task is plausible (for example, "Find generalizations of 'primes', because'primes' turned out to be so useful a concept"). Each concept is represented asan active, structured knowledge module. One hundred very incomplete modulesare initially supplied, each one corresponding to an elementary set-theoreticconcept (for example, union). This provides a definite but immense space whichAM begins to explore. In one hour, AM rediscovers hundreds of common concepts(including singleton sets, natural numbers, arithmetic) and theorems (for example,unique factorization).Summary of Ph.D. dissertation.Hayes, J.E., D. Michie, and L. I. Mikulich (Eds.), Machine Intelligence 9, Ellis Horwood.

"Rule-based inference systems allow judgmental knowledge about a specific problem domain to be represented as a collection of discrete rules. Each rule states that if certain premises are known, then certain conclusions can be inferred. An important design issue concerns the representational form for the premises and conclusions of the rules. We describe a rule-based system that uses a partitioned semantic network representation for the premises and conclusions." In D. A. Waterman and Frederick Hayes-Roth. 1978. Pattern-Directed Inference Systems. Academic Press, Inc., Orlando, FL, USA. pp. 203-221.

SU/X and SU/P are knowledge-based programs which employ pattern-invoked inference methods. Both tasks are concerned with the interpretation of large quantities of digitized signal data. The task of SU/X is to understand "continuous signals", that is, signals which persist over time. The task of SU/P is to interpret protein x-ray crystallographic data. Some features of the design are: (1) incremental interpretation of data employing many different pattern-invoked sources of knowledge, (2) production rule representation of knowledge, including high level strategy knowledge, (3) "opportunistic" hypothesis formation using both data-driven and model-driven techniques within a general hypothesize-and-test paradigm; and (4) multilevel representation of the solution hypothesis.

Expert system technology has been successfully applied to many practical problems, but. In this paper we discuss some of the problems confronting computer vision and present an approach to the development of general knoledge-based vision systems. The primary mechanism is a rule-based approach for the generation of initial object hypotheses, which allow focus of attention strategies. The rule set, applied to the attributes of the lines, regions, and surfaces in an intermediate symbolic representation, is constructed interactively with visual feedback to the user. Simples rules are defined as ranges over a feature value which are converted to a vote for an object label; complex rules are constructed via a functional combination of the output from the simple rules.

We present a rule-based system for computer-aided circuit analysis. The set of rules, called EL, is written in a rule language called ARS. Rules are implemented by ARS as pattern-directed invocation demons monitoring an associative data base. Deductions are performed in an antecedent manner, giving EL's analysis a catch-as-catch-can flavour suggestive of the behavior of expert circuit analyzers. We call this style of circuit analysis propagation of constraints.

See also: Stanford Heuristic Programming Project Memo HPP-77-25Proc. IJCAI-77: Fifth International Joint Conference on Artificial Intelligence, pp 1014-1029

"We define the concept of meta-level Knowledge, and illustrate it by briefly reviewing four examples that have been described in detail elsewhere. The examples include applications of the idea to tasks such as transfer of expertise from a domain expert to a program, and the maintenance and use of large Knowledge bases. We explore common themes that arise from these examples, and examine broader implications of the idea, in particular its impact on the design and construction of large programs."IJCAI 5, 920-927

Computer-Based Medical Consultations: MYCIN focuses on MYCIN, a novel computer-based expert system designed to assist physicians with clinical decisions concerning the selection of appropriate therapy for patients with infections. It discusses medical computing, artificial intelligence, and the clinical problem areas for which the MYCIN program is designed, and it describes in detail how the MYCIN program helps physicians in making decisions. Comprised of seven chapters, this volume begins with an overview of MYCIN and the criteria used in its design. The book also explores MYCIN'S ability to answer questions with respect to its knowledge base and the details of a specific consultation, evaluation and future extensions of the MYCIN system, the limitations and accomplishments of MYCIN, and its contributions in artificial intelligence and computer-based medical decision making. This book is a valuable source of information for computer scientists and members of the medical community.

This text is a description of a computer-based system designed to assist physicians with clinical decision-making. This system, termed MYCIN, utilizes computer techniques derived principally from the subfield of computer science known as artificial intelligence (AI). MYCIN's task is to assist with the decisions involved in the selection of appropriate therapy for patients with infections.

MYCIN contains considerable medical expertise and is also a novel application of computing technology. Thus, this text is addressed both to members of the medical community, who may have limited computer science backgrounds, and to computer scientists with limited knowledge of medical computing and clinical medicine. Some sections of the text may be of greater interest to one community than to the other. A guide to the text follows so that you may select those portions most pertinent to your particular interests and background.

The complete book in a single file.