knowledge representation methods for dairy decision support...

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Knowledge Representation Methods for Dairy Decision Support Systems

H. HOGEVEEN,1.2 M. A. VARNER,, D. S. BREE: D. E. DILL,5 E. N. NOORDHUIZEN-STASSEN,' and A. BRAND1

Department of Herd Health and Reproduction Utrecht University

Yalelaan 7 3584 CL Utrecht, The Netherlands

and Department of Animal Sciences

University of Maryland College Park 20742

and Computer Science Department

University of Manchester Manchester M13 9PL, England

and CenexRand OLakes Ag Services

PO Box 64089 Mail Station 670

St. Paul, M N 55164-0089

ABSTRACT

Knowledge-based systems are currently being applied for decision support systems for management of dairy farms. An important feature in the development and application of knowledge-based systems is the knowledge representation scheme used. Although many knowledge representation schemes are available in artificial intelligence, the existing dairy farm management applications only use production rules. However, the knowledge required for dairy farm management may require other representation schemes, depending on the type of knowledge involved in the decision- making process.

Two classes of knowledge can be dis- tinguished: declarative and procedural (or operational) knowledge. Declarative knowledge is concerned with facts in a domain. Procedural knowledge is knowl-

Received April 18, 1994. Accepted July 5 , 1994. 'Utrecht University. *Current address: Institute of Environmental and

Agricultural Engineering (IMAG-DLO), PO Box 43, 6700 AA Wageningen, The Netherlands.

3University of Maryland. 4University of Manchester. SCenex/Land O'Lakes Ag Services.

edge of how to use declarative knowledge. For both types of knowledge, several characteristics can be defined: completeness, certainty, generality, and level. Knowledge representation schemes can be ranked according to their performance on the various knowledge characteristics.

Common schemes for knowledge representation and their strengths and weaknesses are described. Different knowledge representation schemes are il- lustrated for mastitis and reproductive management. (Key words: knowledge-based systems, knowledge representation, dairy farm management, decision support systems)

Abbreviation key: BBN = Bayesian belief network, CCM = conditional causal model, KBS = knowledge-based system.

INTRODUCTION

Dairy farm management encompasses a wide range of activities (5). Decisions concerning these activities are made continually, often using imprecise methods or incomplete information, which can result in suboptimal results. Computer-based tools can be used to support the decision process, thus enhancing the im- pact and result of the decision (62). Ration balancing and sire selection are two examples

1994 J Dairy Sci 77:3704-3715 3 704

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of areas in which computer-based tools have gained widespread use. A relatively new tech- nique in decision support systems is the use of knowledge bases. Use of these emerging approaches can benefit the user and the developer by providing more robust applications that are less costly to maintain.

The method by which knowledge is represented in a knowledge base, i.e., the knowledge representation scheme, can vary. Many different knowledge representation schemes have been or are being developed for specific domains. Every knowledge representation method has its own strengths and weaknesses with respect to the characteristics of the knowledge to be modeled. The knowledge representation scheme chosen for a decision support system has consequences for the performance of the system and the execution of its task (1 1). Knowledge involved in one aspect of dairy farm management often has characteristics different from knowledge in other parts. Consequently, knowledge for a decision support system for dairy farms may require more than one representation method.

Various authors (19, 22, 23, 30, 31, 55, 57) have described the use of knowledge-based systems (KBS) in agricultural management. Although various knowledge representation schemes exist, most descriptions are limited to rule-based systems. No overview is available of the characteristics of knowledge representation schemes in relation to their possible use in dairy farm management support.

The objectives are therefore to describe various knowledge representation methods with their strengths and weaknesses and to provide examples of their application to dairy farm management. Emphasis is on methods for which product development tools are available.

KNOWLEDGE CHARACTERISTICS

Knowledge includes facts about the problem and a wide array of problem-solving strategies that an expert accumulates over time (10, 59). To solve a problem, two classes of knowledge are often necessary: 1) knowledge about facts in the domain, declarative knowledge, and 2) knowledge of how to use this declarative knowledge, procedural or operational knowledge (28). Both types of knowledge have their own features and are described herein.

Declarative Knowledge

At the farm level, declarative knowledge includes observations made on a farm or known to be important for the farm enterprise. These observations can be made by persons or by automatic devices (sensors). Data collected automatically are sometimes preprocessed before they are stored and ready for use. Declara- tive knowledge can be available either from an on-farm or off-farm database. Two important features of declarative knowledge can be described in terms of completeness and certainty. When decisions are being made on a dairy farm, knowledge is often incomplete, uncertain, or both.

Completeness. Incompleteness refers to the proportion of observations that are missing. For example, when observations are made using automated sensors (e.g., electrical conductivity measurements of the milk), malfunctioning equipment can lead to missing observations.

Certainty. Observations can be regarded as certain. However, during decision making, observations often have to be translated into more general terms that are used as a base to draw inferences. For example, in disease diagnosis, it is useful to know whether an animal has a fever, a body temperature >39"C. A difference of only .2"C (38.9"C vs. 39.1"C) can distinguish between an inference of fever or no fever. A decision maker would be more certain of a fever from a sensor reading of 43'C than from a reading of 39.1'C. Thus, representation of knowledge using fuzzy boundaries is often better, e.g., 39.1"C would be classed as a fever with less certainty than 43°C.

Procedural Knowledge

Procedural knowledge uses observations and data and transforms them into information that is expected to be useful to the user. The procedural knowledge in conventional computer programs, such as a management information system, rearranges and combines the various observations to make them easier to interpret. The procedural knowledge in KBS seeks to help solve specific problems (63). Procedural know ledge has three characteristics : 1) generality, 2) certainty, and 3) knowledge level.

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3706 HOGEVEEN ET AL.

Generality. Procedural knowledge can be general or specific. Specific knowledge can consist, for example, of associations between a problem situation and a solution. These associations are often developed from experience and are sometimes described as rules of thumb. An example of associative knowledge is the use of antibiotic therapy. An experienced veterinarian knows what antibiotic to prescribe in which situation without thinking about the exact mechanism of action of that antibiotic. Once such heuristic knowledge is represented, it can be utilized repeatedly without understanding the underlying mechanisms of action.

In contrast to heuristic knowledge, generic knowledge consists of a causal explanation for the undesirable characteristics of that situation. This explanation permits the assignment of ultimate causes and the elucidation of path- ways leading from those causes to the situation characteristics (60). The knowledge used to generate a causal explanation of a problem is more general; Le., the knowledge can also be used to explain the workings of a system or to simulate the behavior of a system.

Certainty. As with declarative knowledge, certainty is also an important feature in procedural knowledge. Reasoning under uncertainty is common in disease diagnosis. Disease diagnoses by experts often takes a qualitative form, including uncertainty (e.g., Staphylococcus aureus is the most likely pathogen causing this specific mastitis case). The decision processes underlying these diagnoses consist of procedural knowledge with varying degrees of uncertainty. Uncertainty is common in biological systems in which precise knowledge concerning mechanisms of action for an organism is limited. Complete certainty can be thought of as a special kind of uncertainty.

Knowledge Level. A computer system per- forming a specific task has various functional levels. The lowest level is the device level, or bit level. The highest level in a traditional computer program is the program, or symbolic level, and is understandable by most people with programming experience. Symbols are representations of real world objects. A KBS introduces a new computational level above the symbol level: the knowledge level. A knowledge representation scheme can be considered to be a reduction of knowledge from the knowledge level to the lower symbol level


(41). Most knowledge can easily be represented in symbols; i.e., it can easily be transferred from one person to another. Knowledge that can easily be transferred into symbols is defined as symbolic knowledge. Subsymbolic knowledge, however, is knowledge that cannot be transferred easily in symbols; i.e., subsymbolic knowledge is difficult to explain to others. The knowledge involved in pattern recognition is regarded as subsymbolic knowledge. For example, in the analysis of milking curves, the dairy f m advisor can recognize problems in milk production immediately, even when the data in the curve are not complete, explaining why a certain production curve indicates a milk production problem takes much more time. Knowledge involved in curve interpretation can therefore be considered as subsymbolic knowledge.

Methods for Knowledge Representation

Various knowledge representation methods have been developed for use in decision support systems. Some methods included and adopted for public domain, shareware, or commercial software packages can be used to facilitate development of decision support systems. Other methods are currently under development in artificial intelligence research laboratories, and those methods are often suited only for one subject matter domain. The knowledge representation methods that have product development software and that appear to be promising for use with dairy science applications are described in the following sec- tions. These selected methods and their various strengths and weaknesses for declarative and procedural knowledge characteristics are sum- marized in Figure 1.

Production Rules. The best known and most applied knowledge representation scheme is the use of production systems. The procedural knowledge in a production system is represented by a set of rules by which the domain procedural knowledge is incorporated: a database of domain declarative knowledge and an inference mechanism for applying the rules to the database. The rules (if . . . then rules) represent condition and action pairs. The antecedent (if) of a rule is a condition for the rule to be applicable, and the consequent (then) of a rule is the action that results when the rule


Type of Knowledge and Knowledge Characteristics Knowledge Representation Declarative Knowledge

Methodology Complete - hcompiete Certain Uncertain

Production Rules Fuzzy Logic Conditional Causal Model

Bayesian Belief Network Neural Network

Simulation

Operative Knowledge ~ _ _ _ _

Generic * * ‘ - Heuristic Certain ”*Uncertain Symbolic*Bubsymbolic

X x X

x x x x X

I knowledge characteristic on righl = Only appropriate for knowkdge characteristic on left

= Only appropriate for knowledge characteristic on right

= Equally appropriate for bMh knowledge characteristics

= Methodology better for knowledge characteristic left

= Inappropriate UY orthe methodology x

Figure 1. Summarization of strengths and weaknesses of various knowledge representation schemes.

is applied (35). Forward and backward reasoning are the most common inference mechanisms. Production rules are very efficient in representing heuristic knowledge, but complete declarative knowledge is needed to solve problems.

Several development tools, called shells, based on production systems are commercially available (18, 49). Because a variety of shells are available, Meyer (39) described minimum standards for the user interface for production system development tools. Production systems have been used in dairy decision support systems to analyze yearly economic performance (6,53), evaluation of reproductive performance (17, 34, 37), milk production performance (21, 67), and comparisons between desired (planned) results with actual results (3, 66).

F u u y Logic. Using fuzzy set theory, varia- bles can be associated with a membership function that can take values between 0 and 1 to describe the meaning of the variable. The basic features of fuzzy set theory can be defined as follows. If S is a set, and s is a member of that set, a fuzzy subset 0 is then defined as a membership function mF(s) that

describes the degree to which s belongs to F (68). Using predefined membership functions, it can be stated how true the statement “fever” is when a temperature of 39.1’C is observed. When the fuzzy set theory is applied in a mathematical or computer system, it can be referred to as fuzzy logic. Fuzzy logic is often used with production rules to combine measures of possibility (32). These features make fuzzy logic useful in situations in which specification of the defining characteristics of important but not directly observable features is difficult.

A number of decision tool development systems that utilize fuzzy logic are commercially available. Although fuzzy logic is mainly applied in controller tasks, it is being applied more frequently in decision support systems (32). In Japan, fuzzy set theory is applied to d a q farm economics (40). Bayesian Belief Network. The theory of

Bayesian belief networks (BBN), also known as causal probabilistic networks, is based on Bayesian conditionalization. A BBN is, qualitatively, a graph on which the nodes represent domain objects and the links between



1, B

Figure 2. Basic features of a Bayesian belief network.

nodes represent relations between these objects (Figure 2). The knowledge is stated in a causal direction: for example, diseases cause symp- toms. Each node in a BBN has a number of states, describing the possible values of the node. Quantitatively, the relationships ex- pressed by the links are represented by conditional probabilities (4, 9). In a BBN, the conditional probabilities for each node on its parents (in this case P(BIA)) are put .in a probability table. When the state of node B has been observed, the conditional probability P(AIB) can be calculated using the probability table.

A BBN can be created with the decision support development tool, HUGIN (4) (Hugin Expert A / S , Aalborg, Denmark). A BBN, which is very useful in modeling uncertainty, can also reason with incomplete knowledge. In dairy science, BBN have been used to examine the cow’s reproduction (48), to diagnose mastitis caused by environmental factors (l), and to determine the blood group of Danish Jersey cattle (47).

Conditional Causal Model. A conditional causal model (CCM) consists of a set of nodes that describe a domain. The nodes are con- nected by a set of unidirectional links, representing a causal dependency of a node on another node. The magnitude of the dependency may be influenced by one or more con- ditions (24, 54). The relationships in a CCM may be qualitative and quantitative. The basic elements of CCM are graphically represented in Figure 3. Node B is causally dependent on node A, a relationship represented by the unidirectional arrow. Node C is a condition, represented by a circle on an arrow. Forward reasoning (simulation) and backward reasoning (diagnosis) are possible with a CCM. A CCM


C

A B I Figure 3. Basic features of a conditional causal model.

is very flexible and allows the generic representation of complex procedural knowledge. Data or observations used for input need to be complete, and a CCM allows no reasoning with uncertainty.

A CCM can be developed using the tool CAMEL (causal modeling environment and laboratory; Laboratory for Artificial Intelli- gence, Erasmus University, Rotterdam, The Netherlands) (61). Using the graphical interface, models in a domain can be made. The relationships between the nodes can then be quantified with underlying functions, written in the artificial intelligence programming language Common LISP (58).

Hogeveen et al. (25) employed CCM for the diagnosis of herd mastitis problems. Schaken- raad et al. (51, 52) used CCM to support decisions regarding feed and grassland utiliza- tion on dairy farms.

Neural Network. A neural network, a model consisting of layers of highly interconnected processing units, can be trained to perform classification tasks. Patterns of input and output are first presented to the model for training. The subsymbolic knowledge of a trained model is implicitly stored in the weights of the connections or arrows pointing toward and away from the internal representation units (Figure 4) (50). Various methods exist to train a neural network; the most frequently used is back-propagation with the generalized delta rule (50). With back-propagation, the user de- fines the number of hidden layers and nodes in each layer. Then, the model generates a first output, based on random weights of the connections. This output is compared with the desired output, and the difference between model prediction and desired output is calcu-

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4 I l l I

Internal Representation Units

Input Patterns

Figure 4. A multilayer (inpuL output, and one hidden layer) neural network. Based on data from Rumelhart et al. (50).

lated. The total squared sum of the calculated differences is then returned into the model, and the weights of the connections are changed to minimize the error. This procedure is repeated many times for all combinations of input and output. The ultimate goal for the model is to find a single set of weights that satisfies all the pairs of input and output presented to it, which is generalized to classify new data correctly.

Neural networks are good at pattern recognition; they require no assumptions on data frequency or distribution, and, after training, neural networks can perform classification tasks with missing data (43). Thus, trained neural networks can function with incomplete declarative knowledge.

Simulation Models. Simulation models make use of arithmetic instead of artificial intelligence or reasoning, and they are often not considered to be a knowledge representation method. However, simulation models represent knowledge in some ways (65), and, furthermore, they are widely applied in decision support systems for dairy management (12, 29,

64). Simulation models require complete and certain declarative knowledge as input. The procedural knowledge can utilize measures of certainty, in which case, the simulation model is stochastic (13). Integration of heuristic knowledge into simulation models can enhance the performance of those models (3, 27, 45). However, simulation models often provide a solution that is precise but may not be overly robust. A model-based reasoning system can therefore be a good alternative (46).

DOMAIN SPECIFICATION IN DAIRY FARM MANAGEMENT

Domain Classification In Mastltis

On a dairy farm, mastitis problems can occur for individual cows and for herds. For individual cows, a mastitis problem is a cow with clinical or subclinical mastitis. A decision must then be made about treatment of the cow. When the incidence of mastitis on a farm is high, a mastitis problem for the herd exists. For mastitis for herds, a diagnosis must be made and the possible causes of the mastitis problem determined.

In general, decision making about solutions to problems involves three stages: 1) problem detection, 2) problem diagnosis, and 3) decision generation. When these stages are applied to a system for automated detection and diagnosis of mastitis, the resulting system contains six subsystems (Figure 5).

Figure 5. Diagram of the modules in a decision support system for mastitis management. BBN = Bayesian belief network; CCM = conditional causal model.



Mastitis Detection. Mastitis detection on a cow level is normally performed by the milker during the udder preparation before milking. Abnormal milk is a key indicator of clinical mastitis. Diagnosis of subclinical mastitis is more difficult. Even the definition of subclinical mastitis is not clear. An association exists between electrical conductivity of milk and mastitis (42). Electrical conductivity can be measured in the milking cluster and is therefore suitable for use in on-line detection of mastitis (36). Because much of the information in electrical conductivity data is organized in the data pattern, the knowledge used to detect mastitis from electrical conductivity patterns is highly subsymbolic. Therefore, a neural network is a good knowledge representation methodology to use in the detection of mastitis at the cow level (43). Initial results of using a neural network for mastitis detection have been described and seem promising (44).

Pathogen Diagnosis. To treat mastitis properly, diagnosis of the pathogen causing the mastitis is very helpful. Based on clinical examination of the cow, cow history, and herd history, the veterinarian makes a likely diagnosis. The diagnostic reasoning process involves uncertain knowledge (S), and the diagnosis is stated in terms of a likelihood. Furthermore, the declarative knowledge is mostly incomplete. These characteristics suggest that BBN is an appropriate approach for knowledge representation.

Therapy Selection. Once a veterinarian has made a likely diagnosis of pathogen, a decision is made concerning the proper treatment of that cow. Treatment could be antibiotic therapy, but it might also be culling the cow. The reasoning process for therapy advice is heuristic. For a certain pathogen, an antibiotic, alone or in combination with another antibiotic, will be advised. Advice on antibiotic treatment can be extended with other treatment, such as early drying off. Therefore, the most efficient method by which to model the knowledge involved in the therapy selection is production rules. The decision about whether to cull a cow when mastitis is diagnosed depends on the future profitability of the specific cow compared with that of a replacement cow. Stochas- tic modeling can be used to perform the calcu- lations of future profitability (26, 64).

Problem Detection. A mastitis problem for the herd is normally detected by the farmer or,


when the farm is in a veterinary herd health program, by the veterinarian. When a good historical database concerning dairy herd health on a farm is available, automated problem diagnosis can be performed (15). Although exact guidelines for mastitis detection may be difficult to provide, the mastitis incidence rate or a change in incidence rate can indicate a mastitis problem for the herd. When subclinical mastitis is taken into account, SCC can be used to detect a mastitis problem for the herd (16). Electrical conductivity might then be used as a tool to estimate subclinical mastitis and to screen for overall udder health (42). After the herd history data have been preprocessed, heuristic knowledge is used to determine whether a farm can be considered to have a mastitis problem. Therefore, production rules can be used to model the knowledge involved in the detection of mastitis problems for herds.

Causal Diagnosis. To diagnose the causes of a herd mastitis problem, a combination of specialized knowledge from various domains is necessary. The causes of a mastitis problem can, for instance, include a malfunctioning milking machine, improper milking techniques, suboptimal housing, or a deficiency in udder defense. Much of the knowledge involved in the herd level mastitis diagnosis can be described as textbook or generic knowledge. The interrelationship between various causes in the causal diagnosis is very complex. Also, for the herd level diagnosis, uncertainty does not play an important role. An appropriate knowledge representation method would therefore be a CCM.

Advice. When causes for a herd mastitis problem are found, advice must be generated to eliminate the causes. Knowledge involved in the advice is relatively straightforward text knowledge on options to eliminate a cause. When a CCM is used, information boxes with specific advice to eliminate a cause can be included at appropriate places in the system.

Domain Classification for Time of AI

Inefficient reproduction causes significant losses in profitability of dairy herds (7). Inac- curate or inefficient detection of estrus is thought to be the leading cause of these losses. Detection of estrus is required to identify optimal time for AI. A number of estrus detection

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aids have been proposed, and their use has been reviewed (20, 33). but none has been both accurate in identification of estrus and efficient in identifying all estrus periods. Use of an expert system to identify estrus has been proposed (57), but no details on the operation of that system were provided. A hybrid decision support system that utilizes the knowledge described could be constructed to identify optimal time for AI. Such a KBS must be able to reason using knowledge from different sources. The system consists of eight components (Figure 6), seven components concerned with the interpretation of data from the various data sources for estrus detection and an integrating module to interpret the knowledge provided by the other modules in the system. The characteristics of the components involved in a KBS for optimalization of the AI time are described herein.

Dates of Previous Estrus. The dates of previous estrus are useful in predicting the date of the next estrus or in c o n f i n g behavior indicating estrus. Because many estrus periods

are unobserved, this kind of knowledge is likely to be incomplete. Not all cows are ac- curately identified as being in estrus, so some uncertainty is associated with this knowledge. Fuzzy set theory is the knowledge representation method that matches these characteristics of declarative knowledge.

Dates of Previous AI. The dates of previous AI are related to dates of previous estrus. However, because cows are not always insemi- nated, some dates of previous estrus have no AI, and thus are a separate source of knowledge with different characteristics. The AI dates are utilized for other purposes, such as prediction of parturition dates and billing for AI services. Consequently, dates of previous AI are likely to be complete and certain because of their importance. Various methods of knowledge representation could be used with complete and certain knowledge, but fuzzy set theory would provide some flexibility in representing the normal variation in estrus cy- cle length that would be used to interpret current knowledge using previous AI dates.

Dates of Previous Dates of Previous Veterinary Observation Estrus AI Palpation Results of cows

[Fuzzy Set Theory] [Fuzzy Set Theory] [BBN] [BBNI

Traditional Aids Milk Progesterone Electronic Data For Estrus Concentration - Milk Production

Detection - Feed Consumed [Production Rules] - Pedometer

Integrating Module To Identify Time For AI

[Fuzzy Set Theory]

Figure 6. A theoretical diagram of a hybrid decision suppoa system to identify optimal time for AI. BBN = Bayesian belief network.


37 12 HOGEVEEN ET AL.

Results of Veterinary Palpation. Results of rectal examination of the cow reproductive tract by a veterinarian are often used by the dairy farmer to detect pregnancy, to predict estrus, or to identify bovine ovarian dysfunction. The veterinarian usually makes infrequent or irregular examinations of a cow; therefore, those data tend to be incomplete. Research (38) has shown that predictions of estrus or bovine ovarian dysfunction are often inaccurate at predicting estrus or bovine ovarian dysfunction. Although the accuracy of the results may be uncertain, the veterinary assessments are typically certain. The veterinarian's observations are the declarative part of the knowledge and can be considered to be certain, but the procedural part of the knowledge contains uncertainty. Thus, a knowledge representation method such as a BBN would be very appropriate for most types of results of rectal palpation.

Observations of Cows. Observation of cow behavior by the dairy fanner is the primary method for identification of estrus and determination of AI time, but the declarative knowledge is incomplete because cows are not observed 24 h/d. As with results from rectal palpation, not all cows identified in estrus are truly in estrus, and the procedural knowledge would thus be uncertain. However, the pro- ducer is frequently certain of the observations. A BBN would be appropriate for observations of cows that contain incomplete and relatively certain declarative knowledge.

Traditional Devices for Estrus Detection. Various commercial devices for estrus detection are available, and they are used to varying degrees on dairy farms (33). Typically, these devices show many false positives, reflecting a high degree of uncertainty in the procedural component of the knowledge concerning devices for detection of estrus. For herds in which these devices are used, the declarative component of the knowledge is frequently complete (the device is used on all cows) and certain (the device gives yes or no as an an- swer). Production rules could then be used as knowledge representation method with these devices for detection of estrus.

Progesterone Concentrations in Milk. Progesterone concentrations in milk can be used to determine whether the ovary has an active corpus luteum and that knowledge can


be used to infer other knowledge (38). For instance, high progesterone concentrations in milk would be associated with days of very low cow fertility, and AI would not be indi- cated. Data on progesterone in milk would be relatively incomplete because milk samples are infrequently analyzed for progesterone, but the knowledge from the levels would be highly certain, suggesting that a BBN would a proper knowledge representation method.

Electronic Data. An increasing number of electronically collected knowledge sources are becoming available on dairy farms. Electronic pedometers have been utilized to record the activity of dairy cows; activity increases when the cow is in estrus (33). Electronic mount detectors have also been reported recently (14). Milk production and feed consumed sometimes decreased during estrus (2). Typically, the data associated with these electronic sources are complete unless a sensor fails. The declarative component of the knowledge is certain. The certainty of the procedural component for the knowledge varies widely according to the knowledge source. A BBN is a useful knowledge representation method for this kind of electronically collected data.

Integration Module. The knowledge provided by the components in the hybrid KBS described must then provide knowledge to an integration module (bottom of Figure 6) . This integration module would use fuzzy set theory to combine information from the other modules and to predict a time for AI.

DISCUSSION

Knowledge representation is of great in- terest in research of artificial intelligence. A complete review of knowledge representation schemes is therefore beyond the scope of this paper. The knowledge representation schemes described herein include the various knowledge characteristics that tend to be public domain or available in commercial software packages. Therefore, these methods can easily be applied in development of decision support systems for dairy farms.

When KBS are applied in such systems, use of the proper knowledge representation scheme is important to profit from the strengths while avoiding the weaknesses of a single method. Use of the proper representation scheme en-


hances the efficiency of the system. In a larger decision support system, a series of systems can be applied to solve a problem efficiently. The examples of mastitis and reproduction management illustrate this concept. A similar approach was also used by Serodes and Rodriguez (56) in management of drinking water quality. Also, support systems for operational decision making can be a part of a management support system for tactical decision makmg.

The knowledge-based methods described herein are exciting tools in the development of decision support systems and need to be added to existing techniques to apply information processing to obtain functional decision support systems that are accepted by the farming community.

ACKNOWLEDGMENTS

This research has been made possible by the SKBS (Foundation for Knowledge Systems). The authors gratefully thank S. K. Andersen of the University of Aalborg, Denmark, for his advice in Bayesian Belief Networks.

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