Agricultural Systems 41 (1993) 53-76
Techniques of Representing Knowledge in Knowledge-Based Systems*
Institut fi~r landwirtschaftliche Betriebslehre, Justus-Liebig-Universit,~tt, Senckenbergstr. 3, W-6300 Giessen, Germany
(Received 30 May 1991; accepted 18 March 1992)
A BS TRA C T
Increasingly complex decision-making situations increase the probability that wrong choices will be made. Knowledge-based systems can help increase the success rate, thereby improving farm performance.
Knowledge-based systems differ according to their construction, the way in which they are programmed, and the mode of representing knowledge that is employed. The representation of knowledge in the form of production rules has established itself as a viable method alongside those models that are based on production functions. The nature of the problem to be solved determines which of the two techniques of representation should be selected.
Two concrete examples have been employed to illustrate the methods and techniques involved in the two approaches; the various advantages and disadvantages have been discussed, and it has been shown in conclusion where the pitfalls are and how the advantages of both procedures can be utilized by creating hybrid systems.
Successful farmers can be distinguished from less successful ones by virtue of the fact that they make bad decisions less frequently and do the right thing more often. However, going about things the right way has
* This paper is based in parts upon a presentation of Prof. Dr Dr h. c. F. Kuhlmann together with the author.
53 Agricultural Systems 0308-521X/92/$05.00 1992 Elsevier Science Publishers Ltd, England. Printed in Great Britain
54 Peter Wagner
become more and more difficult in an age of ever more complex processes and general uncertainty. The farmer often does not have the time to acquire the proficiency in such areas as production technology, marketing or management that can help him steer clear of the wrong choices.
It would thus seem appropriate to offer the farmer the knowledge required for good production and management in a different form than has been customary so far. One way of doing this is by means of 'knowledge-based systems'. The concept of knowledge-based systems involves the use of computer programs which compile widely-scattered existing domain knowledge to help the individual user in reaching decisions within clearly demarcated problem areas. Models of this kind are conceived and programmed chiefly on the basis of production functions and/or production rules. For examples, see Section 3.
This paper is aimed at explaining why knowledge-based systems are needed, and to illustrate what they are. Two concrete examples are employed to demonstrate how systems based on production functions can be differentiated from those based on production rules, how such models work in practice, and what the conditions are that affect the choice of a particular mode (or the selection of a hybrid form) in presenting specialized domain knowledge.
2 THE NECESSITY FOR KNOWLEDGE-BASED SYSTEMS
2.1 Necessity dictated by progress
Biological, technological and organizational progress in recent years has resulted in a tremendous increase in the intensiveness of agricultural land-use and livestock farming. The initial advantages in terms of economizing on scarce land and manpower are, however, increasingly offset by disadvantages.
One chief result of these new developments has been that farm producers are now confronted with a very broad and rapidly expanding range of farming techniques designed to increase and ensure production and to optimize productivity. This variety sooner or later becomes so complex that it may get out of hand and necessarily begins to encourage miscalculations; and miscalculations--the too-extensive over-application of the means of production at the wrong time and in the wrong way--mean waste. The most immediate negative effect is the economic disadvantage incurred by the individual farmer. Over and above this, such miscalculations can cause ecological damage resulting from increased pressure on natural resources.
Representing knowledge in knowledge-based systems 55
There are two main ways of limiting these negative economic and ecological effects. First, methods and processes can be developed to bring about structural improvement; this, however, is not the object of enquiry in the present paper.
Second, an attempt can be made to optimize processes, i.e. to improve and/or to lower the range and level of application of production factors, with due regard for the farm and field specific conditions. The state of electronic data processing now permits the development of knowledge- based, computerized operational and regulative procedures which allow biological production systems to be run in close accordance with precise aims and objectives.
2.2 Necessity dictated by the nature of decisions
The decision-making situation in farm processes is generally characterized by incomplete information or uncertainty (and it should not be forgotten that the models that are constructed themselves often simplify or exclude these very factors). There are two causal areas that are responsible for uncertainty in decision-making processes. The first arises from the complexity of the decision-making domain, i.e. from the sheer range of possible alternatives. This can be called the 'structural component of uncertainty'. The second causal area arises from the lack of predictability of certain input variables of biological production systems that are beyond our control and not accessible to proper quantification. This can be called the 'dynamic component of uncertainty'.
2.2.1 The structural component of decision making under uncertainty A reduction of the uncertainty resulting from the structural component can be achieved with the aid of quantitative and qualitative models. As well as including a system of equations for calculating the relative economic advantages of alternative courses of action, these models contain complete systems of production functions and/or production rules for showing the quantitative and/or behavioral framework of the various strategies that are to be implemented. These concepts will be elaborated by means of the examples that follow.
It is always difficult to estimate such functions or to formulate the rules underlying the models. In some cases, systems of equations can be worked out on the basis of previously documented experimental results involving appropriate procedures for approximation. As data-bases are not available for many areas, experiments must first be conducted. A second avenue of approach is to have recourse to existing expert knowl- edge (scientists, farmers, production advisers).
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A number of problems whose structure cannot be described in formal mathematical terms are associated with uncertain knowledge or un- certain relationships which themselves can no longer be described in terms of interval or ordinal scales. Particular problems of this kind relate to diagnostic or therapeutic measures, or to dichotomous decision areas. The solution of diagnostic or therapeutic problems forms the basis of many decisions in the realm of plant protection, for example. Problems relating to dichotomous decision areas are generally characterized by the occurrence of one of two possible events (for example, a herbicide may be harmful to bees, or can be viewed as not harmful). It is also advisable here to draw on the knowledge and experience of experts in determining production rules (or, more simply, rules) and the courses of action relevant to these.
The circumstances here described make it clear that such projects lead themselves to new ideas and new experiments in agricultural research. This is true whether or not the construction of a rule-based or a functional model is involved. Existing research deficiencies are often discovered only during the developmental stages of such systems. Also, it is unfortunately all too common that the ways in which model-builders communicate with those who could help in removing such deficiencies often leave much to be desired, i.e. interdisciplinary cooperation is absolutely necessary.
2.2.2 The dynamic component of decisions made under conditions of uncertainty The goal of constructing decision-making models is, however, accompanied by the aim of reducing the second (dynamic) component of uncertainty. To understand the underlying ideas it is necessary to differentiate within the totality of decision problems by classifying these (in accordance with cybernetics and control theory) into groups relating to open loop and closed loop control. The dynamic dimension of uncertainty is to be reduced by applying models based on the concept of closed loop control (for more details, see Kuhlmann & Wagner, 1986; Wagner & Kuhlmann, 1991, p. 290).
2.3 Necessity arising from intensification
Apart from the general reasons for the necessity of knowledge-based systems, of whatever kind, pressure in the direction of legitimizing the use of decision-supporting models increases according to the level of capital-intensive operation in farm enterprises. A capital-intensive mode of operation is characterized by high turnover compared to the profits gained (for example, with laying hens or feeder pigs). This is also true for intensive plant production. Such production processes have a low
Representing knowledge in knowledge-based systems 57
turnover-to-profit ratio, with the result that operational mistakes are much more likely to reduce profits than in less capital-intensive modes of production such as single suckler cows. This trend will increase in the future, if the increasing complexity of capital-intensive production systems is anything to go by. The more complex the operation of bio- logical production systems, the higher the rate of error. A higher rate of error in determining alternative courses of action obviously has a negative effect on farm profitability.
Models of real systems---and models based on production functions or rules are precisely this---can serve to help the farmer to reduce the level of uncertainty involved in making decisions.
Before going on to discuss two examples of the difference between rule-based and function-based systems, a few examples of knowledge- based systems shall be noted briefly, so that the reader may gain a better idea of what is meant.
3 EXAMPLES OF KNOWLEDGE-BASED SYSTEMS
Knowledge-based systems may be distinguished by different criteria, for example by the kind of representing knowledge. Besides other forms, knowledge can be represented by means of production functions or production rules or a mixture of both (so-called hybrid models).
Examples of function-based systems are Simplan, a model for the simulation of a farm unit for teaching purposes (Mtihe, 1989) or a model designed to simulate the nitrogen regime in arable soils (Kersebaum, 1989).
In a simulation of farm operations by Lal et al. (1987), the allocation of machinery resources are made to fields on a farm-specific priority basis.
The models Genis (variety choice) and Herby (weed control) are intended to support the farmer in decision making related to variety choice and weed management in winter wheat (Kuhlmann & Mtihe, 1990). The models provide decision-oriented information on the expected economic benefits for various relevant actions. Genis and Herby, both function-based models, are commercially available.
An example of a rule-based system is the Grain Marketing Advisor (GMA) developed by Thieme et al. (1987). In operation, the GMA asks the user about time within the crop season, and if on-farm or commercial storage is available. He is also asked for assumptions concerning risk, cash flow, flexibility in delivery and knowledge of the futures market. Finally, the GMA comes up with the most preferable marketing recommendation. The GMA is commercially available.
58 Peter Wagner
Soybug, another rule-based model, is described by Jones et al. (1986). The program Soybug gives advice on the control of soybean insects.
Farmexpert (Wagner, 1992), also a rule-based system, analyses profitability of farms by means of comparing the farm in question with a successful group of similar farms. Furthermore, the model is able to give hints about improving the situation of farms that are in a poor position.
Three out of many examples of hybrid systems are reported by McClendon et ai. (1987), Yost et al. (1988) and Huirne (1990). McClendon et al. designed a soybean pest management expert system that uses scout- ing data and expert knowledge to project insect populations for the next week in a crop of soybeans. Estimates of the expert system are given to a crop growth model to estimate the expected yield loss that would occur in the next week if control action is not taken. Depending on the yield loss and the grower's sensitivity to risk, recommendations are made on the type of insecticide to apply. Yost et al. (1988) developed a program for recommending lime applications for crop production on tropical soils. Appropriate equations compute lime requirements and relative yield loss without lime application, based upon responses from the user. Evaluating the complex preconditions to use the equations properly is done by the expert system. Huime's program, CHESS (Computerized Herd Evaluation _~ystem for Sows), is primarily intended to support farm managers and other livestock specialists in analyzing the economic situa- tion of individual sow-herds.
Further examples may be found in the following work:
--Barret and Jones (1989) draw interesting conclusions on Simulation and Artificial Intelligence. Furthermore, they deliver a comprehensive description of expert systems that have been developed in the USA.
--Dent and Jones (1989) edited a special issue of Agricultural Systems on expert system applications in agriculture. Most of the presented expert systems originate in the USA, one is from the UK and another is from The Netherlands.
--The Deutsche Landwirtschafts Gesellschaft (1988) published the proceedings of their 2nd Congress for Computer Technology in Agriculture where a huge collection of knowledge-based systems from many countries and very different domains is presented. This source is also available in French and German translations.
--The proceedings of the 3rd Congress for Computer Technology "in Agriculture (Kuhlmann, 1990) contain a number of decision support expert systems from almost all European countries. Some interesting models developed in countries of Eastern Europe are also described. This source is also available in French and German translations.
Representing knowledge in knowledge-based systems 59
--Learned Information (1986) presented the most comprehensive (not limited to agriculture) listing of expert systems so far. Unfortunately this publication...