Expert System Applications in Chemistry

257 Pages·1989·4.597 MB·English

by  Bruce A. Hohne and Thomas H. Pierce (Eds.)

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In Expert System Applications in Chemistry; Hohne, B., el al.;
ACS Symposium Series; American Chemical Society: Washington, DC, 1989.
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In Expert System Applications in Chemistry; Hohne, B., el al.;
ACS Symposium Series; American Chemical Society: Washington, DC, 1989.
408
ACS SYMPOSIUM SERIES
Expert System Applications
in Chemistry
Bruce A. Hohne, EDITOR
Rohm and Haas Company
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8.f Thomas H. Pierce, EDITOR
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10. Developed from a symposium sponsored
doi: by the Division of Computers in Chemistry
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8 at the 196th National Meeting
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American Chemical Society, Washington, DC 1989
In Expert System Applications in Chemistry; Hohne, B., el al.;
ACS Symposium Series; American Chemical Society: Washington, DC, 1989.
Library of Congress Cataloging-in-Publication Data
Expert system applications in chemistry
Bruce A. Hohne, editor, Thomas H. Pierce, editor
p. cm.—(ACS Symposium Series, 0097-6156; 365).
1
0
0 Developed from a symposium sponsored by the Division
w of Computers in Chemistry at the 196nd National Meeting
8.f of the American Chemical Society, Los Angeles, California,
40 September 25-30, 1988.
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89- Includes bibliographical references.
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1
k- ISBN 0-8412-1681-9
b 1. Chemistry—Data processing—Congresses. 2. Expert
1/ systems—Congresses.
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0.1 I. Hohne, Bruce A., 1954- . 1. II. Pierce, Thomas
1 H., 1952- . 2. III. American Chemical Society. Division
oi: of Computers in Chemistry. IV. American Chemical
d Society. Meeting (196th: 1988: Los Angeles, Calif.).
9 | V. Series
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19 QD39.3.E46E96 1989
1, 540'.285'633—
ber dc20 89-35271 CIP
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D Copyright © 1989
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ati American Chemical Society
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In Expert System Applications in Chemistry; Hohne, B., el al.;
ACS Symposium Series; American Chemical Society: Washington, DC, 1989.
ACS Symposium Series
M. Joan Comstock, Series Editor
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In Expert System Applications in Chemistry; Hohne, B., el al.;
ACS Symposium Series; American Chemical Society: Washington, DC, 1989.
Foreword
The ACS SYMPOSIUM SERIES was founded in 1974 to provide a
medium for publishing symposia quickly in book form. The
format of the Series parallels that of the continuing ADVANCES
IN CHEMISTRY SERIES except that, in order to save time, the
papers are not typeset but are reproduced as they are submitted
1 by the authors in camera-ready form. Papers are reviewed under
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w0 the supervision of the Editors with the assistance of the Series
8.f Advisory Board and are selected to maintain the integrity of the
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0 symposia; however, verbatim reproductions of previously pub
9-
98 lished papers are not accepted. Both reviews and reports of
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k- research are acceptable, because symposia may embrace both
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In Expert System Applications in Chemistry; Hohne, B., el al.;
ACS Symposium Series; American Chemical Society: Washington, DC, 1989.
Preface
THIS IS THE SECOND TIME we have organized a symposium on artificial
intelligence (AI) and prepared a book based on the symposium. Since
our first effort (Artificial Intelligence Applications in Chemistry, ACS
1 Symposium Series 306), the field has changed dramatically, in large part
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pr0 because of the advent of commercially available PC-based expert system
8. software. Expert system development has moved out of the hands of the
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0 AI gurus and into the hands of practicing chemists. This is a positive
9-
98 step for the field of chemistry. There no longer appears to be a danger of
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k- AI being dismissed as a fad or held back through lack of interest from
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21/ the chemical community. Now, the issues have become cost effectiveness,
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0.1 intellectual property protection, and application selection.
1
oi: The main thrust of this book is to present examples of how expert
9 | d systems can solve chemical problems. To make the book more useful to
8 novices in the field of artificial intelligence, we have written a brief
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1
1, introductory chapter explaining expert systems. The glossary at the end
er of the introduction should be of help to novices and experts alike. We
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e have also included highlights of a panel discussion held at the
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Se symposium. The panel was posed the question, "Can knowledge bases be
ate: made generally available in a useful format?" Unfortunately, more issues
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n were raised than questions answered.
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ati We wish to thank the authors who contributed their time and ideas
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bli to the symposium and the book. In addition, we would like to thank the
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providing us the opportunity to publish this book. Finally, we
acknowledge the encouragement and support we received from our
management at Rohm and Haas Company.
BRUCE A. HOHNE THOMAS H. PIERCE
Rohm and Haas Company Rohm and Haas Company
Spring House, PA 19477 Bristol, PA 19007
July 5, 1989
xi
In Expert System Applications in Chemistry; Hohne, B., el al.;
ACS Symposium Series; American Chemical Society: Washington, DC, 1989.
Chapter 1
Introduction
Expert System Applications in Chemistry
Bruce A Hohne1 and Thomas H. Pierce2
1Rohm and Haas Company, Spring House, PA 19477
1 2Rohm and Haas Company, Bristol, PA 19007
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0
h
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8. This symposium series volume is the second of its kind (1). Both
0
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0 symposia were organized with several purposes in mind. The first,
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8 and most general, is simply to expose the chemical community to
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k-1 expert systems. Expert systems should be of interest to people in a
1/b wide variety of fields of chemistry. The second is to educate chemists
02 in the capabilities of expert systems; both what they can and can not
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0. do for them. Finally, by presenting a variety of applications, it was
1
oi: hoped that attendees would generate further new ideas for expert
89 | d osypsptoermtu napitpyl itcoa trieovnise. w Tthhee s percoognrde sssy mofp ossoimuem opfr tehseen wteodr kt hdee sacdridbietdi oinna l
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1 the first symposium.
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er The first symposium, presented at the fall meeting of the
mb American Chemical Society in 1985, was open to any type of artificial
pte intelligence application. The second symposium was restricted to
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S expert systems. This is only a minor restriction because most
ate: practical artificial intelligence applications to date have used expert
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n systems in one form or another. Below is a brief overview of expert
atio systems. More information on expert systems for chemistry can be
c found in reference 2. General expert system information can be
ubli found in references 3 and 4. Because artificial intelligence has
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developed its own vocabulary, a short glossary is also included.
Expert Systems
Expert systems are programs that attempt to solve problems in a way
similar to how a human expert would solve them. They incorporate
"rules of thumb" that experts in the field have developed through
years of experience. The problems attacked are not necessarily
procedural, they are often vague, complex, and can contain incomplete
or inexact information.
Expert systems contain three basic pieces: a knowledge base, an
inference engine, and a user interface. The knowledge base contains
the information which the program uses to reach decisions. A key
0097-6156/89/0408-0002$06.00/0
o 1989 American Chemical Society
In Expert System Applications in Chemistry; Hohne, B., el al.;
ACS Symposium Series; American Chemical Society: Washington, DC, 1989.
1. HOHNE & PIERCE Introduction: Expert System Applications in Chemistry 3
difference between expert systems and classical computer programs
is the fact that the knowledge is separated from the program. The
inference engine is the program that manipulates the knowledge base
to reach these decisions. The user interface allows the program and
chemist to communicate with each other in an effective manner.
These three pieces of an expert system are described below.
Knowledge Base. The knowledge base of an expert system is a
resource of information about a specific domain, or problem area.
The knowledge base is the most important part of the expert system,
and the most difficult to construct. The completeness and accuracy
of the knowledge base will determine how well the expert system will
perform solving problems. The scope of problems which can be solved
is completely determined by the scope of the knowledge base.
01 Knowledge must be encoded in such a way that it can be a)
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ch entered into the computer; b) manipulated by the inference engine;
08. c) understood by the expert. There are several common ways to
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9-0 encode the expert's knowledge, each with its own advantages and
8 disadvantages. The encoding scheme must match the underlying
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k- structure of the knowledge.
b
1/ The two most common methods of encoding knowledge are
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0 production rules and frames. Sometimes both methods are used.
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0. Production rules take the form: IF x is true THEN y is true. For
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oi: example:
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1, THEN the solution is acidic.
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at frame describes hierarchical dependencies between objects. In a
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ati objects beneath it in the hierarchy (children). In other words, children
blic inherit attributes from their parents. For example:
u (FRAME - Alcohol
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(SLOT - Reactions
(VALUE - "Oxidized by permanganate")
(VALUE - "..."))
(SLOT - Infrared Absorbances
(VALUE - 3600 cm1 - 3100 cm1)))
(FRAME - Ethanol
(SLOT - Chemical Class
(VALUE - Alcohol)))
Inference Engine. The inference engine is the central program which
manipulates the rules and facts in the knowledge base to reach
conclusions. The structure of the inference engine depends strongly
upon the type of knowledge base which the expert system incor-
In Expert System Applications in Chemistry; Hohne, B., el al.;
ACS Symposium Series; American Chemical Society: Washington, DC, 1989.
4 EXPERT SYSTEM APPLICATIONS IN CHEMISTRY
porates. Inherent in all of the program structures, however, is a basic
set of functions which expert systems perform. These functions are
described below using an example knowledge base which incorporates
production rules.
The inference engine may approach the problem from either the
top or the bottom, beginning with either facts or conclusions. If a
user begins with several hypotheses and wants to determine which,
if any, are correct, then the program should examine all the facts
using a goal-directed (also called goal-driven) approach. However, if
the user begins with a series of facts which are known to be true and
wants to determine what conclusions can be reached, the program
should use a data-directed (also called data-driven) approach.
A goal-directed expert system begins with a limited set of
possible hypotheses and attempts to prove the validity of each one.
01 This type of expert system uses a reverse-chaining (also called
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h backward-chaining) algorithm. The knowledge base is searched to
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8. find a rule which concludes the initial hypothesis. The IF-clauses
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8 search. The process continues until all of the remaining IF-clauses
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k-1 are known to be true (hypothesis is true) or until no more rules apply
1/b (hypothesis is false). This approach starts at the bottom (conclusions)
02 and works its way to the top (facts). An example of this mechanism
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1, 1 rules for one that concludes the pump is broken. It may find a rule
er like:
mb IF "there is no solvent flow"
pte "all tubing is properly connected"
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ate: "the solvent program module is functioning"
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ubli is no solvent flow". The process would continue until the conclusion
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"the pump is broken" is either proved or disproved. In this example,
the chemist would be asked to input information whenever an IF
clause was an observable symptom of an HPLC problem.
Data-directed expert systems begin with a list of the facts known
to be true, and see what conclusions can be drawn from those facts.
This type of expert system uses a forward-chaining mechanism. Each
rule in the knowledge base is tested to see if all of its IF clauses are
contained in the list of known facts. When such a rule is found, the
system adds the THEN-clauses from the rule to the list of known
facts. All the rules in the knowledge base are scanned repetitively
until no new facts can be concluded. An example of using forward-
chaining is illustrated by a structure elucidation problem based on an
IR spectrum:
In Expert System Applications in Chemistry; Hohne, B., el al.;
ACS Symposium Series; American Chemical Society: Washington, DC, 1989.