Knowledge modelling for constructing an expert system to support reforestation decisions

Knowledge-Based -2XSTEMF ELSEVIER

Knowledge-Based Systems 9 (1996) 41-59

Knowledge modelling for constructing an expert system to support reforestation decisions Christine “Department hFacult,v of Commewe

W Chan”, Michael Johnstonb

qf Computer Science, Utriversit; of’Regina,

and Business Administration.

Uniwrsit)~

of British

Regina,

Sask S4S OA2. C’andu

Columbia.

Vancouvar,

BC V6 T IZ?.

Ctrndu

Received 24 October 1994; revised 12 June 1995; accepted 21 June 1995

Abstract The paper describes the inferential model as a domain independent template for the conceptual modelling of a domain. The model consists primarily of a classification of knowledge types expressed in the most general mathematical terms. The paper also presents a systematic technique for knowledge analysis called the inferential modelling technique, which makes use of the inferential model. This is a domain independent, top-down technique which can be used in conjunction with bottom-up techniques such as protocol analysis for analyze elicited expertise in a domain that is unfamiliar to a knowledge engineer. Some results from the application of the inferential modelling technique to the conceptual modelling of a reforestation domain are also presented. Kq”l~,ords: Knowledge modelling: Knowledge analysis

1. Introduction The process of knowledge acquisition (KA) can be refined into the three stages of knowledge elicitation, analysis, and interpretation. Recent work in this area has suggested the need to focus on individual stages within KA rather than tackling the entire process [l]. The three identifiable phases within the KA process [2] are (a) knowledge elicitation (the process of obtaining information from an expert), (b) knowledge analysis (the process of making sense of the data collected in the first step), and (c) knowledge representation (the process of expressing the analyzed knowledge in an understandable and usable form, to enhance communication and eventual implementation). Our objective in this paper is to focus on the analysis phase of the knowledge acquisition process, and we suggest a top-down model. called the inferential model, to facilitate knowledge analysis. We focus on investigating the second (analysis) phase of the KA process because of the following. Much work has been done on the first and third phases of the KA process. A variety of both manual techniques (e.g. structured interviews, verbal protocol analysis, card sorting, and repertory grid analysis (see, for example, [3])) and automated tools (e.g. AQUINAS and KITTEN, (see. for 0950-7051/96/$15.00 c; 1996 Elsevier Science B.V. All rights reserved SSDI 0950-7051(95)0l019-x

example, [4])) are available for eliciting or capturing information from experts. Machine induction has also been used in knowledge acquisition (see, for example, [5]). [6] contains some representative work done in this area. [3,7-91 provide good assessments and comparisons of the various KA techniques. Knowledge representation has also been extensively researched, and various knowledge representation schemes, such as logical formalisms, semantic networks, frame based structures, rule-based representations, and object-oriented representations, have been proposed. By comparison, less work has been done on the intermediate step of knowledge analysis, whereby textual or verbal data elicited from expert sources are converted into a conceptual model before implementation. Furthermore, the intermediate step of knowledge analysis is important, because the result from this phase is a conceptual model of the domain, which is crucial in expert system development [lo]. Data and information obtained from manuals, textbooks, experts, and even users need to be analyzed and reformulated for representation in a system. Since the performance of an expert system depends directly on the knowledge that is incorporated, knowledge or conceptual modelling is crucial to the eventual success of the system. The analysis step needs to be studied because the belief that there is a

42

C. W. Chan. M. Johnstoni~onowled~e-Based Systems 9 (1996) 41-59

‘magical one-to-one correspondence’ between the expert’s verbal comments and the real items of knowledge in his or her head is misleading. Rather, the knowledge must be inferred from the verbal data. It has been suggested that a formal, top-down approach for guiding the translation from verbal comments to expert knowledge is crucial to building successful systems [l 11.In the knowledge acquisition research community, this process is referred to as knowledge modelling (see, for example, [ 121). KRITON [4], KEATS [13], and KADS [14] are some of the more comprehensive systems of knowledge engineering aids. While KRITON and KEATS support the conversion of the elicited information into some intermediate knowledge representation formalism, they lack an explicit underlying methodology [4]. Only KADS, more recently called CommonKADS, possesses a structured methodology for knowledge acquisition and system development [15,16]. However, the KADS methodology has been criticized for both the time spent on constructing the detailed models, and the overhead in documenting the development process [ 171. We suggest in this paper a domain neutral classification of knowledge types and inferences called the inferential model as an alternative for guiding the knowledge acquisition process. The model functions as a ‘conceptual map’ or template for identifying the items of knowledge from the elicited expertise and it serves as a framework for organizing the analyzed knowledge. On the basis of the model, we have derived the inferential modelling technique. To illustrate the use of the technique, we applied it to the construction of the conceptual model in the domain of site preparation in reforestation. Site preparation is commonly employed in forestry to improve reforestation success. The application of the proper treatment can affect the future ecology of the site significantly. SYTEPREP (System To Evaluate PRescription Effects on Productivity) is an expert system that was developed to model these reforestation decisions. In this paper we describe the process of developing the conceptual model for this system. This paper is organized as follows. Some background on conceptual modelling is given in the second section of the paper, the inferential model and the inferential modelling technique are described in the third section, and some results from the application of the technique to conceptual modelling of the site preparation domain are presented in the fourth section. In the fifth section, we comment on how the technique differs from other knowledge modelling approaches, and discuss some lessons that can be drawn from the application experience and some directions for future research.

2. Background After the problem has been defined, the first step in

developing a knowledge base is the construction of a conceptual model of the problem domain [18]. Conceptual modelling involves the analysis of knowledge acquired from human experts and the construction of a conceptual model from the elicited data. This process of knowledge acquisition for knowledge-based system development or knowledge modelling is no longer seen as a process of expertise transfer, but rather as a cooperative and communicative process between the knowledge engineer and expert in defining a model that represents a common understanding of some important aspect of a domain. Two approaches for knowledge modelling predominate in the field of knowledge acquisition: (a) problem-solving methods, and (b) domain ontology. A problem-solving method can be seen as an abstract model which provides a means of identifying, at each step, candidate actions in a sequence of actions that accomplish some task within a specific domain [ 191, while an ontology defines the vocabulary of representational terms with agreed upon definitions in human and machine readable forms [20]. Much research work has focused on problem solving methods and task definitions as the predominant organizational axis for expert systems in general and knowledge acquisition in particular. The interpretation models in the original KADS methodology have emphasized the importance of the task structure as a basis for defining the domain ontology (i.e. knowledge roles specified in the inference and task levels of the models of expertise can completely determine the items of domain knowledge to be elicited during knowledge acquisition). Specific problem solving methods such as heuristic classification [21], the cover-and-differentiate method [22], and the propose-and-revise method [23] have formed the basis for a number of automated KA tools. The approach taken in Protege I and II [24] is based on the premise that a task-specific model of the application is necessary to support the acquisition of domain knowledge. While this approach facilitates communication between the expert and knowledge engineer by suggesting a framework of items to be elicited according to an abstract task or problem solving model, it restricts the development of the domain ontology to that tailored for a particular task model or problemsolving method. If, at some later point in the knowledge engineering process, the task model is found to be unsuitable for the problem addressed, then the entire knowledge analysis, and perhaps even the elicitation process, needs to be repeated according to another task model. This is sometimes referred to as the problem of backtracking or ‘rolling back’ a model [25]. Worse yet, the error is not discovered because the developed ontology supports the task model initially, although wrongly, chosen. Ontological analysis as a knowledge modelling technique

43

C. W. Ghan. M. JohnsronjK~~o~~~led~e-Ba.~ed S~siemr 9 i 1996) 41b.59

was first introduced in [26]. Since then, other researchers have emphasized the importance of creating an ontology of a domain [20]. More recent works from the CommonKADS project echo this recognition of the importance of modelling domain knowledge. While domain knowledge was relegated to being specified during the design phase in the original KADS methodology [14], [16] acknowledged that, in the current version of CommonKADS, the ‘KADS-1 modelling methodology was weak in the support it gave for modelling the domain’. In CommonKADS, an improvement is made and application domain knowledge is explicitly modelled with a domain model, a model ontology and a domain schema [ 161. Hence, the importance of modelling application domain knowledge is evident and a knowledge axis has been added to the task axis for the support of knowledge modelling. We have previously combined the two approaches described above and proposed a domain independent classification of knowledge types called the inferential model [27,28]. We have also derived from the model a systematic technique of knowledge modelling [12,29]. In this paper, our focus is on the application of the technique in the conceptual modelling of the site reforestation domain. In the next section, we first introduce the inferential model and the associated inferential model-, ling technique.

Static component: Inference

0+ uR:

,,R:

D_vnamic component : Strategy level: This defines implicit assumptions in the domain or in the task which guide or order the invocation of goals at the task level. Task level: This invokes elements of inference and domain levels to accomplish goals and subgoals.

iI-

Domain level: 0, P 1.1

-

O,., R

P,.,.,

P I.2

P /*~I 2

P 1.3

P , I..q etc.

where 0 is an observable object, 0. is a conceptual object, R is a relation between observable objects 0, oR: is a relation between observable objects 0 and conceptual objects 0’ , “R: is a relation between conceptual objects Ot , and R,, R: and UR: are associated with their respective strength indicators S and criteria C. Domain level This has three components: objects: a set of 0 observable domain objects posited by the expert as a useful ontology of the domain, attributes: a set V of observable attributes of the objects, attribute values: a set V’of sets of symbolic, Boolean, real, or integer values, relations: a set R of relations on 0 that are partially ordered with respect to a strength indicator S which represents the relative significance of the relation according to some criterion C.

model

The inferential model consists of the same four levels of domain, inference, task, and strategy knowledge as those in the KADS expertise model [14], but the contents of the levels are different. The inference knowledge in KADS emphasizes the ‘roles’ that domain knowledge plays and canonical inferences or elementary steps in the reasoning process. However, the typology of inferences is not complete, and the labels for the inferences are not unambiguous [30]. We advocate a more generalized notion of inference which does not aim at representing a complete typology of inferences because we believe that inference depends on the properties to be preserved in particular applications (for a detailed discussion of this, see [31]). The inferential model is as follows, and it is characterized formally below.

0 0+

3. Inferential model and inferential modelling technique 3.1. Inferential

level:

The relations among objects can conceptually become a set of second level objects which has its own associated sets of attributes, values, and relations. Theoretically, the process of building higher level objects, attributes, values, and relations can continue indefinitely. However. in practice, it is more useful to view relations built on domain objects as conceptual, theoretical. abstract, unobservable, or inferred objects O+ which belong to the inference level. Inference level This level consists of a set of inference structures which may be viewed as a graph in which the vertices are domain or conceptual objects and the edges are inference relations. There are two additional considerations: ??

Inference types: There are two types of inference relation, those between observable and unobservable objects (,R:), and those between unobservable and unobservable objects (,R:). In both cases, some property that is essentially associated with the particular

44

??

C. W. Chan, M. JohnstonlKno,~,lrd~r-Based

relation in question is preserved. A property is preserved by an inference relation if and only if the relation between two objects guarantees that the second object has the property when the first one has it. Truth is the property of interest in classical logic, but the determination of which other properties are to be preserved ought to depend upon the requirements of particular applications. For example, metalinguistic properties that may be preserved include coherence, relevance, necessity, normality and abnormality of function inputs and outputs [31]. Strength of inference: A strength indicator is associated with each inference relation within a structure to represent the relative inferential significance of the relation according to some criterion such as credibility, validity, importance, or likelihood of occurrence. For example, the certainty factor is a strength indicator that represents the inferential reliability of a relation. The choice of criterion depends on the domain. The inferential relations can be partially ordered according to the criterion and the strength indicators. It is possible that, within a single domain, different criteria are relevant; hence, different partially ordered sets of inference relations may be defined which are themselves partially ordered. To correspond with the two different types of inference relations oR: and “Ri, the strength indicators and associated criteria are denoted as .S:, .Sz and .C:, ,Ci.

Task level A task is some job in the world that needs to be modelled. It consists of a set of related activities the completion of which satisfies a goal. The set of activities is operationalized by procedures which invoke domain and inference objects and relations, and describe how static entities of the two lower levels are used. Without this level of knowledge, system functions cannot be implemented. The body of task knowledge can be conceptualized as a set of task structures. Since a task is accomplished by means of a method, each method can be represented as a structure or network whereby 0, A, R, oRz and ,, R: of the domain and inference levels are invoked to accomplish an objective embodied in a task. A task structure may consist of substructures so that a task objective is accomplished by coordinating a number of subgoals. A task substructure can correspond to one or more than one inference structure(s). To accomplish a task objective, either domain independent problem solving methods or domain dependent methods that are not generalizable to other applications can be invoked. When the task structure has been clearly specified, it may be found to be implementable using a predefined problem solving method associated with the particular task in question, such as the library of task models developed in the KADS methodology. Alternatively, it is also possible that some domain independent methods such as

S~~.stm.\ 9

( 1996)

41-59

the weak search algorithms of breadth first search are sufficient to perform the task. A task objective can belong to several task modalities. For example, in the problem solving modality, a task structure describes a problem solving method, whereas in the tutorial modality, a task structure may describe an explanation. Strategy level Strategy knowledge has been defined as the knowledge used, at any time during the diagnostic process, to decide what is the most opportune choice to make, or, alternatively, to judge if it is worth executing a certain action with respect to other possible actions [32]. This definition can be generalized for nondiagnostic domains. Strategic knowledge often consists of criteria or ‘preference parameters’ [32]. It refers to the underlying or implicit assumptions which affect the trade-off decisions that the expert makes in a real world context. For example, the preference parameters of ‘cost’, ‘risk’, and ‘importance of task’ may be the criteria for prioritizing a sequence of tasks or activities. In clarifying strategic knowledge, the knowledge engineer and expert are forced to communicate about the implicit notions which explain the priorities in the ordering of the task structures derived from the nature of the problem domain. Strategic knowledge is also important in realtime domains. but we will limit our discussion to static applications in this paper. Sometimes. strategic knowledge is embedded in the ordering of activities in a task structure and it is not explicitly represented in the system. For example, in the design application, strategic knowledge consists of user specifications, which often act as criteria or constraints for partially ordering objects at the other three levels. In other words, they control the invocation of goals and subgoals at the task level, as well as objects and relations of the inference and domain levels. A detailed example will be given the fourth section. Next, we describe the inferential modelling technique, which has been developed on the basis of the inferential model. 3.2. Injbential

rnodelling technique

Our principal objective in developing the inferential model is to support the analysis and interpretation stages. and it is secondarily to provide guidelines for the elicitation stage of the knowledge acquisition process. On the basis of the inferential model, a technique of semantic analysis called the inferential modelling technique (IMT) is derived. The inferential modelling technique (IMT) is a systematic technique of cognitive modelling in which the inferential model functions as a template or conceptual map for classifying and organizing the units of knowledge embedded in the verbal or textual data elicited from

C. W. Ghan. M. JohnstonlKnotvledge-Based

(a) knowledge

elicitation ):

4

analysis

, I I

(IM&

A

)r ,

I (c) knowledge Fig. 1. Role of inferential knowledge acquisition

model

representatiob within

context

of three

45

task structure, and strategic knowledge. With the template of knowledge types in mind, the KE can use the following sequence of steps to classify the elicited units:

, ’ (b) knowledge

Systems 9 11996) 41.-59

stages

of

experts. The inferential modelling technique provides top-down guidance on the knowledge types that are required for problem solving, and it can be used in conjunction with bottom-up techniques such as content analysis, verbal protocol analysis, and discourse analysis. Bottom-up techniques do not provide guidelines on the types of knowledge that are required for problem solving in a domain. What often happens is that the types of knowledge implicit in a problem are considered domain dependent. Therefore, the individual knowledge engineer (KE) confronted with the task of analyzing expertise and constructing a conceptual model of the domain is left with his or her ability to learn about a domain as fast as possible or to his or her intuition. For example, one effort at analyzing the knowledge constructs involved in the design of business expert systems found only three relevant types: criteria, relationships, and equivocal situations or the boundary of the domain [l]. As mentioned earlier, KA is refined into the three stages of knowledge elicitation, knowledge analysis, and knowledge representation [2]. The cyclic nature of the three stages in the KA process and the role that the IMT assumes is shown in Fig. 1. The arrows indicate that a cycle may occur from the collection step (process a) to the analysis step (process b), and to the interpretation step (process c), and then back to processes b and a. Alternatively, iteration between processes a and b may occur without the necessity to proceed onto process c, or between processes b and c without the necessity to cycle back to process a, etc. In all these instances, the IMT can be useful. Specifically, the inferential model is useful for elicitation because the KE can pose questions according to the classification of objects and relations in the model. For example, when an object is identified, its properties and relations to other objects can be discussed. It can be used for knowledge analysis in that the model serves as a template to guide classification of the knowledge items elicited. A clearly analysed version of the elicited expertise can be explicitly represented. The IMT is a procedure which facilitates the development of the ‘specific categories’, including domain and conceptual objects and properties, and their values, domain and inference relations, strength factors, criteria,

. Step 1: Specify the physical objects in the domain. . Step 2: Specify the properties of objects identified in Step 1. . Step 3: Specify the values of the properties identified in Step 2. or carry out Step 4. . Step 4: Define the properties as functions or equations. . Step 5: Specify the relations associated with objects and properties identified in Steps 1 and 2 as functions or equations. 0 Step 6: Specify the partial order of the relations identified in Step 5 in terms of strength factors and criteria associated with the relations. 0 Step 7: Specify the inference relations derived from objects and properties identified in Steps 1 and 2. ?? Step 8: Specify the partial order of the inference relations identified in Step 7 in terms of strength factors and criteria associated with the relations. . Step 9: Specify the tasks in the problem. 0 Step 10: Decompose the tasks identified in Step 9 into inference structures or subtasks (which invoke units identified in Steps 1, 2, 5 and 7). . Step 11: Specify the partial order of the inference and subtask structures identified in Step 10 in terms of strength factors and criteria. . Step 12: Specify strategic knowledge in the domain. . Step 13: Specify how strategic knowledge identified in Step 12 is related to inference and task structures specified in Steps 9 and 10. 0 Step 14: Return to Step 1 until the specification of knowledge types is satisfactory to both the expert and the KE. A key feature of the IMT is that it involves iterative refinement of the conceptual model. As indicated by Step 14 of the procedure outlined above, the iterative process should eventually produce an explicit classification and organization of the knowledge units identified through content analysis. When both the KE and expert are reasonably satisfied that the conceptual model that emerges represents the problem solving expertise, they terminate the process. An explicit classification, even if

46

C. W. Chun. M. JohrtstonlKnoM,led~e-Based

it is initially ‘incorrect’, poses a question to be clarified between the KE and the expert, and it constitutes a step towards the conceptual modelling of a problem domain. It should be pointed out that the sequence of steps in the IMT does not need to be rigidly followed. In fact, the sequence is listed primarily to indicate the progression of knowledge clarification from the domain to the strategy levels, i.e. from the concrete and obvious to the implicit and less obvious. It is entirely possible that strategic knowledge or task knowledge is acquired or analysed before domain knowledge. The iterative nature of the IMT can be interpreted to mean that the sequence of steps can begin at any point and end at any point, but a suggested progression is to start at the domain level. To put it differently, the sequence of steps may not proceed from the top down like a cascading waterfall. Instead. inferential modelling can be compared to juggling, when the types of knowledge acquired or analysed are analogous to the balls dropping into the juggler’s hands. Both strategic, dynamic knowledge and properties of the domain are emphasized in the technique. In contrast to the generic task approach [33], the task taxonomy does not constitute the organizing axis of the knowledge types. Rather, our objective is to support the acquisition of both domain and task knowledge, and, in particular, the often implicit strategic knowledge. As will be discussed later, strategic considerations such as the expert’s use of measurable attributes are often tacit unless they are explicitly stated in a checklist as a type of knowledge to be elicited. The technique has been successfully used in the knowledge acquisition step in the construction of a solvent selection advisory system [27], and it has also been applied de facto to the domain of the law of negligence [29]. As will be discussed later, in the application to the reforestation domain, we used the IMT after knowledge elicitation and initial modelling as a tool for formalizing the categorization of domain knowledge.

4. SYTEPREP

system

4.1. Overview Reforestation is the term used to describe the process of replenishing forests after harvest. Proper reforestation of a harvested site often requires the application of a site preparation treatment, which can have multiple purposes: to reduce fire hazard, to facilitate planting, to promote natural restocking, to promote healthy seedling development, to reduce competition from other forms of vegetation, and/or to sanitize the site with respect to insects or disease. A site preparation treatment can have a substantial impact on plantation success and on site productivity; a poorly chosen treatment can lead to diminished long-term site productivity (for example

Systems

9 (1996) 41-59

because of soil degradation) and even to plantation failure. In addition, a proper choice of treatment can lead to direct savings, as the various treatments vary considerably in terms of their cost. The three major methods of treatment are prescribed burning, mechanical treatment (using modified agricultural machinery) and chemical treatment (which is much less commonly used and involves activities such as spraying herbicides). Treatment selection can be a complex decision and it usually requires knowledge of the ecology of the area and familiarity with the different types of site preparation treatments. A treatment affects site productivity by changing the growth ,factors that influence seedling establishment. These factors include temperature, moisture, nutrients, and the amount of vegetation competition. The interaction between the treatment and growth factors is very complex, and site preparation decisions are mostly based on qualitative information. While a certain amount of research has been done in the filed, the ecological effects of a particular site preparation treatment can only be crudely predicted for a specific site. This is because an individual effect of a treatment on a particular growth factor may increase or decrease site productivity (for the effects of prescribed burning, see, for example, [34]). Because of the lack of good models, foresters depend on knowledge which is dispersed over many sources such as planning guides and manuals (for example [35,36]), and on their expertise and prior experience. This situation has led to the proposition that the use of expert systems can be an appropriate approach to the problem. Expert system prototypes have been built for several problems in the domain of forestry. These include the planning of forestry roads [37], the dispatch of fire control resources [38], and the treatment of tree fungi [39]. A very simple demonstration of the applicability of expert systems to fire effects has also been developed [40]. A partial summary of expert systems applications in related areas is given in [41]. The possibility of developing expert systems for designing prescriptions for burning is discussed in [42]. In order to make treatment decisions, the effects of treatments on regeneration success have to be predicted in terms of forest management objectives, which include economic considerations, wildlife support, and visual and recreational concerns. The SYTEPREP system emphasizes the ‘biological appropriateness’ of a treatment, which is defined as the effects of the treatment on site productivity. The operational considerations of how to execute the treatment were not modelled. The most important measure of site productivity is the volume of timber available for re-harvesting at a given point in time. Predicting the long term effects of treatments is an extremely difficult problem [43]. Hence, plantation success at the critical stage of seedling

C. W. Ghan, M. JohnstonlK~,o~~led~e-Basrd System

establishment, typically within five years after planting or natural regeneration, was chosen as an indicator for long term productivity. However, there are no reliable quantitative models for predicting plantation success, and the decision was made to use expert systems technology to provide access to existing knowledge and experience. The approach taken was to analyze the effects of a site preparation treatment on the factors affecting tree growth on a site, which are called growth factors. These may be classified into direct and indirect types [34]. Direct factors include nutrients, moisture, soil temperature. air temperature, and the amount of direct light. Indirect factors include competing vegetation and root pathogens. Growth factors are tangible concepts that are familiar to foresters. Hence, expert knowledge was sought about treatment effects on growth factors. 4.2. Site preparation:

prescribed

burning

The complexity of the site preparation problem is demonstrated by considering prescribed burning. A typical prescribed burning treatment involves the use of a helicopter to ignite the slash (logging debris) on a site. The burn is ignited in a circular pattern to form a convection column which directs the air flows generated by the fire towards the centre of the circular pattern. Proper selection of the ignition pattern and ignition conditions keeps the burn under control and prevents fire escapes into adjacent timber. The effects of fire on a site can be described in terms of short and long term outcomes [34,44-471. In the short term, the fire results in changes to the physical, chemical and biological characteristics of the site [43]. Physical changes are related to the amount of organic matter consumed by the burn, to soil structure and porosity, to the moisture regime, and to the temperature regime. Chemical changes relate to acidity and to the total content and availability of nutrients. Biological changes include effects on fauna and on the vegetative cover. These factors interact, resulting in a complex series of changes to the site which in turn affect the long-term growth [34,43,45]. The short-term effects are understood much better than the long-term ones as demonstrated in the literature. The complexity of interactions of the various factors is demonstrated in Table 1, which shows the relationships among these factors. Consider, for example, the effects of fire on available soil moisture and nutrients. The soil moisture will interact with the available soil nutrients because the nutrient uptake by the tree will be affected by the moisture. Furthermore, fire usually changes the vegetation cover on a site because of differences in the tolerances of species to fire. Hence, vegetation control can be the primary outcome of a prescribed burn through the establishment of a ‘time window” for the tree seedlings to reach the free-to-grow stage. Depending

9 ( 1996 J 41-59

41

on the conditions and vegetation species present, burning can decrease or increase the resulting vegetation cover on a site. 4.2.1. Fire severity andjre behavior The main way in which the effect of fire can be controlled is by burning under particular moisture conditions (in the logging slash and the forest floor). Moisture conditions are affected by weather conditions, and hence by the season. For example. a spring burn will generally be less severe than a fall burn because the forest floor is still quite moist in the spring owing to snow melt ]351. The effect of fire on the ecology can be defined in terms of the impact of fire on site characteristics. Since there are several important characteristics that are affected by fire, fire severity is a multivalued concept. Thus. a fire could be considered severe in one respect and moderate in another. In our system. we use forest floor consumption as the measure of fire severity (alternatives are slash consumption and mineral soil exposure). 4.2.2. Prescribed burning decision models unddecision uids The primary decision regarding prescribed burning is that of whether fire is the best site treatment alternative for a given site, both economically and ecologically. Secondary decisions involve determining the most appropriate conditions for burning in terms of, for example, moisture indices and weather. Several models have been developed with the purpose of supporting burningrelated decisions. These include a decision analysis framework to determine whether fire is the most appropriate site preparation treatment for a specific case [40-501, a probabilistic model of the costs and risks of prescribed burning [51], an interaction matrix model to show how different characteristics of a fire prescription can substitute for each other [52], and a conceptual model to evaluate the effects of wild fire [53]. Various decision aids have been developed that make knowledge acquired by research groups available to field users. One of the earlier developed decision aids was the prescribed fire predictor/planner (PFP) [.54]. which was an attempt to facilitate the task of developing prescriptions to meet burn objectives. This decision aid is composed of a series of tables that relate weather, fuels and topography to fire severity (duff consumption, slash consumption, and mineral soil exposure). Another decision aid is a one-page key that classifies site sensitivity to fire in a particular region [55]. However, this decision aid does not adequately model the complexity of the problem. 4.2.3. Suitubiliry,for expert systems modeling The nature of the site treatment problem makes it a good candidate for the application of expert system technology. for several reasons. First. the problem is

48

C. W. Ghan. M. Johnston/Knowledge-BasedSystems 9 (1996) 41-59

Table I Effects of fire treatment Type of factor Direct growth

Factor factors

Possible effects of burning

Possible effects on tree Positive, maybe negative if south facing slope or shaderequiring species

Available

light

increased

Available

water

Decreased infiltrability, and forest floor (and usually upper soil) water holding capacity

Negative if serious summer drought on tree microsite

Increased

Positive if not excessive

Temperature regimes (air and soil)

(decreased

shading)

in receiving

areas

Increased range above ground, surface, and below ground

Decreased range at surface mineral soil exposed

Rooting

Some indirect factors

growth

substrate

Decreased

if erosional

Positive if respiration decreased at night and/or soil temperature increased Negative if too extreme for species (slope, aspect important)

at

possible

Negative

losses

Negative if significant losses occur

Seedling frost-heaved effects

due to temperature

Negative

Increased

gain

Positive if not excessive

if sediment

Decreased owing to atmospheric leaching and erosion

Competing

Increased if propagation encouraged (e.g. salal roots not burned) Decreased if propagules discouraged

Root pathogens

Positive if decreased heat stress on stem and/or increased heating of root environment Negative if frost heaving occurs

Decreased if species relies heavily on forest floor

Soil nutrients: total content vegetation

if

consumed

losses,

Negative if any nutrient below requirements Negative because competition

or

pool falls

of increased

Positive

Increased if propagation or growth encouraged (e.g. Rhizina undulata spore germination, growth encouraged by fire)

Negative

Decreased if opposite effect (i.e. only if persistent deep burning of roots occurs)

Positive

naturally complex, owing to the interaction of ecological factors. Second, no analytical models exist to explicitly predict the effects of a given treatment because good quantitative models for processes such as the effect of fire on site conditions and the effect of growth factors on plantation success do not exist. Third, owing to the variability of conditions across sites and the specificity of treatment effects by site, even when decision aids are available, treatment decisions still depend on the decision maker’s experience of similar situations. Finally, the relevant knowledge is dispersed over a variety of sources, such as guides and manuals, and among experts whose domains of expertise include soil, vegetation, fire behavior, and tree pathology. Hence, this is a typical case in which both public and private expertise exist and are used to deal with decision situations. Both interpretation, defined as inferring situation description from sensor data, and prediction, defined as inferring likely consequences of a given situation [18],

forest floor

aptly described the site preparation and specifically prescribed burning problem. Given a set of site characteristics, interpretation relates to the classification of a site by the potential effects of treatment. Prediction of the treatment effects on growth factors is necessary in order to evaluate the productivity of the site after burning, which in turn becomes a consideration in deciding whether to apply the treatment, and at which level. Clearly, an expert system that can predict outcomes can also be used as an aid for planning or ‘designing actions’. Moreover, an expert system that can predict outcomes of decisions can be used for instruction because the system can present to the user the potential outcomes of a possible decision, and familiarize the user with the information necessary for arriving at a decision. Of particular importance in this respect is the ability of the expert system to provide explanations about why certain information is required and how the inference was made.

4.3. Kno~~led~q~engineering

with site preparation decisions. At first, more effort was spent on the user interface. As the project progressed and the user interface stabilized, the emphasis shifted to knowledge base evaluation via the examination of actual scenarios suggested by the participants. The scenarios usually involved site- preparation situations that were considered difficult. The workshops led to modifications of both the user interface and the knowledge base. In the last phase of the knowledge acquisition process. the knowledge engineer demonstrated the system to groups of experts in the various subdomains of vegetation growth, soil science, and root pathogens who were not involved in the original development of the knowledge base. These experts evaluated the conceptual model and the outputs of the system for specific known test sites. These discussions resulted in further knowledge base modifications.

In this section, we describe the knowledge engineering process in order to give some background to the conceptual modelling phase of development. 4.3.1. Kno~~~ledge engineering process Many experts were consulted in the development of the SYTEPREP system [43,56] (Fig. 2 shows some sample screens of the system output). Experts were selected for their local knowledge of various aspects of site preparation treatments and tree growth, their coverage of various related organizations, and the availability of the experts over the duration of the project. The knowledge engineering process comprised four types of activities, from interviews with individual experts to workshops with groups of users and experts. These activities are described as follows: The first phase of conducting interviews with individual experts began before a conceptual model was available. and it led eventually to the construction of the conceptual model. The interviews were largely unstructured. The tools used initially were diagrams, and, later, simple prototypes were implemented using the expert system shell VP-Expert*. Through the interviews. the second author of this paper, who was also the knowledge engineer. became familiar with the problem domain. Together with the experts, he developed a common terminology to deal with the knowledge. They also focused on the limiting factor approach in constructing the conceptual model and a prototype knowledge base for the fire effects system. During the interviews a central expert was identified who was later responsible for the conceptual model and major parts of the knowledge base, and for identifying the required expertise and appropriate additional experts. The second phase of knowledge acquisition involved discussions with groups of two experts. The groups included the central expert and an expert in one of the subdomains. Since the additional expert was not familiar with the technology, the prototype system was used to explain to him the notion of an expert system. and the process of knowledge engineering. From the discussions. the two experts generated typical scenarios and added rules to the knowledge base. The rules were related to the specific domain knowledge of the additional expert. The third phase of the knowledge engineering process involved demonstration of the system in workshops for the validation of the knowledge base and the evaluation of the user interface of the system. Participants were field experts who were involved routinely

* Trademark

or WordTech

Systems

Inc.. USA

During evaluation, special attention was paid to the explanations generated by the system. The explanations were considered important because (a) they could enhance system credibility with users, (b) they summarized knowledge that had been dispersed over many sources, and (c) the explanations constituted an important feature which enabled the system to be used as an educational tool. Two key tools in the knowledge acquisition process are the conceptual model and the prototype system. The importance of the conceptual modelling phase of the knowledge-based development process was demonstrated because the majority of the knowledge engineering sessions during the first year of the project were devoted almost entirely to constructing the conceptual model of the domain. The development of a prototype expert system was a central tool in knowledge engineering. An initial prototype of the system, based on the conceptual models developed in some of the early sessions, aided the process of conceptual modelling. 4.4. Conceptual

modelling

The conceptual modelling of the system involved stages which are described below.

three

4.4.1. Process models The first step in building the conceptual model was to develop a framework to model how the treatment would affect the growth on the site. Initially. an inputtprocesss output model was used to model the problem domain. The effects of the treatment were described as a sequence of processes that converted some inputs to some outputs. In particular, four generic types of processes were identified: ??

The,fire process, which transforms

burning

prescription

C. W. Chan. M. Johnston/Knowledge-Based

Eile

View

Go-To-Module

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View

Go-To-Module

Outions

SYTEPREP

9 (1996) 41-59

Helo

Windows

Qgtions

Systems

Application

- MURPHY.FIL

Help

The north-east aspect and moderate to steep slope of this site will adversely the soil temperature regime.

affect

’ L

51

C. W. Chan. M. JohnstonlKttowledge-Based Systems 9 f 1996) 41-59

Il_LUSTRATIOW

OF

LIMITING

FRCTOR

CONCEPT

LIMITING LEVEL INDEX 81

INSIGNIFICANT

7

UERY

6

MILD

5

MDDERClTE

4

HIGH

3

UERY

'2 1 PRE-TRERTHEWT

MILU

HIGH

SEUERE EXTREME

POST-TRERTHEMT

Fig. 3. Limiting

parameters (e.g. forest floor moisture) into immediate effects (such as forest floor consumption), soil processes, in which physical and chemical processes transform the immediate effect into changes in the levels of direct and indirect growth factors, physical, chemical and biological processes, which transform indirect growth factors (e.g. competing vegetation) into changes in levels of direct growth factors (e.g. the amount of light received by seedlings), tree growth processes, which transform levels of growth factors into tree growth. Since there are no exact analytical models for these processes, expert knowledge was sought in modelling these transformations for specific site types and for specific treatments. The above framework was used to identify knowledge subdomains, including ecosystem classification, soil characteristics, fire behavior and the effect on soil characteristics, and vegetation growth. A major concern in developing the SYTEPREP system was the need to integrate knowledge obtained from various sources. which included local field experts with knowledge of different aspects of the problem, and various manuals, guides, and research papers containing knowledge formalized by experts and researchers. The process models closely resemble the physical processes, and the complex interaction among the processes render these models almost incomprehensible to the nonexpert. This complexity possibly explains the conflicts among different studies concerning the ecological effects of prescribed fire [45]. To overcome the complexity, we proceeded onto the second step in conceptual modelling by focusing on the central concept of how fire would affect just the factors that are considered to limit the growth on a given site [34,57]. In other words. we simplified the complex process by focusing on the limiting factor concept, which was adopted as the basis for developing the conceptual model of the system.

factor concept.

4.4.2. Limiting factor concept The limiting factor concept is used in considering plant growth [58]. It was originally proposed by Liebig [59]. This concept states that the growth rate of a plant species will be determined by the level of the factor that is the most limiting or the least optimal. As long as the level of the limiting factor is not improved, growth will not be affected by enhancing other factors. Fig. 3 illustrates the limiting factor concept as a barrel full of water. The volume of water in the barrel represents the level of seedling productivity. The volume of water will be determined by the height of the shortest stave of the barrel. In the barrel on the left, vegetation is the most limiting factor. Therefore, in order to improve seedling productivity, the treatment must increase the height of the vegetation stave, i.e. the limiting level of vegetation. For the barrel on the right representing the posttreatment seedling productivity, soil nutrients now constitute the most limiting factor. The limiting factor concept has been applied to tree production in forestry. Thus, the British Columbia Forest Service Site Preparation Guide [60] uses the limiting factor concept when it instructs the user to indicate whether the moisture regime or the nutrients are limiting on a site. Although the limiting factor concept is an oversimplification of reality because some factors can in fact compensate for others, it is a concept that is easy to understand and to implement in guides and in computerized systems. In the case of the site preparation expert system, it was the key to decomposing the knowledge into components related to different subproblems. The usefulness of being able to partition a problem into subproblems as a method of dealing with complex situations in expert systems has been recognized [61]. The growth limiting factors approach simplifies the problem, and yet it does not formalize the knowledge. First, it is unlikely that experts can provide accurate quantitative estimates of the effect of a treatment on each growth factor. Second. even though the effect of

C. W. Chan, M. Johnsron/Knowledge-Based

52

the limiting level on tree growth cannot be precisely quantified, experts do reach decisions regarding the effects of growth factors on tree growth success. Hence, it can be safely assumed that they possess the necessary expertise, at least in qualitative terms. To capture and represent this qualitative expertise, we proceeded onto the third stage of conceptual modelling by using the inferential modelling technique.

o A1,6 : moisture, o A,, , : nutrient level, ??

preparation

of inferential modelling technique to site domain

The inferential model proved useful in formalizing the mainly qualitative knowledge that experts possess by allowing specification of the conceptual objects that experts implicitly use in their reasoning about the site preparation problem. In developing SYTEPREP, we adopted the IMT after initial modelling and prototyping of the system. Many of the initial KA sessions were unfocused and the task objectives of the system undefined. As KA proceeded, the task objectives became more defined but they also changed over the course of the project as more subsystems that modelled different treatments and different parts of the treatment prescription development process were added. The modeling of mechanical and chemical treatments was also more complex than that of fire treatments. Buchanan et al. have suggested that expert system development processes often do not follow a waterfall model. Instead, they are more aptly described by an iterative cycle that allows backtracking to earlier development stages [lo]. In this project, we began knowledge acquisition using mainly unstructured interviews. As more knowledge was accumulated, we adopted the IMT which facilitated the structuring and organisation of expertise. The IMT enables the acquired information to be decomposed into knowledge elements, after which the organization of the knowledge components into a conceptual model can proceed. For SYTEPREP, this was important, because there was an abundance of information from diverse sources, including multiple experts. Hence, it was crucial to adopt a technique which allowed the slotting of information and the integration of different perceptions. The application of the technique formalized the types of knowledge in the domain as follows. Domain level:

The objects and their associated attributes included the following: ??

site 0, with the associated attributes of o o o o o

A,, , : climate, A,, 2 : elevation, A1,3 : latitude, A,,, : aspect (orientation), Al,, : slope,

seedling 02, with the associated attributes of o Al,, : height, o A?,? : species type, o A2,3 : shoot/root ratio,

??

4.4.3. Application

Sysrems 9 (1996) 41-59

treatment Os, with the associated attributes of 0 Aj, I o A3,2 o A3,3 o A3,4

: type3 : depth, : width, : length.

Fig. 4 illustrates the identification of some of the objects for the site preparation treatment problem. The treatment employed in this case was aerial ignition using a helicopter and driptorch. Each of the attributes of the objects was associated with a range of valid values V’.Sample values of attributes included the following: the set of real integers (e.g. for the length and width of treatments), the set of symbolic values for the various properties, e.g. the enumerated sets of types of treatments, species of seedlings, and types of forest floors, A relation R is a natural relation among objects. For example, causal relations exist among soil properties. such as that between the soil moisture content and the heat capacity of the soil. Inference

level

A limiting factor is considered a conceptual object. Seven limiting factors are used in the pretreatment classification task. Each factor reflects the interaction among a number of components, such as slope, aspect, and soil moisture content. For example, for the limiting factor due to soil moisture, a relation R exists between the site object 0, and seedling object O2 such that the attribute of the soil moisture content of the site object interacts with the drought tolerance level of the seedling object. This physical relation can in turn be considered an object, and the conceptual object 0: of species type limitation adjustment due to soil moisture can be derived from it. Hence, an inferential relation ,,R: exists between the observable object of the interaction relation between the site and seedling objects and the unobservable object of the species limiting factor adjustment due to soil moisture. Similarly, for the limiting factor due to soil temperature, the relation between the two physical attributes of the topography adjustment factor due to the site object, specifically the aspect of orientation and the slope, form the basis for the conceptual object of topography adjustment factor due to solar radiation, or the aspect A1,4 and slope A1,5 of the site object Or determine the topography

C. W. Ghan, M. JohnstonlKttowledge-Based

S~~stur~c9 (19961 41-59

53

SITE ~SEE~ILING

’

ShooVRoot Ratio

Fig. 4. Domain knowledge for SYTEPREP

adjustment factor of the solar radiation conceptual object 0:. This inference relation and its transformation into

tables and rules is shown in Fig. 5. In the rules derived from this inference relation, the topography adjustment factor in the consequent of the rule becomes a factor affecting the limiting level due to soil temperature 0:. This inferencing from the solar radiation conceptual object to the growth response due to soil temperature is shown in Fig. 6, and it is described in more detail below. In the posttreatment interpretation (prediction) task, the seven limiting factors each consist of a number of components, such as the pretreatment limitations, slope, aspect, and soil moisture content. Each limiting

factor can be seen as a conceptual object derived from the interaction relation among the relevant components which are observable parameters. In addition to considering site and seedling characteristics, attributes of the treatment are also included in the inferences drawn. For example, for the limiting factor due to soil nutrients, the type of treatment directly affects nutrient levels in the soil. Specifically, mechanical treatment and burning removes all or part of the mor humus form (which is the thin organic material on top of the mineral soil), while chemical treatment has a negligible effect on this top layer. Since nutrients are stored in this top layer, removing this top layer can adversely affect the soil nutrients. In inferential modelling terms, ‘treatment type’ A I .3

54

C. W. Chan. M. Johnston/Knowledge-Based

Systems 9 (1996) 41-59

__.

! SOLAR

lNFERENCE TABLE

RADIATION CONCEPTUAL : OBJECT imwaPh”

ST.? _. ..

. . ..:

..___L

:’

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i

<= 20

ASPECT

North

-1

East

0

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1

West

SLOPE 120 -2 0 2

0

0

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1

IF Aspect = North Slope <= 20 THEN Topography

RULE

2

Adjustment

IF Aspect = North Slope > 20 THEN Topography

AND

Factor

= -1

Factor

= -2

AND

Adjustment

Fig. 5. Sample inference relation

is an attribute of the object ‘treatment’ 03, and there is a causal relation between the treatment object 0s and the site object Or such that the attribute of the treatment type As, I determines the attribute of the nutrient levels Al, 7 of the site object 0,. The relation between the treatment and the soil in turn interacts with the seedling object. Hence the relation among the site object Ot , seedling object 02, and treatment object 0s forms the basis for the conceptual or inferred object 0: of limitation due to nutrient level. Similarly, for the limiting factor due to air temperature, the treatment type affects the property of the air temperature of the site object, which in turn interacts with the frost tolerance of the seedling object. On the basis of this interaction relation, the conceptual object 0; of limitation due to air temperature can be derived. Task level

There are two subtasks for the ecological component of the site preparation decision. The first subtask

involves using the site and seedling characteristics to estimate the present limiting levels of the seven growth factors. The objective of this subtask is primarily the classification of the site. The second subtask involves using the treatment characteristics in addition to the site and seedling characteristics in order to model the levels of the growth factors following treatment. The objective of this subtask is prediction. A number of objects, relations, and conceptual objects are involved in the performance of each of these subtasks. A description follows of how the two subtasks are intertwined. As an example, take the classification of the site according to the growth response due to soil temperature, and then the prediction of the posttreatment growth response. Fig. 5 illustrates the subtask structures for the soil temperature growth factor. The attributes of the site and seedling objects are used to estimate the attributes of the conceptual objects of limitations due to air temperature, solar radiation, heat capacity, and sensitivity to temperature. These attributes

C. W. Chan, M. Johnston/KnoM,ledge-Based Systems 9 (1996) 41-59

55

Mfect of treatment

treatment type treatment depth ,

\

.

.

/ . Fig. 6. Subtask

structures

for soil temperature

of the conceptual objects are then used to estimate the conceptual object of the growth response due to soil temperature (i.e. the pretreatment soil temperature limiting level). The treatment and site objects are then used to determine the heat transmission conceptual object which is then used to estimate the posttreatment growth response (i.e. the posttreatment limiting level). Strategy

level

A number of implicit strategies in the domain are discussed as follows. The experts focused on attributes of objects which could be reliably measured and easily obtained as the basis for deriving the limiting levels which were conceptual or unobservable parameters. An example is the conceptual object of a subzone which can easily be determined by integrating a number of site properties, with the attribute of the climate playing the major role. The determination of a subzone builds upon

growth

factor.

the site classification system that maps the entire province of British Columbia into subzones [62]. However, the existing site classification system ignores the finer variations within a site, and hence it does not coincide with what would be the treatment classification for the province. The strategic decision was made to first adopt the existing subzone or site classification system as the basis, and then add variables to it which affected the treatment selection process. Therefore, as shown in Fig. 7, the SYTEPREP subzone object is developed from the site object of Fig. 6, and it determines the base limitation for many of the growth factors. Specifically, the conceptual object of subzone base limitation, together with the topography and moisture adjustment factors. determine the pretreatment limiting level. Another example of the experts’ focus on easily and reliably measurable attributes of objects is the fact that

C. W. Ghan, M. Johnston/Knowledge-Based

56

Systems 9 11996) 41-59

species type height shoot/root ratio

treatment depth

Fig. 7. Subtask

structure

with strategy

the pH value is not used as an attribute of a site even though it could be. Since it is difficult to accurately measure pH without sophisticated instrumentation, this soil characteristic is not used as the basis for determining a limiting factor. A second implicit strategy reflected in the revised task structure which explains the evolution from Fig. 6 to Fig. 7 is the strategic simplification of dropping the seedling object as a consideration in the evaluation of how limiting the soil temperature is. The seedling object, with its associated attributes of sensitivity to temperature, has only a negligible effect on the evaluation task. Similarly, the conceptual object of heat transmission is incorporated into an inference table (st6) and not explicitly represented in Fig. 7. In addition, the pretreatment and posttreatment limiting levels are used to rank potential treatments for the site. The knowledge which guided this ranking was

level knowledge

incorporated

strategic knowledge, and it consisted of the posttreatment limiting levels. In inferential modelling terms, the posttreatment limiting levels function as the criteria whereby the treatments are prioritized when presented to the user as system output. Some criteria that are used include the cost of the treatment, the ecological or environmental repercussions of the treatment, and the hazard level of the treatment. Although the knowledge has been acquired, the incorporation of the three criteria and the presentation of system recommendations ranked according to the different criteria remain future implementation objectives.

5. Conclusions

and future work

This paper describes the inferential model, which can be used as a domain-independent template of the types of

C. IV. Chart. M. JohnstonlKtlou,led~~-Based Systems 9 i 1996) 4lb.59

knowledge and inferences for the clarification of application domain knowledge. It offers a solution to the dilemma posed in [25], i.e. that ‘some domain knowledge needs to be elicited before the abstract problem solving model can be established, but at the same time directed elicitation of domain knowledge requires an abstract model’. Instead of adopting a predefined task or problem solving method as the ‘abstract model’, we proposed the use of a domain independent classification scheme of knowledge types and inferences as the ‘abstract model’. In inferential modelling, we aim to integrate the domain and task aspects of knowledge and to adopt a problem solving method only when knowledge analysis clearly supports its use. The inferential modelling approach differs from other knowledge modelling approaches in two important ways. Firstly, it differs from the task oriented modelling approaches which rely on the task structure as the organizational axis of expertise. For example, Steel’s model-based architecture relies on the task structure as the ‘backbone’ of expertise [63]. A similar emphasis on the task structure as the foundation of expertise is echoed in the approaches of the problem solving methods [ 141, reported in [ 191, the original KADs methodology and generic task research [33]. In contrast, in the inferential model, the emphasis is ~ZOZ on the task structure; it is distributed to all four levels of knowledge. In inferential modelling, the clarification of application domain knowledge is as important as the specification of task objectives and associated methods. Hence, both the domain ontology and the task-specific information are emphasized. Secondly, the approach differs from the modelling approach of the CommonKADS methodology, in which problem solving methods and strategic knowledge are considered ‘metaapplications’. Both of these are called problem solving knowledge [ 15,161. In inferential modelling, they are both integral aspects of the application domain characteristics. Our approach does not prevent the knowledge engineer from adopting some predefined problem solving method for accomplishing a specified task. while it ensures that both strategic knowledge and problem solving methods are intrinsic characteristics of the domain. We believe that this approach offers its users the advantage that properties of the problem domain drive the elicitation and analysis process. and the choice of the problem solving method and task is made after detailed characterization of the domain, thereby avoiding the problem of ‘backtracking a model’. From the experience of using the inferential modelling technique for conceptual modelling of the SYTEPREP system, we found that the categorization of knowledge types is sufficient to analyse most items of knowledge in the SYTEPREP domain. The inference relations between objects are often more complex. To model the

57

complexity, we have added explanatory knowledge to the inference relation. Hence, an inferential relation between two objects needs a more detailed representation than a simple line or arrow connecting two entities. In this project, we attached a diamond to the single line in the arrow, which can be expanded into a table that relates attributes from two different objects. The table can then be translated into a set of rules for the related attributes. This representation scheme documents the trend of variation among the attributes in the table and the set of rules represents the core of expertise in the domain (see Fig. 5). The classification scheme which the inferential model proposes has been useful in that the knowledge engineer is forced into thinking in terms of objects and attributes. Once an object has been identified. he or she also knows that it must be related to other objects. Thinking in such terms facilitates the transition to formal representation schemes which are implementable. The classification scheme is also useful in that it forces the decomposition of a problem domain into its knowledge elements. While the progression from domain to strategic types of knowledge may not be followed precisely, the checklist ensures that all the different types of knowledge that are possible in a domain are considered. In addition to providing a methodology for constructing the conceptual model, the inferential model also facilitates the documentation of the knowledge analysis process without much overhead. The KE in this project spent about 4 man hours spread over five sessions learning about the inferential model. Since the knowledge types are generic, the model. once learned can be applied to diverse domains. A weakness in the model is that the properties being preserved in an inference relation are not explicitly described. Inferential relations in a domain in fact preserve a variety of properties, but this level of detail is not included in the present analysis effort. We shall invcstigate the issue of inference as preservation in the future. In addition, we plan to continue applying the inferential model to diverse domains in order to test the comprehensiveness of the classification of knowledge types. We are currently working on including a temporal dimension to the model so that it can be used for modelling dynamic domains. In terms of implementation, we are expanding the SYTEPREP system by incorporating additional parameters into the prediction task. Instead of simply recommending a treatment type based on the input site and seedling characteristics, we shall include considerations of harvest regimes in the system. SYTEPREP is also being extended to incorporate more tasks. It is expected that more inference structures, which essentially make up the knowledge base, will be developed to support the new tasks. These extensions to the system will be reported in future publications.

58

C. W. Chart, M. Johnston/Knowledge-Based

Acknowledgements

The first author is grateful for the generous support of a research grant from the Natural Sciences and Engineering Research Council of Canada.

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