ABSTRACTA lot of expertise has not be harnessed in the past fromexpatriate experts due to lack of a standard methodology forknowledge acquisition. A knowledge acquisition methodologyconsists of a collection of procedures, techniques, tools, anddocumentation aids which will facilitate the development of anexpert system. The drawbacks and associated factors whichcharacterise this bottle-neck problem are outlined. Knowledgeelicitation is presented as the most effective mode of knowledgeacquisition. The use of inductive programs as tools of knowledgeacquisition is discussed. Employment of this methodology in thedevelopment and as a useful model in acquiring knowledge for bothknowledge bases and procedural systems such as relationaldatabases is proposed. By using heuristic or rule-of-thumbknowledge in the areas where algorithmic knowledge is incompleteor inappropriate, a particular system can be described morecomprehensively. This paper describes the methodology currentlyin use in the construction of an expert system for Agro-meteorological data analyses and interpretation started lastyear. The model runs procedures in INSTAT (InteractiveStatistical package) on a LEORNADO Expert System shell, amongother things, for:(a) onset, end and length of rainy or growing season(b) dry spell analysis(c) Frost risk(d) potential evapotranspiration(e) water balances(f) crop calendars and(g) agro-ecological zoning.The knowledge acquisition process was recently completed and theknowledge base and inference engine are now almost complete. Foragricultural systems, the benefits have been ease of expressingknowledge, flexibility of expression, explanation of reasoningand human-like reasoning in agro-meteorological data analysis andinterpretation.
INTRODUCTIONA number of experts have been involved in numerous projects inthe Zimbabwe Government ministries in an attempt to transferexpertise to the local employees. However, not enough success wasmade in acquiring the knowledge due to lack of a clearmethodology and an expert evaluation strategy. An automaticknowledge acquisition methodology would attempt to transfer theexpertise from the expert in an efficient and effective manner.Inmeteorology, the start of the rains is usually not definedaccording to the absolute rainfall amount received over aspecified period, but by whether or not that rainfall, light orheavy is from a weather system that normally marks the onset ofthe rains. A certain rainfall threshold may be biased againstareas of low mean annual rainfall in defining the start of therains. For agricultural purposes, a knowledge of both the startof the rains and planning opportunity are invaluable. Thedefinition of planting opportunity, just like start of rainsdepends on crop and farming system. Where dry planting is inpractice, planting opportunity is not so much a problem but themost probable date for germination or emergence. An expert systemcan give a rainfall threshold that presents a realistic plantingopportunity. The expert system under construction is expected togive crop calendars for a variety of crops that are grown overthe Southern African region. Drought monitoring groups in theregion are now concerned with thee availability of adequate watersupply and food to the population of the country.
Knowledge acquisition is defined as the transfer andtransformation of problem-solving expertise from some knowledgesource to a program (Buchanan et al, 1983) . The main usableresources of knowledge are experts, textbooks, data bases, andexperience from humans. Knowledge elicitation is a special kindof knowledge acquisition where the source of information is thehuman expert and a knowledge engineer. The role of the knowledgeengineer is to elicit verbal data from the expert, which may bedifficult for the expert to verbally express his knowledge. Theknowledge engineer then interprets the data by abstraction intotypes and structures of knowledge, or simply modelling. Modellingmay be complicated by incomplete data or integration of data frommultiple sources. The knowledge engineer is expected to choosethe most appropriate storage structure of the various identifiedtypes of knowledge and be aware of the techniques and tools tobe employed. By using rapid prototyping, the knowledge engineertries to build a model system in small steps. Building aprototype focuses on the elicitation and interpretation processcarried out by the knowledge engineer and may motivate the expertsince it illustrates the feasibility and continuing effectivenessof the enterprise. The problems of rapid prototyping identifiedby Wielinga (Siekmann J.,1987) were that a solution isconstructed without an encompassing analysis of the problem andso the inadequate problem solving method results in frequent anddrastic backtracking, and mapping the data and implementation
constructs. In commercial environments, the experimental approachis expensive, hardly manageable, and fails to fit the life cyclemodels of traditional software engineering practice.
Hayes-Roth (et al 1983) and Frieling ( et al 1985) overcame someof the problems inherent in the experimental approaches by usingmore structured approaches, here the knowledge acquisition wasdecomposed into a number of stages. The main stages of knowledgeacquisition consist of problem identification, problemconceptualization, knowledge formalisation, representation,prototype construction, review, knowledge refinement andextension, reformulation and the field test. Even though thestage approaches are a considerable improvement over theexperimental approaches, they are flawed by lack of availabletools to support the different activities, vague specificationof the intermediate results, lack of documentation (in particularthe intermediate results), and the development process ignoresthe features of the operational environment.
The purpose of this paper is to outline a methodology forknowledge acquisition in the construction of a knowledge basedsystem for agro-meteorological data analysis and interpretation.The objectives of the methodologies for knowledge acquisitionare :(a) To record accurately the requirements of an expert
system.(b) To provide a systematic method of development in such
a way that progress can be effectively monitored.(c) To provide an expert system within an appropriate
time limit and at an acceptable cost.(d) To produce a system which is well documented and easy
to learn.(e) To provide an indication of any changes which need to
be made as early as possible in the developmentprocess.
(f) To provide a system which is liked by those peopleaffected by the system.
PROBLEMS IN KNOWLEDGE ACQUISITIONKnowledge acquisition or the construction of knowledge bases inknowledge based systems is the most difficult task in the problemdomain of the knowledge based systems. The drawbackscharacterising this bottle-neck problem are that the singulartheories of knowledge acquisition are often not regarded asportable enough for the practice of knowledge acquisition on aday-to-day basis, and the experience base of knowledgeacquisition is currently still small and superficial, preventingsignificant validation of the methods and theories developed forknowledge acquisition in the large (Savory, 1988) .Knowledge acquisition is a "bottle-neck" problem because of:(1) Difficulty of communication between experts in
specialist fields due to their particular jargon;(2) The facts and principles underlying many domains of
interest cannot be characterised precisely in termsof a mathematical theory or a deterministic modelwhose properties are well understood;
(3) Experts need to know more than mere facts orprinciples of a domain in order to solve problems;
(4) Human expertise, even in a relatively narrow domain,is often set in a .broader context that involves agood deal of commonsense knowledge about theeveryday world;
Other problems that occur in knowledge acquisition are:(1) The expert may provide incorrect information;(2) The knowledge crafters ' questions may introduce
spurious expertise into the knowledge base;(3) The expert's terminology may not be understood;(4) The expert's explanations may wander aimlessly;(5) There may be frequent interruptions;(6) The experts and the knowledge crafters may not get
along because of personality differences.
One of the contributing factors to the difficulty in acquiringexpert know-how for expert systems is the fact that experts knowboth qualitatively and quantitatively well above the novices.LaFrance (1989) compared expert versus novice knowledge andconcluded that:(a) experts are goal-driven rather than data-driven;(b) experts focus on goals rather than effects;(c) experts' knowledge is more functional than that of
novices;(d) experts' knowledge is broken off differently from
that of novices' knowledge;(e) experts' knowledge is "automatic" in the sense that
particular environmental situations immediatelytrigger specific solutions from long-term memory,by-passing conscious processing;
(f) experts have greater knowledge situations selectedfrom experience.
Implications of differences between expert and novice knowledgeindicate the difficulty in coming up with a standard interviewsituation, resulting in neglecting to define the depth of theexpertise, mismatch between expert and knowledge engineer, andthe pragmatic nature of the expertise. Suggestions to overcomethe difficulties of knowledge acquisition consist of elicitingproblem schema, uncovering goals, focusing on the knowledgestructures, grasping complexity by use of a structuredacquisition process, recognizing and describing deviations fromthe usual, and giving more time to eliciting stories aboutconcrete cases.
MODES OF KNOWLEDGE ACQUISITIONThe most effective mode of knowledge acquisition is knowledgeelicitation, which was employed in this study. Common elicitationtechniques as outlined by Siekmann (1987) are focused interviewswhich cover a sequence of topics to obtain factual knowledge;structured interviews for deep probing of structure of
concepts/models; introspection which is a hypothetical taskperformance to obtain global strategies/ self-report where theexpert thinks aloud while solving the problem on reasoningstrategies/inferences; user diagnosis; review; laddering foracquiring concept structures by use of graphical models; andmultidimensional scaling method to obtain structures of concepts,problems and solutions.
Interviewing techniques are used in obtaining most of theinformation. A focused interview is a "normal" interview whoseeffectiveness is highly limited. It is very simple and providesfactual domain knowledge, types of problems, functions of theexpertise within the environment, objects and agents in theenvironment, and some characteristics of users. The focusedinterview is similar to a normal conversation where the topicsare prepared in advance. Its global structure consists of anintroduction, a series of questions focusing on a sequence oftopics, and a closing summary of the interview. Meaningfulassociations between topics are prepared.
A structured interview is like an interrogation to uncover thedetailed structure of a concept. In a structured interview,knowledge pertaining to some concept or model is elicited bycontinuous clarification, explanation, consequence,justifications, and instances. Unlike a focused interview whichis "breadth first" oriented, a structured interview is "depthfirst" oriented. A successful structured interview demands theknowledge engineer to be well acquainted with the domainterminology and have good interviewing skills, and the expert tobe actually motivated.
Introspection is analogous to telling stories, anecdotes orgiving testimony, and is a form of thinking aloud on solving animaginary problem. This is contrary to a focused interview wherethe expert is not asked to think aloud but answers a question by"running a mental model" of some task. As suggested by !K (1972),introspection drafts are shorter than self-report protocols. Themain data obtained by introspection is a global description ofthe strategies that the expert uses in solving some set ofproblems, some justifications for the solutions and decisions inthe problem solving process, and some global knowledge on thetypes of knowledge the expert uses in solving problems.
AUTOMATING KNOWLEDGE ACQUISITIONCreation of knowledge bases can be improved by the assistance ofautomated knowledge acquisition tools. An example of such toolsare programs that contain detailed models of groups ofapplication tasks where the user enters knowledge on specificapplications into these tools in terms of the predefined taskmodels incorporated into the tools. Musen (1989) came up with aninteractive program that assists knowledge engineers in theconstruction of task models, and that automatically generatescustom-tailored, graphical knowledge acquisition tools based onthose models, which was called PROTEGE. The knowledge acquisition
tools that PROTEGE generates to enter the knowledge that definesindividual applications are used independently by experts. Thedevelopment of task-based knowledge acquisition aids isoversimplified, and the difficult model-building part ofknowledge acquisition can be separated from the entry of contentknowledge by experts. The PROTEGE system is now used to generatetwo different knowledge acquisition tools such as p-OPAL and asystem called HTN for the hypertension domain, both of which areused for producing knowledge bases for medical consultations.
AUTOMATIC KNOWLEDGE ACQUISITION BY INDUCTION PROGRAMSAutomatic knowledge acquisition is aimed at automating theprocess of knowledge acquisition. Common examples are automatedknowledge elicitation where the expert's knowledge is transferredto a computer orogram as a side-effect of a man-machine dialogue;machine learning where the computing system could perhaps learnto solve problems in much the same way that humans do i.e. byexample; interactive programs which elicit knowledge from theexpert during the course of a "conversation" at the terminal;programs that learn by scanning texts rather than as humans readtechnical books; and programs that learn the concepts ofsupervision from a human teacher.
Inductive learning is a heuristic search through a space ofsymbolic description, generated by the application of variousinference rules to the initial observational statements. One formof inductive learning is when a learner is provided with set ofdata, some which are examples of a concept and some of which arenot (the counter-examples). Examples are sometimes calledpositive instances, and counter-examples are called negativeinstances. An induction program might be able to induce rulesfrom the examples: rules that the expert himself might not beable to formulate. In inductive learning we are given a rule thatis true in general, and we deduce that it applies in specificcases; whereas in induction, we are given specific examples andwe induce a general rule.
Induction can be useful if there are documented examples, or ifthey can be obtained easily; and suitable problems includepattern recognition (for a subset of shapes), fault diagnosis,and guidance in the use of a set of procedures. Induction isconsistent and unbiased, although it probably uses only one formof reasoning. Rules are relatively easy to understand, and theoutput is simpler than that from statistical packages. Unlikemany statistical methods, it makes very few assumptions about theunderlying distributions in the data. It is repeatable andindefatigable, and it does not make false assumptions or forgetto state results as an expert might. If a training set isavailable, then induction is rapid, and provided that theexamples are comprehensive, it can discover rules that the expertmight be unaware of or be unable to express clearly. It cansuggest rules and identify difficult, interesting, orcontradictory cases in the training or subsequent examples.
Induction discovers knowledge away from the expert, providing theknowledge engineer with results , questions, and hypotheses toform the basis of a consultation with the expert. Alternatively,the expert might find an inductive program a useful way ofexpressing his knowledge or experimenting with hypotheses.Induction usually produces results without explanations. Itcannot distinguish between attributes that are necessary andthose that are confirmatory. Output needs to be evaluatedcritically.
RESULTS AND DISCUSSIONThe acquired expertise was implemented into the Expert systemmodel for Agro-meteorological data analysis and interpretation.The initial stages involved(a) the set-up of the conversion of historic and climatic
data to computer compatible form;(b) the process to organise, check and make historic and
current data sets easily available for applications;(c) the development and application of software
for all kinds of users within the Southern Africaregion for agro-meteorological data analysis and
interpretation.
Figure 1 shows an outline of the process for fitting and usingmodels for rainfall data on this system. Fitting the agro-meteorological models is done in two ways, for rainfall amountsGamma distributions are used and for the chance of rain a MarkovChain model is applied. The expert system program runs INSTAT-format macros to do the analysis. Gamma distributions were fittedto the rainfall amounts. What happened on the previous day couldbe taken into consideration to fit the model. A plot of the meanrain per day when the previous day is dry and rainy is shown onFigure 2 and Figure 3, respectively. A first order Markov chainis fitted to the chance of rain, where Fourier series are usedto model the seasonality (Figure 4). The plot shows the fittedvalues for the chance of rain against the day number (DAY) forthe previous day dry and rainy respectively. Figures 5 and 6 arefor the mean rain per rainy day The final stage is to use themodel that has been fitted. As an example, results are derivedfrom the model for the probability of having 5, 7, and 10 day dryspells, in 15 day periods, conditional on the initial day beingrainy. The results are shown in Figure 7. They show that theprobability of a long dry spell (after planting rains) is lowduring October and November.
A knowledge acquisition methodology outlined in this paper is aneffective strategy of harnessing as much expertise from theexpert as possible. Productivity and efficiency would ultimatelyimprove if the expert system is finally constructed using theautomatic acquisition methodology in the field of agriculture.The expert system offers a great deal in decision support inagriculture, especially the to encapsulate a human expertknowledge, experience and reasoning in an almost human way for
the maximum benefit of an almost infinite number of potentialusers in Africa.
PERSPECTIVES IN KNOWLEDGE ACQUISITIONArinze (1989) proposed a Natural Language Interface or Front-Endas a means of effective knowledge acquisition for knowledge basedsystems. The advantage of such a system is in transferring theburden of knowledge acquisition from the user to the system, atthe same time providing ease of natural language interaction tothe user. Using a knowledge acquisition expert system in parallelto this front-end is highly beneficial in the development of partor wholly-automated knowledge acquisition. In this study,knowledge elicitation and induction programs were the main toolsused in acquiring the expertise needed in the construction of aknowledge base. Further research is anticipated in thedevelopment and use of the model for acquiring knowledge for bothknowledge bases and procedural systems such as relationaldatabases specifically for crop calendars for the SADC region(Southern Africa Development Community).
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