computer m i e ~82 .
edited by JOHN W. MOORE Eastern Michigan University. Ypsilanti. MI 48197
The Application of Expert Systems in the General Chemistry Laboratory Frank A. Settle, Jr. Virginia Military Institute, Lexington, VA 24450 Artificial intelligence (AI) attempts to build computer svstems that nossess the characteristics associated with human intelligence such as communicating through language, learning, reasoning, and problem solving. While some researchers are using A1 to investigate human reasoning, the more praematic features of A1 nromammine embodied in expert systems have found applicat&ns in chemistry, geology, enpineerinp, and medicine. -~ . An expert system contains organized information covering a specific area of knowledge. The system functions as a consultant for the given area of knowledge and provides an explanation of reasoning upon request. Thus, these systems are useful in situations where human exnertise is not readilv ~~~~~~~~~accessible. Such systems are currently used to assist in the diagnosis of blood diseases and telenhone cable ~roblems. theselection of operating conditions for ultracentr;fuges and liquid chromatograohs. and in the desien of comouter svste& and very &e scale integrated hrcuits. he use-of artificial intelligence in the form of expert svstems can nrovide the practi&g scientist or technician with the inteilectual equivalent of a mechanical advantage solv- in problem . ing. The purpose of this paper is to describe the construction and use of expert systetkusing rommercially available softwure known as an expert system "shell". Two applications will be descrihvd. R simnle one. the identification of seven white substances and a more complicated one involving the qualitative analysis of six metal ions. ~
Journal of Chemical Education
Knowledge Base for a Slmple Experl System
Substance
Biphenyl, Cq2H,.
Baic acid. H38O3
Behavior an heatino in air melts at 70 'C
Sand. SIO.
melts. decomposes losing Water melts, decomposes loses water, color changes to yellowbrown melts above 1400 ' C
Lime. CaO
melts above 2500 ' C
Sugar. C12H22011
DBCMpnes lmO watw Bnd a blad solid melts 185-186 'C melts at 801 'C
Hypo. Na&O,.5H20
~
Expert Systems The most common type of expert system consists of a set of rules, known as a knowledge base, which contains expertise on a certain subject. A second component of an expert system is the inference engine or rules interpreter, which extracts the desired information from the knowledge base. Upon request, the system can provide the user with an explanation of how the answers were obtained. This involves displaying the sequence of rules used to arrive a t t h e answers and explaining the rationale behind each rule employed. The third and most visil~lepart of an expert system is the user interface. This interface consists of the software that first generates the questions necessary to obtain information from the user and then displavs answers and exnlanations. Theuser interfaceallows thk user to interactwiththe system through a dialogue, similar to a consultation with a human expert. This dialogue typically consists of an explanation of the problem by the user, suggestions of probable causes or solutions by the system, questions from the user concerning the system's reasoning and finally explanations by the system. 340
Table 1.
san. NaCl
Solubility In water Insoluble
Soluble Soluble
LIIIIIuS test of aqueous solution
No color change (Neutral) blue to red (Acidic) No color change (Neutral)
Insoluble
No c o l a change (Neutral) Sparingly Red to blue (Basic? Soluble No color change (Neutral) Soluble No color change (Neutral)
Expert System Shells An expert system shell' consists of an editor component and a run-time component. The editor allows information to be entered into the system by the instructor (knowledge engineer) either in the form of IF-THENrulesor in the form of examples. In the latter case, the program converts the examnles into rules automaticallv. The run-time software permits the student to use a compiled version of the system. Expert svstem shells are commerriallv available for nersonafcom~utersa t prices ranging from $200 to $4000.?he use of a shell is much more convenient for constructine expert systems than the use of either PROLOG or LISP, the conventional lanmages of artificial intellieence. It should be noted, however, i h a i the use of a shell fo; system development on a personal computer can limit both the functions and size of the resulting expert systems.
' Williamson, M. PC Prod 1985 (December).
(the author in this case). Each rule may also contain a note and a reference. This information is not used by the inference engine of the system hut appears with the rule when an explanation is requested by the user. An important characteristic of the EXSYS shell should be mentioned at this point. Assigning a probability value of 01 10 to a substance in a given rule will override all values associated with this substance in the other rules. This means that asingle value of 0110 will always give an averagevalue of 0110 regardless of the other probability values associated with this suhstance. In like manner, a single value of 10110 for a substance in any rule will always result in an average valueof l0llOfor the substance, thus the scale of 1/10 to 9/10 "..ho+.nral "-"o-'.bb". wed in the current problem. The information contained in Table 1 bas been used to Three additional rules each containing conditions, probaconstruct the knowledge base of the simple expert system SUBID (for SUB^^^^^ ~ ~ ~ ~ ~ ~h~ i system f i ~ is used ~ ~ i ~bilities, ~ ) and , notes are required to cover all the possible results of the solubility test. When these rules are entered into by students to check the validity of their identifications. the expert system computer program, they generate the folObservations associated with each test are entered into the lowing multiple-chOice question: system from the keyboard in response toquestions displayed on the screen. The svstem then informs the students of the SUBSTANCE X probable identity of 'the suhstance. Based on these answers, 1) is water soluble. 2) is water insoluble. students can verifv their identifications or, in cases where 3) is partially soluble in water. the system indicates no positive identification is possible, 4) was not tested for water solubility they can use the explanation facility to investigate the source of error. The information in Table 1concerning the behavior of the suhstances uDon heatine and the reactions of their aaueous Conversion of Informallon lnlo a Knowledge Base solutions wiih litmus paper is used to construct l i more Two different expert systems were developed for this exrules. These 11 rules are the source of two additional quesperiment. The first was constructed using EXSYS2 and the tions for the user: second using KDS3. EXSYS is a rule-based shell in which SUBSTANCE X, UPON HEATING the author (knowledee eneineer) uses the editor Droeram to 1) melts at a relatively low temperature (below the boiling develop theset of^^-^^^^ ml& comprising thekniwledge point of water). base. In contrast, KDS is an example-based shell that 2) melts, decomposes with the loss of water, and retains its "learns" from example situations provided by the author. original color. When the EXSYS editor program is used to transform the 3) melts, decomposes with the loss of water and turns to a data in Table 1into a knowledge base, the first rule covering yellow, brown color. solubility is: 4) does not melt. 5) decompaaes into water and a black solid. RULE #1 6) THE TEST WAS NOT PERFORMED. IF: SUBSTANCE X is water soluble. THEN: A DROP OF WATER WHICH HAS HEEN I N CONTACT WITH (not soluble) BIPHENn -Probability = 1/10 SINSTANCE X BORIC ACID-Probability = 9/10 (soluble) 1) turns red litmus paper blue. (soluble) HYPO -Probability = 9/10 2) turns blue litmus oaoer . . red. (not soluble) SAND -Probability = 1/10 3) does not change the color of litmus paper (partially soluble) LIME -Probability = 1/10 4 ) was not tested with litmut paper SUGAR -Probability = 9/10 (soluble) The SUBID EXSYS knowledge base isnow comdete with (soluble) BORIC ACID-Probabilitv = 9/10 NOTE: 15 rules. The next step is to test the system ti see if it Four of the substances are soluble in water, boric acid, hypo, correctly identifies each substance when its properties are sugar and salt. entered in response to three multiple-choice questions. Upon completion of this testing, the system is ready for ~h~ rule was construhd as follows, onestarts with a general use. qualifier, in this case, "SUBSTANCE X". The possible valIn developing a knowledge base with KDS, the author ues that can be assigned to the qualifier are considered next. enters a list of cases (the seven white substances) and then If the value "is water soluble" is assigned to the qualifier develops conditions (results of the three tests) that are "SUBSTANCE X , the condition (qualifier plus a value) unique for each substance. The KDS editor program is easy "SUBSTANCE X is water soluble" results. The EXSYS to learn and use. Conditions should he entered in order of editor program allows information to be entered in this mantheir relative importance to the analysis. In this example the ner. The condition thus forms the I F portion of the rule. results of heating the substance were judged to be the most The THEN portion of the rule lists all seven substances useful and thus were entered as the initial condition, "Your and oermits the author to assien a "nrobahilitv" value to analyte melted or decomposed when heated", with the foleachsuhstance for the given co&ition: The sca~kof "prohalowing responses for each case (substance): bilities" (looselv defined in the statistical sense) used with this particular system ranges from 1/10, highly improbable, case O-biphenyl -True to 9/10, highly probable. A probability value was assigned to case 1-lime -False each substance for the condition stated in the I F portion of case 2-sand -False the rule. These values reflect the experience of the expert case 3-alt -False case P b o r i c acid -True case 5--hypo -True ldenllticallon at Seven Whlle .~ .-~~~~~~- Substances An experiment involving the identification of seven unknown substances plus an expert system containing data on these substances was used to introduce students to the expert systems. The laboratory work involves the identification of seven unknown substances based upon their chemical and physical properties. The student is given seven unknowns. The following properties are determined for each unknown: (1) behavior upon heating, (2) solubility in water, and (3) the acidity, basicity, or neutrality of any resulting solutions. The student attempts to identify each unknown by consulting Table 1, which lists the properties of the seven ~~
~~
~
~
~~
~~
~
~
Pa.- - L r --- . , r c n-. -B
EXSYS, Exsys Inc.. P.O. Box 75158. Albuquerque. NM 87194. KDS (KnowledgeDelivery System),KDS Corp.. 934 Hunter Road,
Willmette. IL 90091.
-T.IIP ... .
Figure 1shows the screen containing the initial statement, helpful hints, and prompts. The author may place any inforVolume 64 Number 4 Am11 1987
341
Table 2.
KDS Matrix for Seven White Substances Condition'
Case
0
1
2
3
4
biphenyl lime sand salt boric acid
T
F F F F T T T
F F F F F T F
F F F F F F T
F F F T T T T
hypo
F T T
sugar
T
o Flgure 1. Initial KVS scream
mation that will be useful to the user on the right side of the disnlav. The nossible student resvonses to the statement may de True,'False, or Don't care ?meaning test not run or don't know). The KDS knowledge base for the seven substances expkriment is representea as a matrix (Table 2). The author enters the conditions and responses necessary to identify a given substance (case). Th; editor program vrovides manv useful vrompts and keeps track of any redundant information. It is not necessary to enter all six conditions to identify a given unknown. For example, in the case of hiphenvl, rrspmses to only the first twoconditions (0 and 1 ) arc necessary to identify this suhstance. If the responses to conditions 2 throueh 7 for hinhenvl . . are not entered into t h e knowledge hase by the author, they will appear as "-" (don't care) in the matrix. Thus the system needs to have definite answers to the first two conditions to determine the presence of biphenyl. ~~
~~~
~~
~
~
-
~
Use of the Svstems
Students experienced little difficulty in entering the results of their tests into the exvert systems. Both EXSYS and KDS provide useful promp& for the user. If a student ohserved the following results for an unknown white suhstance, X: (1) heating decomposes X intoa black solid and water, (2) X is soluble in water, (3) X does not change the color of litmus, the responses (underlined) to the three multiple choice questions should he I. SUBSTANCE X iesnlublc in w a ~ e r . 2. SIIRSTANCE X. UI'OS HKA'I'ING derorn~osesinto _water and a-black solid.
:I.
AiiH01' Ob' \\.A'I'EK WHICH HAS BEEN IN ONTACT
WITH UNKNOWN X does not chanee the color of litmue
-.
These responses form a "temporary knowledge base" for the unknown suhstance and invoke the appropriate rules in the knowledge hase. For example, rule # I returns the following prohahilities for the seven substances: biphenyl (11 lo), lime (1/10), sand (1/10), salt (9/10), boric acid (9/10), hypo (9/10), and sugar (9110). When the probabilities from the rules associated with the results entered for the other two tests are entered the average probabilities are displayed for each possible suhstance: biphenyl lime sand salt boric acid hypo sugar
4/10 1/10 4/10 6/10 4/10 6/10 9/10
These results indicate substance X is sugar. Hypo is the next most likely choice. This expert system program re-
342
Journal of Chemical Education
F F
1 2 3 4 5 6
5
6
F
T T F
F T F F F F
F
F F F
aC@ndltians: your analyte melted a decompose6 when heated. Your analyta decampoer and iooes water when heated. when heated, me anawe tums a ysliow-brown color. neat dernmpses the anslyte into a black sub3latance and H,O. YO", ana1yte is completely water Mlvbls. Your analyte Is Insoluble in wafer. An aqueous solution of the analyte turns red litmus blue.
quires the average probability value to be 9/10 for positive identification. If, after entering the experimental obsewations from the three tests for a siven unknown substance, none of the average values for t& seven substances is 9/10, then the lahorat o w tests for the unknown must be repeated. The unknown is most likely to be one of the substances with the higher probabilities. The student should consult the rules used to obtain these bieher vrobabilities in order to determine which of the tescs shokld be repeated. The notes contained in the rules will provide helpful information for running the tests and recording observations. The student should run the program for each of the seven unknown suhstances. A printer can he used to obtain a copy of the results and exverimental observations for each substance. Next, the student should compare the system's answers with his or her deductions. Do they agree for every unknown? Are there unknowns that the expeit system cannot positively identify'! Did problems ocrur in identifying these unknowns without the Ad of the computer? Tests that are suspect should then be repeated. In contrast to the EXSYS-based svstem. the KDS svstem asks only the questions i t needs to identifithe unknown. As soon as this svstem has acauired sufficient information to identify an udknown from ;he student, the interrogation is terminated and the results displaved. For the sugar unknown example described above, the KDS system needs definite answers to the questions concerning the thermal test to determine the identity of the unknown. The solubility and litmus tests are not required for the positive identification of sugar. The output from the KDS system for this unknown is: Your analyte is sugar. The responses and inferences with an ' in the leftmost column were specifically necessary for me to draw this conclusion. The information you gave me was: *O Your analyte melted or decomposed. *1Your analyte decomposes and loses water. '3 Heat decomposes the analyte into a black substance and water.
From these responses, I drew further inferences, some of which may appear trivial or obvious. All my inferences are: *2 Your analyte is completely water soluble. '4 When heated, the analyte turns a yellow-brown color is FALSE. .5 Your analyte is insoluble in water is FALSE 6 ' An aqueous solution of the analyte turns red litmus blue is FALSE. KDS does not ask redundant questions and therefore may be used by the student to determine if further testing is necessary to identify an unknown. The system will move on
Table 3.
A Oualifier and Its Values
The addition of 6M HCi(aq) to the unknown sdn.produces QUALIFIERS a colorless soh. S1, containing no ppt. a colorless s o h S1, containing a white ppt.. X. a yellow colored sol". S1, containing no ppt. VALUES a white ppt.. X, in a yellow colored soln. S1. T F T NOT R I IN
Table 4.
The Structure of
a Rule
RULE #I IF:
me addition of 6M HCl(aq) to the unknown saln. produces 1 Soh. $1, containing no ppt. 1 VALUE
THEN: Ag(1) -Probability = 0110 and Pb(1i)-Probability = 0110 J W Q l E S and Fe(iiltPmbabi1ily = 1/10
NOTE: Addition of dilute HCI will ppt. Ag(1) and Pb(l1) ions if they are present. None of the other ions will ppt. The lack of any yellow color In lhe soln.. S1, indicates the probable absence of Fe(ii1).
REFERENCE: EXP 25. p 3. IA
to auestions concernine soluhilitv and litmus test if the studeit enters "don't care" responses for the heating tests. "Don't care" responses indicate that the student has either not run the tests or the results are ambiguous. In this situation the system might produce the following report: Your responses suggest the following is possible: 3 Your analyte is salt 4 Your analyte is hypo 5 Your analyte is sugar
There may be more than three possible answers. Do not assume that I have exhausted all possibilities. The infomation you gave me was: 0 Your analyte melted or decomposed. is don't care 1 Your analyte decomposes and loses water. is don't care 2 Your analyte discolors to a yellow, hrown color. is don't care 3 Your analyte decomposes into water and a black substance. is don't care 4 Your analyte is completely water soluble. 5 An aqueous solution of the analyte turns blue litmus red is FALSE.
Fnm
responses. I draw further infrrcnces, some of m,hirh may npp'nr trivial or obvious. All oimy infrrrncrs are: 5 Your analyte is insoluble in water is FALSE. rhea?
This example emphasizes the importance of the heatina tests in identifying t h e unknown and illustrates how KDS deals with situations in which insufficient data is available. After receivine the above outout. . ,the student should obtain results from heating the unknown. If the results entered for heatine were erroneous. KDS either misidentified the unknownor reported that i t could not make an identification. In these cases the explanation facility was not as helpful as that found in EXSYS. Both systems demonstrated the use of an expert system to confirm student deductions, to indicate potential sources of error and to provide supplemental information concerning specific tests. In the design and use of expert systems, one should remember the two major functions of the explanation facility: (1) to interpret or explain the question to the user (student) and (2) to describe the process used to arrive a t t h e answer(s)
from information stored in the knowledge base and the user's input. A System for Use with Qualltatlve Analysis of Selected Metal Ions
We have used EXSYS to develop a more sophisticated expert system, QUALl, for assisting students in the qualitative analysis of six metal ions (Fig. 2). The student determines the behavior of each cation using solutions of known composition and appropriate reagents. On the basis of these tests and observations, the student develops a flow chart to assist in the analysis of several unknown solutions. After identifying the ions present in these solutions, the students consult the QUAL1 expert system to confirm their results. If the system indicates uncertainty in the identification of one or more ions, the students may request an explanation of the results from the system and repeat any laboratory tests they feel are necessary to remove the uncertainty. In contrast to the rules developed for SUBID, which contained only one condition, many of the rules used in the knowledge base of QUALl have several conditions. The nrohahilitv scale used in QUALl ranees from 0110, ion definitely not present, to 101i0, ion definitely present: Average probability values of 1/10 to 3/10 indicate that the ion is most likely absent hut one or more tests are required for absolute confirmation. Values of 7/10 to 9/10 indicate that the ion is probably present hut further testing is required for positive identification. Total uncertainty about the presence or absence of an ion is signified by values of 41 10 to 6/10. The possibility that the student did not run a given test must be considered for every test in this analysis. 'If the test was not run, probability values of 5/10 are assigned to all ions sought hy the given test. The probability values for the ions in specific rules were assigned by the author based on his experience. Notes and references citing pages and sections in the student's lab manual appear with each rule as part of the explanation feature of the system. This feature has proven most useful to students exoeriencine difficultv because it euides them to specific information for the r~solutionof a given ~rohlem.l h r~rwidinethis information tostudents. the sssk m assumed the role of an intelligent, patient laboratory assistant. Tahle 3 shows the first qualifier and its values used to generate the first multiple choice question to he presented to the student. The student's response to this question informs the system of the results of the addition of HC1 to the unknown solution. A qualifier plus a specific value form the IF part of a given rule. Next, a specific qualifier-value pair is matched with the cation nrobahilitv values in the THEN 3). T&, the single qualifier portion to form a rule with its five oossihle answers eenerates RULE it 1(Table 4) plus four additional rules. The precipitation reactions used to separate the metal cations into three classes (Fig. 2) involve simple rules, for example RULE 1. When a separation reaction produces a precipitate, the two ions in the given class have equal probabilities of being present; a specific ion is either present alone or in combination with the other ion of the class. When the results of each subsequent test within a given class are entered into the system, the rules either increase or decrease the prohahility of the presence of a given ion. Within each class, new conditions are formed for each chemical test. Each additional test adds a new condition to the existing condit i o n ( ~ to ) form another rule. In order to obtain a positive identification (probability 10/10) for an ion, the rile containing positive results for all the required tests must he satisfied. The user input and resulting average probabilities for the six ions based on the results of one student's analysis are shown below.
able
-
Volume 64
Number 4
April 1987
343
~
.
1laql ~ pb2* * laql
.
~ a " Iaql Ba2+ iaql
.
Iaql Fe3+ laql
Ag
Add 6 M HCl ( a d
A
P P ~ .X
soln. S l
I
I
.
b13' laql ca2* laql AgCl l s l , white PbCl, I s ] , w h i t e
.
Fe3' laql Ba"
laql
I
b u f f e r e d NH,
Hot water
NH, l a d
K,CrO,
A
I
lad
I
PPt.
Excess NaOH Ap INHJ: lad
HNO, ( a d
s o l n . S5
I
1
w
I
PPt. Y
s o l n . S2
ppt. X i
Z
A
s o l n . 57
PbCrO, laql
BaCO, 14, white
NaOH laql
I HCl laql
KSCN laql
blood r e d
6M acetic acid ammonium a c e t a t e
Alumlnon Dye
A1 IOHI3 IS1 pink
ppt. Z l
I
!sY ppt. = precipitate s o h . = solution [aq) = i o n i n aqueous s o l u t i o n 1s) = s o l i d Colors o f complex i o n s o r p r e c i p a t e s are l i s t e d w i t h t h e species
NaS , O,
laql
white
Figure 2. Flow chart for sapsration and identification d six metal cations
344
Journal of Chemical Education
K.CrO,
I laql s o h . SB 1
INPUT (student response to multiple choice questions underlined) When HCI is added to the unknownsoln.: a soln.. SL containinwhite oot.. X. is observed. When hot water is added to out. .. X: eet. -. X does not dissolve and remains in the s o h . now known as 52. The addition of KnCrOd(aq)to soln. S2 produces: a vellow ppt. u ~ ~ djul!~$3. The additiun UI mnr. NnOH to soh. 93:dissolves out. X1. The addition 01' NH3(aql,ammonium hydroxide, tu ppt. X: & solves nut. X to rive s o h S4. The addition of HNOa(aq)to soln. 54: produces no I&. Soh. S1 (from the addition of HCI) is made slightly basic and buffered NHs(aq) soh. is added: no not. is formed in soh. S. When soln. S5 isevaoorated toabout3 mL (needed to concentrate any remaming ions, and 3M ammonium carbonate is ndded to thus concen~ratcds o h until the remaining soh. is hnaic: TEST .YOTN.
OUTPUT (with corresponding probabilities): Pb(I1) 10110 Ag(1) 7/10 Ba(I1) 5/10 Ca(I1) 5/10 Al(II1) 0110 Fe(II1) 0110 The results of the system's evaluation of the student's input indicate definite confirmation of the presence of Pb(I1) and the absence of Al(II1) and Fe(II1). Ag(1) is most likelv nresent but analvsis should be reneated. The student may now use the explanation facility of the system to see the roles and notes used to determine the probability for Ag(1). Since the reaction used to precipitate Ca(I1) and Ba(I1) ions as carbonates was not nerformed, the results indicate a total uncertainty about thepresence of these ions.
..
Conclusions All three of the expert systems have been used successfully in the freshman laboratory program a t the Virginia Military Institute, where all freshmen are required to take chemistry. Each system was used as an integral component of the lahoratory and was available to students on personal computers adiacent to the lahoratorv. Because all freshmen are required to take a course in the use of personal computers and anolications software. students exnerienced little difficultv in using the system. In addition to the interest aenerated hv the application of this new technology to chemical analysis, the-use of the system required students to organize their results in order to respond to the questions posed by the expert system. The explanation facilities of the system provided consistent, patient responses to the students' queries, regardless of redundancv. This feature freed instructors to deal directly with probiems associated with laboratory procedures. The compiled "run-time" versions of the expert systems were used by the students. This software runs easily on IBM-PC's or PC-compatible computers. Color monitors made the displays more interesting. A Epson LX-80 printer was used for hard-copy output. When the method for the metalion analysis was examined todevelop theQUA1.I system, an alternative method for the analvsiv became apparent. Instead of identifying ions in a .. given class immediately after separation, all the ions can first he separated into three classes and then ions in each class can be identified. This makes the method less cookbook in nature and emphasizes the distinction between sepA.
aration and identification reactions. The expert svstem based on this method, QUAL2, assumes that the ions i n the unknown solution have been separated into the three classes by the correct sequence of precipitation reactions before the identification of any ion is attempted. After this initial separation. anv class l ions are identified. then ions in class 2. &d finall; those ions from class 3. ~ e g a r d l e s sof which p;ocedure is employed, it is important that the sequence of questions posed by the computer he the same order as the lahoratory procedures performed hy the student. The careful examination of a knowledge domain required in the desian of an exDert svstem mav lead to im~rovement of e x i ~ t i n ~ m e t h oor d sto the development of new-ones. This is a benefit which often results from the desim - of expert systems. The application of an expert system to chemical analysis does not~eplncelaboratoryoperitions and observations,but rather providesa tuol toassiat thestudent inexamining their results and drawing accurate conclusions. Thus. t h e s e a n ~ l i cations represent the realistic use of an expert system. Some of the more important advantaees of exnert svstems as educational tools are summarized bzow: .A
1. Delivery of specific information to meet the needs of individual
students. 2. Explanation of how information was derived. 3. Exnlanation of whv a oarticular ouestion was beine asked. 4. Patient. tireless resnonse to studint needs. 5. Ease of design and modification of system software by the
instructor. 6. Organization of information and educational functions re-
quired to develop an expert system.
I . Convenient storage of knowledge on a particular subject.
Finally, the design and modification of expert systems usingcommercial shells such asEXSYS and KDS appears to he much easier than the construction of such systems with versions of PROLOG or LISP currently available for personal comnuters. An instructor with some comnuter exnerience should he ahle to construct a simple system using either EXSYS or KDS after 6 to 8 hours. The construction of such simple systems using PROLOG requires more extensive training in this language. Expet System Avallablllty
The EXSYS runtime program and the files, SUBID, QUAL1, and QUAL2 are available from Project SERAPHIM on a single disk. The KDS version of SUBID is also availahle. Acknowledgment This work wassupported in part hy granrsfrom the Exxon Educational Foundationand the VMI Research Committee. The author would like to thank B. W. Mundy who provided manv useful suerestions and to Daniel McClintock who developed the KI% system. Blbllography Denning, P.J.Am.Sei.1986.74,lS. Dessy,R.E.AndChrm.l984,56,12WA. Feigenbaum. E. A,: Bur, A. The Handbook of Arfifirid
Inlelligenee: Kaufm-: h Forsyht, R. Expert System: Principles and Case Studies:Chapman and Hall: London, 1984. Hohne, 8. A,: piera. T. H., Eds. Artificial Intelligence Applicofiow in Chemktv; American Chemical Soeiefy: Washington, DC, 19%. Thompson.8. A.:Thompn, W.A.BYTE 1985,315. Watorman, 0. A. A Guide foEzpen S ~ 8 t e mAddison-Wdey: ; Reading. MA. 19%. Altos.
1981.
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