The Key idea is to represent explicitly and separately: a A. The limitations of MYCIN for application to teaching domain-independent diagnostic strategy in the form of meta-rules,

Theorem Proving Vision Robotics Information Processing Psychology Learning and Inductive Inference Planning and Related Problem-solving Techniques A. Natural Language Processing Ovnrview The most common way that human beings communicate Is by speaking or writing In one of the "natural" languages, like English, French, or Chinese. Computer programming languages, on the other hand, seem awkward to humans. These "artificial" languages are designed to have a rigid format, or syntax, so that a computer program reading and compiling code written In an artificial language can understand what the programmer means. In addition to being structurally simpler than natural languages, the artificial languages can express easily only those concepts that are important In programming: "Do this then do that," "See it such and such Is true," etc. The things that can be expressed In a language are referred to as the semantics of the language. The research on understanding natural language described in this section of the Handbook is concerned with programs that deal with the full range of meaning of languages like English.

EMYCIN is a programming system for writing knowledge-based consultation programs with a production-rule representation of knowledge. Several major components of the system, Including an explanation program and knowledge acquisition routines, are described. EMYCIN has been used to build consultation systems in several areas of medicine, as well as an engineering domain. These experiences lead to some general conclusions regarding the potential applicability of EMYCIN to new domains. Keywords: knowledge-based systems, production rules, knowledge representation, automated consultant.

Report 77-11 Structure Elucidation Based on Computer S Stanford -- KSL Analysis of High and Low Resolution Mass Spectral Data. A tremendous effort has been directed toward development of advanced instrumentation for mass spectrometrlc analysis. Advancements include everincreasing sensitivities and resolving powers, new ionization techniques, metastable ion probes of ion decomposition and structure and computer systems for rapid acquisition and reduction of data. We sometimes lose sight of the fact that these developments are designed to provide information about chemical and biochemic,a1 structures at greater TeTTE--aTrin greater detail than previously available. The ultimate goal in most research in mass spectrometry is to provide powerful tools for molecular structure elucidation, either directly, by exploitation of existing techniques, or indirectly by development of new techniques. Concurrently, several computer-based techniques designed to assist chemists in the analysis and interpretation of mass spectral data have been developed. Reprinted with permission from Smith, Dennis H. and Carhart, Raymond E. in "High Performance Mass Spectrometry: Chemical Applications," Michael Gross, Ed., in ACS SYMPOSIUM SERIES, No. 70; American Chemical Society: Washington, D.C., 1978, pp.325-347. Library search procedures (2) and their extensions (I) or attern recoviitioz programs (4) may provide clues to t e identity of the structure or be used to determine the structure uniquely. A computer program for analysis of spectra based on classspecific fragmentation rules, is available (5). There are several reasons or this lag: There is no formal theory.

In this paper we examine the selection.

A Glossary of Terms Used in Protein Crystallography.. - - - - A KNOWLEDGE-BASED SYSTEM FOR THE INTERPRETATION OF PROTEIN X-RAY CRYSTALLOGRAPHIC DATA ABSTRACT The broad goal of this project is to develop intelligent computational systems to infer the three-dimensional structures of proteins from x-ray crystallographic data. The computational systems under development use both formal and judgmental knowledge from experts to select appropriate procedures and to constrain the space of plausible protein structures. The hypothesis generating and testing procedures operate upon a variety of representations of the data, and work with several different descriptions of the structure being inferred. The system consists of a number of independent but cooperating knowledge sources which propose, augment and verify a sol.uticn to the problem as it is incrementally generated.

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.

The use of computers in chemistry and biochemistry has been widespread for many years, with machines performing many complex numerical calculations, e.g.

This chapter will discuss one example of how AI techniques are being integrated with, and extending, existing molecular biology sequence analysis methods. AI ideas of complex representations, pattern recognition, search, and machine learning have been applied to the task of inferring and recognizing structural patterns associated with molecular function. We wish to construct such patterns, and to recognize them in unknown molecules, based on information inferred solely from protein primary (amino acid) sequences.

The RNA secondary structure prediction problem (2 RNA) is a critical one in molecular biology. Secondary structure can be determined directly by x-ray diffraction, but this is difficult, slow, and expensive. Moreover, it is currently impossible to crystallize most RNAs. Mathematical models for prediction have therefore been developed and these have led to serial (and some parallel) computer algorithms, but these too are expensive in terms of computation time. The general solution has asymptotic running time exponential in N (i.e., proportional to 2 N), where N is the length of the RNA sequence. Serial approximation algorithms which employ heuristics and make strong assumptions are significantly faster, on the order of N 3 or N 4, but their predictive success rates are low -- often less than 40 percent -- and even these algorithms can run for days when processing very long (thousands of bases) RNA sequences. Neural network algorithms that perform a multiple constraint satisfaction search using a massively parallel network of simple processors may provide accurate and very fast solutions.