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IEEE TRANSACTIONS ON ENGINEEIUNG MANAGEMENT, VOL. 37. NO. 4, NOVEMBER 1990 ~ 277 Toward Successful Implementation of Knowledge-Based Systems: Expert Systems vs. Knowledge Sharing Systems Abstract-Knowledge based systems are categorized as expert systems and knowledge-sharing systems in terms of knowledge transfer. Knowl- edge sharing systems are applicable to rather ill-structured management fields in which few expert systems have been developed. Some different strategies are presented for successful implementation of these two types of knowledge-based systems. 1. INTRODUCTION NOWLEDGE engineering was first proposed in 1977 K17i as the art of artificial intelligence (AI) that designs and builds knowledge-based systems (or expert systems). The advent of knowledge engineering was one of the most impor- tant epochs in computer science/engineering history because it has led to significant expansion of computer application fields. Major research in AI, before the emergence of knowl- edge engineering, had concentrated on exploring general methods for problem solving, and for this reason was said to have produced no major breakthroughs. Knowledge engineer- ing, on the other hand, paid special attention to domain specialists’ knowledge and incorporated it into computer systems as knowledge bases for solving real-world problems. (A system built by knowledge engineering is usually called an expert system because domain specialists are also consid- ered experts.) Today, knowledge-based systems are emerging in practice. However, there still remain many fields in which few knowl- edge-based systems have been successfully implemented. Typical examples are management fields, in which the neces- sity of using AI has recently been stress [39]. The main reasons for few knowledge-based systems (or expert systems) in management fields are that the tasks in these fields are rather broad in scope and somewhat ill-structured and that system users are experts rather than novices. In fact, a basic premise of expert systems has been that knowledge of a single (or very few) expert in a rather narrow area should be input in a knowledge base so that many novice users can use it [24], [41]. It is therefore necessary to establish a new area, Manuscript received October 16, 1989; revised February 20, 1990. The review of this paper was processed hy Editor D. F. Kocaoglu. The author is with Hitachi, Ltd., Advanced Research Laboratory, Ha- toyama, Saitama 350-03, Japan, and a Visiting Professor at Portland State University, Engineering Management Program, P.O. Box 751, Portland, OR 97207. IEEE Log Number 9037557. engineering/technology management for knowledge-based systems, in order that such systems can be more widely and effectively implemented. It also seems timely to establish an engineering/technology management area for knowledge-based systems. This is largely because we are rapidly advancing into an informa- tion-intensive society in which knowledge obtained through experience by domain specialists will increase its relative value, while almost all other data will be easily accessible through worldwide networks and a number of large data bases. Another reason is that in order to establish such a new area, we can now make the most use of considerable experi- ence in developing knowledge-based systems that have been accumulated for the past ten years. In addition, the necessity of establishing related areas such as information engineering [38] and also knowledge management [3] has recently been presented from an academic point of view. The purpose of this paper is to present strategies for successful implementation of knowledge-based systems as an approach toward establishing engineerring/technology man- agement for knowledge-based systems. These strategies are primarily based on the authors’ ten years of research, devel- opment, and implementation of knowledge-based systems for project risk management [26] - [33]. Existing strategies and suggestions in common textbooks and articles for knwledge- based system development have mostly been presented from the viewpoint of researchers and developers. They have indicated how existing AI or knowledge engineering tech- niques should be smoothly applied to actual problems [5], [ 161, [24], [40] - [42]. However, this paper emphasizes users’ points of view because their evaluation is a crucial factor in real applications. This paper first discusses the crucial role of the knowledge flow (from knowledge suppliers through knowledge-based systems to system users) in successful implementation of knowledge-based systems. This leads to a presentation of two paradigms (i.e., consulting paradigm and knowledge sharing paradigm) in which knowledge-based systems should be de- veloped and used, followed by identification of major imple- mentation requirements for those two paradigms. Some strategies for successful implementation of knowledge-based systems are then presented for expert systems that use con- sulting paradigms and knowledge sharing systems that use knowledge sharing paradigms. 0018-9391/90/1100-0277$01.00 O 1990 IEEE

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Page 1: Toward successful implementation of knowledge-based systems: expert systems versus knowledge sharing systems

IEEE TRANSACTIONS ON ENGINEEIUNG MANAGEMENT, VOL. 37. NO. 4, NOVEMBER 1990

~

277

Toward Successful Implementation of Knowledge-Based Systems: Expert

Systems vs. Knowledge Sharing Systems

Abstract-Knowledge based systems are categorized as expert systems and knowledge-sharing systems in terms of knowledge transfer. Knowl- edge sharing systems are applicable to rather ill-structured management fields in which few expert systems have been developed. Some different strategies are presented for successful implementation of these two types of knowledge-based systems.

1. INTRODUCTION NOWLEDGE engineering was first proposed in 1977

K 1 7 i as the art of artificial intelligence (AI) that designs and builds knowledge-based systems (or expert systems). The advent of knowledge engineering was one of the most impor- tant epochs in computer science /engineering history because it has led to significant expansion of computer application fields. Major research in AI, before the emergence of knowl- edge engineering, had concentrated on exploring general methods for problem solving, and for this reason was said to have produced no major breakthroughs. Knowledge engineer- ing, on the other hand, paid special attention to domain specialists’ knowledge and incorporated it into computer systems as knowledge bases for solving real-world problems. (A system built by knowledge engineering is usually called an expert system because domain specialists are also consid- ered experts.)

Today, knowledge-based systems are emerging in practice. However, there still remain many fields in which few knowl- edge-based systems have been successfully implemented. Typical examples are management fields, in which the neces- sity of using AI has recently been stress [39]. The main reasons for few knowledge-based systems (or expert systems) in management fields are that the tasks in these fields are rather broad in scope and somewhat ill-structured and that system users are experts rather than novices. In fact, a basic premise of expert systems has been that knowledge of a single (or very few) expert in a rather narrow area should be input in a knowledge base so that many novice users can use it [24], [41]. It is therefore necessary to establish a new area,

Manuscript received October 16, 1989; revised February 20, 1990. The review of this paper was processed hy Editor D. F. Kocaoglu.

The author is with Hitachi, Ltd., Advanced Research Laboratory, Ha- toyama, Saitama 350-03, Japan, and a Visiting Professor at Portland State University, Engineering Management Program, P.O. Box 751, Portland, OR 97207.

IEEE Log Number 9037557.

engineering/technology management for knowledge-based systems, in order that such systems can be more widely and effectively implemented.

It also seems timely to establish an engineering/technology management area for knowledge-based systems. This is largely because we are rapidly advancing into an informa- tion-intensive society in which knowledge obtained through experience by domain specialists will increase its relative value, while almost all other data will be easily accessible through worldwide networks and a number of large data bases. Another reason is that in order to establish such a new area, we can now make the most use of considerable experi- ence in developing knowledge-based systems that have been accumulated for the past ten years. In addition, the necessity of establishing related areas such as information engineering [38] and also knowledge management [3] has recently been presented from an academic point of view.

The purpose of this paper is to present strategies for successful implementation of knowledge-based systems as an approach toward establishing engineerring/technology man- agement for knowledge-based systems. These strategies are primarily based on the authors’ ten years of research, devel- opment, and implementation of knowledge-based systems for project risk management [26] - [33]. Existing strategies and suggestions in common textbooks and articles for knwledge- based system development have mostly been presented from the viewpoint of researchers and developers. They have indicated how existing AI or knowledge engineering tech- niques should be smoothly applied to actual problems [ 5 ] , [ 161, [24], [40] - [42]. However, this paper emphasizes users’ points of view because their evaluation is a crucial factor in real applications.

This paper first discusses the crucial role of the knowledge flow (from knowledge suppliers through knowledge-based systems to system users) in successful implementation of knowledge-based systems. This leads to a presentation of two paradigms (i.e., consulting paradigm and knowledge sharing paradigm) in which knowledge-based systems should be de- veloped and used, followed by identification of major imple- mentation requirements for those two paradigms. Some strategies for successful implementation of knowledge-based systems are then presented for expert systems that use con- sulting paradigms and knowledge sharing systems that use knowledge sharing paradigms.

0018-9391/90/1100-0277$01.00 O 1990 IEEE

Page 2: Toward successful implementation of knowledge-based systems: expert systems versus knowledge sharing systems

278 IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT, VOL. 37, NO. 4, NOVEMBER 1990

11. EXPERT SYSTEMS AND KNOWLEDGE SHARING SYSTEMS

A . Knowledge Transfer (KT) Technology transfer (TT) generally means the flow of new

technologies from research to production, or from research to practical application [9], [37], [ 1 11. Consequently, it may be quite natural to consider the transfer of AI and knowledge technologies from researchers (or AI suppliers), through knowledge-based system developers (or vendors), to knowl- edge-based systems as a crucial factor in successful knowl- edge-based system implementation [23], [25] which seeins to have formed the basis of a lot of implementation suggestions

However, the primary concern of knowledge-based system users should be whether the systems can provide useful knowledge for efficient problem solving and not merely whether such systems contain new AI technologies. The author wants to stress this user point of view because success or failure of systems largely depends on user evaluations as has been shown by the extensive research on the implementa- tion of management science and information systems for the past 20 years [14], [20]. Thus, issues regarding how knowl- edge-based systems are emerging in practice should be dis- cussed from the users’ point of view, that is, whether useful knowledge can be provided.

Therefore, instead of TT, we should pay more attention to knowledge transfer (KT) as a key to successful application of knowledge-based systems. Knowledge transfer has recently been defined by the author as the flow of knowledge from knowledge suppliers (through knowledge-based systems) to users [28]. The relation between TT and KT is shown in Fig. 1. TT moves from the bottom to the top, whereas KT moves from left to right. The dotted line represents the movement of needs feedback from users to vendors/researchers and knowledge suppliers.

Regarding the evaluation of knowledge-based systems, there have been many studies on verification, validation, credibility, assessment, and evaluation. Verification is de- fined as ‘‘the demonstration of the consistency, completeness and correctness of the software” [4] which is aimed at eliminating errors in the system. “Validation is the determi- nation of the correctness of the software with respect to user requirements” [4], [34]. An issue of credibility is the extent to which a model is credible to its users [6]. Assessment includes the adequacy and efficiency of the hardware, the quality of the implementation, and the documentation of the system [35]. Evaluation concentrates on the worth or benefit of the system [36]. Traditionally, computer professionals have been concerned with verification and validation; model building professionals, such as operations research scientists, have concentrated on validation and credibility. On the other hand, users and project sponsors are usually concerned with assessment and evaluation [36]. This paper focuses on evalu- ation from the user’s point of view, especially from KT, thereby approaching a new area: engineering /technology management for knowledge-based systems.

The following sections discuss two paradigms for knowl-

[161, ~ 4 1 , ~411.

I I I I I I I

I) Technology transfer 4 Knowledge transfer --3 Needs feedback

Fig. 1 . Relation between technology transfer and knowledge transfer in knowledge-based systems [29].

edge transfer: 1) a consulting paradigm and 2) a knowledge sharing paradigm. The emphasis is placed on the introduction of the knowledge sharing paradigm and the identification of different implementation requirements for two systems using consulting and knowledge sharing paradigms.

B. Expert Systems Consulting Paradigm: Expert systems may be character-

ized by the following three features: 1) use of a knowledge base that consists of domain specialists’ knowledge (e.g., rules generated on the basis of their experience), 2) use of an inference engine (mechanism) that logically draws new con- clusions from given facts, and 3) an attempt to solve real- world problems as effectively as domain specialists do.

Although some people have recently developed a wide variety of expert systems [22], using an expert system pro- gramming style that permits highly modular knowledge rep- resentation, and have incorporated these systems into large automated systems in engineering or manufacturing fields, this paper focuses on a major purpose of expert systems in terms of KT, i.e., to help novice users solve real-world problems by poviding knowledge from knowledge bases con- sisting of expert knowledge. Thus, it is appropriate to assign the term “consulting paradigm” to expert systems. The framework of this paradigm is diagrammed in Fig. 2. Typi- cally, one expert (or sometimes several experts) provides knowledge and many other novice users utilize it.

Based on this paradigm, there have been many expert system applications in various fields [22], [40], [41]. Some typical examples are medical expert systems, chemical expert systems, or plant diagnosis expert systems where very few experts provide knowledge and many users such as doctors, chemists or engineers can utilize the knowledge as if they were consulting with experts.

Implementation Requirements: There are extensive pub-

Page 3: Toward successful implementation of knowledge-based systems: expert systems versus knowledge sharing systems

NIWA: KNOWLEDGE-BASED SYSTEMS: EXPERT SYSTEMS VERSUS KNOWLEDGE S H A d N G SYSTEMS

@+

(Expert )

Knowledge base

( Novice ) Fig. 2. Consulting paradigm.

lications that discuss implementation requirements for expert systems [16], [41], [42]. These mostly have been proposed from the system developer’s point of view, i.e., how expert system techniques should be applied to actual problems.

However, this paper concentrates on implementation re- quirements in terms of KT, especially from the user’s point of view. Such requirements include the following two items:

1) Knowledge consistency in the knowledge acquisition phase. 2) Explanation ability in the knowledge utilization phase.

Consultation using expert systems should be based on consistent knowledge. Without consistency, most novice users cannot rely on such systems and will not want to continue using them. In the implementation of expert systems, knowl- edge consistency is especially important in the knowledge acquisition phase. For example, if there are multiple knowl- edge suppliers (although one expert is usually desirable as a knowledge supplier for each problem domain), it is necessary to achieve consistency in their knowledge.

Since users are assumed to be novices in the consulting paradigm, it is necessary for expert systems to provide them with knowledge in a very understandable manner. This corre- sponds to the second requirement. Inference seems to have succeeded in attaining this. A simple example of inference is that both (A) and (if A then B) result in (B). This is logical or, in a sense, mechanical, which can easily be understood by novices [ 151. The explanation function of backward reason- ing in rule-based systems has also been successfully used for this purpose by demonstrating how pieces of knowledge (e.g., rules) are used to reach conclusions.

C. Knowledge Sharing Systems Disadvantages of Expert Systems in Management Fields:

The author developed an ordinary expert system for large project risk management and identified some disadvantages of applying expert systems to management fields [32]. These disadvantages include: 1) difficult knowledge acquisition and 2) limited system functions.

Knowledge acquisition from experts may generally be the biggest problem in knowledge engineering. There have been various AI R&D studies such as investigation of knowledge representation schemes and languages that help ease knowl- edge acquisition, development of efficient maintenance tools for a knowledge base, exploration of automatic acquisition tools, and implementation of effective interview methods with

In addition to the difficulties of knowledge acquisition from experts 111, 121, [lo], 1181.

Knowledge base

279

( Suppliers = Users) Fig. 3. Knowledge sharing paradigm.

experts that have been widely discussed, the point to be stressed here is a fundamental problem, i.e., we cannot identify experts as knowledge suppliers. This is because expertise in management fields is generally decentralized among many (virtually all) managers in various positions.

The second disadvantage, limited system functions, is de- rived from the fact that system users are not novices; instead they are considered average managers. As they become accustomed to the ordinary expert system, they begin to find that the system answers are too limited or too predictable. This is because expert systems use inference mechanisms, which result in the answers from expert systems being within the set that consists of initial knowledge and knowledge that is obtained by the inference processes. However, human managers, unlike expert systems, do not always reason by using inference mechanisms [15]. For example, they can easily perform such flexible knowledge utilization as knowl- edge association, which connects different pieces of knowl- edge that were not connected in advance by inference or various network chains when the knowledge base was imple- mented [27], [31]. In other words, they wanted to be in- formed of knowledge from the knowledge base in different situations that could not have been anticipated when the knowledge base was implemented.

Knowledge Sharing Paradigm: The above discussion shows that in management fields expertise is generally decen- tralized and hence managers are considered average rather than expert managers or novice managers. This indicates the paradigm mismatch between management fields and expert systems (consulting paradigm) that require experts (as knowl- edge suppliers) and novices (as system users). As for the flow of knowledge in management fields, we also found that managers very often want to know other managers’ experi- ences, which means that knowledge transfer among managers plays an important role.

Thus, a knowledge sharing paradigm is proposed. The essence of the knowledge sharing paradigm is that knowledge suppliers are the same set of system users who use the knowledge base, as shown in Fig. 3. The primary purpose of this knowlege sharing system is for users to share and use knowledge that they have provided. In other words, system users share knowledge among themselves through knowledge bases.

The knowledge sharing paradigm may be especially effec- tive for application to knowledge bases in such areas as management fields that consist of several parts corresponding

Page 4: Toward successful implementation of knowledge-based systems: expert systems versus knowledge sharing systems

IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT, VOL. 37, NO. 4, NOVEMBER 1990 280

to different organizations. This is because, for such fields, it becomes more and more desirable to use as many pieces of knowledge as possible to solve today’s complicated prob- lems, even if they are not related to one another by inference chains when they were input into the knowledge base.

Implementation Requirements: Corresponding to the implementation requirements for expert systems that were determined in the previous section, the following are some of the requirements that are necessary for the implementation of knowledge sharing systems that use the knowledge sharing paradigm.

1) A variety of knowledge in the knowledge acquisition phase. 2) Knowledge association ability in the knowledge utilization phase.

A variety of knowledge should be collected and stored in knowledge sharing systems because this is what users want in areas in which expertise is decentralized such as management fields.

Since a variety of knowledge from many knowledge sup- pliers has been stored and since there are many independent pieces of knowledge (non-logically related knowledge) in knowledge sharing systems, it is necessary for knowledge sharing systems to connect and provide related knowledge in the knowledge utilization phase. This corresponds to the second requirement, knowledge association ability.

It is very difficult to handle knowledge association by existing information retrieval, by analogical reasoning, or by case-based reasoning. A thesaurus or synonym dictionary (e.g., [43]) can be used for retrieval of association words such as fun and amusement, and white and nurse. However, this retrieval method cannot be applied to “knowledge asso- ciation” because knowledge is generally expressed not in words but in clauses, phrases, or sentences. Analogical rea- soning that exploits past experience focuses on the similarity between new and past situations [U]. The primary difficulties in applying analogical reasoning to knowledge association involve two factors: 1) there are various similarities that depend upon purposes and circumstances that cannot be easily foreseen in advance, and 2) there are other relevant factors, in addition to similarity, between new and past situations. Case-based reasoning [ 131, [21] uses memory of relevant past cases (experiences) to interpret or to solve a new problem case. Key technical elements involve cases and past experiences (case memory), ways to retrieve them (inde- xing) and assess their relevance, similarity, or difference, as well as techniques to modify past experiences. Although indexing is a basis of this method, it is practically impossible to select appropriate indices in order to retrieve relevant past experience in management fields because the tasks in these fields are rather broad in scope and somewhat ill-structured (e.g., compared with cooking problems that Hammond [21] uses as case studies).

111. STRATEGIES FOR SUCCESSFUL IMPLEMENTATION

This section presents some suggestions for implementation of expert systems and knowledge sharing systems. It does not

cover all aspects of implementations issues; instead it concen- trates on some crucial issues. Regarding these issues, imple- mentation suggestions are presented to accomplish the re- quirements that were determined in the previous section as well as some other common requirements.

A . Issues The following six major issues are involved in the imple-

mentation of knowledge-based systems. These issues are determined by reorganizing several important studies [8], [16], [41] as well as the author’s findings [31].

Issues in the System Planning Phase 1) Problem Selection: Selecting suitable problems or do-

mains is one of the most important tasks for successful application of knowledge-based systems.

2) Knowledge Supplier Collaboration: The most distin- guished feature in knowledge-based systems (rather than other computer systems) is that they cannot be successfully implemented without the collaboration of knowledge suppli- ers. Issues in the System Development Phase

3) Knowledge Engineering Techniques: Many tech- niques and tools have been developed for knowledge-based system development [7], [12], [19], [22], [41]. Conse- quently, it is often difficult to use suitable techniques.

4) User Commitment: Users are sometimes reluctant to use systems that they have not created, which is well known as the NIH (not invented here) problem. Therefore, it is important for users to think that they have developed such systems.

Issues in the System Maintenance Phase 5) Knowledge Updating: Since knowledge is the main

aspect of knowledge-based systems, knowledge updating is inevitable for continuous usage of such systems.

6) System Improvement: Since knowledge-based systems use new technologies, repetition of the cycle of system development, usage, and improvement is required for the system to reach a workable level that satisfies users.

B. Strategies for Expert System Implementation Strategies for successful implementation of expert systems

are summarized in Fig. 4. Regarding the six issues, sugges- tions are presented to achieve implementation requirements identified in the previous section as well as common require- ments for knowledge-based systems.

System Planning Phase 1) Suggestions for Issue 1, Problem Selection: As a

fundamental condition for expert system planning, it is neces- sary to determine application domains in which experts can be identified as knowledge suppliers. (This is very difficult for management fields in which expertise is decentralized.) It is then recommended that domains be narrowed so that a few (one is better) experts are identified in order to attain knowl- edge consistency (Requirement 1). This conicides with a basic premise of pexpert systems, i.e., focus on a narrow area [24].