Rule-based and case-based reasoning are two popular approaches used in intelligent systems. Rules usually represent general knowledge, whereas cases encompass knowledge accumulated from specific (specialized) situations. Each approach has advantages and disadvantages, which are proved to be complementary to a large degree. So, it is well justified to combine rules and cases to produce effective hybrid approaches, surpassing the disadvantages of each component method. In this paper, we first present advantages and disadvantages of rule-based and case-based reasoning and show that they are complementary. We then discuss the deficiencies of existing categorization schemes for integrations of rule-based and case-based representations. To deal with these deficiencies, we introduce a new categorization scheme. Finally, we briefly present representative approaches for the final categories of our scheme.

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Categorizing approaches combining rule‐based and case‐based reasoning



The social demands for third-party logistics (3PL) are further developing the model of supply chain management. The analysis of an effective approach for 3PL service supplier evaluation is given in terms of its direct relation to the operational efficiency and benefit of the service-demanding enterprise as well as its supply chain management. An analysis of the traditional academic theoretical results and practical methods for 3PL supplier selection indicates the deficiencies of this approach. In this paper, a case-based reasoning model framework is postulated for a 3PL evaluation and selection system. This work further expands upon the theoretical basis of this system and its reasoning process by discussing the advantages and practical value of this framework.

A model of a decision support system based on case-based reasoning for third-party logistics evaluation

The traditional approach to the development of knowledge-based systems (KBS) has been rule-based, where heuristic knowledge is encoded in a set of production rules. A rule-based reasoning (RBR) system needs a well constructed domain theory as its reasoning basis, and it does not make substantial use of the knowledge embedded in previous cases. An RBR system performs relatively well in a knowledge-rich application environment. Although its capability may be limited when previous experiences are not a good representation of the whole population, a case-based reasoning (CBR) system is capable of using past experiences as problem solving tools, therefore, it is appropriate for an experience-rich domain. In recent years, both RBR and CBR have emerged as important and complementary reasoning methodologies in artificial intelligence. For problem solving in AIDS intervention and prevention, it is useful to integrate RBR and CBR. In this paper, a hybrid KBS which integrates a deductive RBR system and an inductive CBR system is proposed to assess AIDS-risky behaviors.

An integrated rule- and case-based approach to AIDS initial assessment.

Case-based reasoning solves a problem by retrieving from its case memory a solution which has solved a similar problem in the past, and then adapting the solution to the problem. Recent work in knowledge-based systems in AIDS initial assessment (AIA) reflects a growing interest in the case-based paradigm. The reason is that AIA experts rely heavily on memory of previous cases when assessing AIDS-risky behaviors. The paper describes a case-based system that uses experience in the form of previous cases to assess AIDS-risky behaviors. An important feature of the system is that fuzzy mathematical algorithms are used to retrieve previous cases and determine the best matching case. The paper concludes with a discussion of future research.

Case-based reasoning for AIDS initial assessment

As modern business functions become more complex and knowledge-intensive, with increasing demands for quality services, there is an emerging trend for organisations to develop and deploy intelligent knowledge-based systems for mission-critical operations. Some of the challenges in successfully implementing this breed of systems depend on how well the intelligent system is integrated with conventional existing information systems and workflow, and the quality of the intelligent system itself. Developing quality expert systems lies in the effective modelling of cognitive processes of human experts and representation of various forms of related knowledge in a domain. An integrated intelligent system called the Intelligent Help Desk Facilitator (IHDF), has been developed for computer and network fault management. The system, which comprises various modules including an expert system, is successfully deployed in a problem response help desk environment of a local bank. This paper describes a cognitive-driven approach to the development of the expert system based on a hybrid knowledge representation and reasoning strategy. The approach incorporates a hybrid case-based reasoning (CBR) framework of techniques which include case memory organisation structures (discrimination networks and shared-featured networks), case indexing and retrieval schemes (fuzzy character-matching, nearest-neighbour similarity matching and knowledge-guided indexing); and an interactive and incremental style of reasoning. The paper discusses the design and implementation of the expert system component of IHDF and illustrates the appropriateness of the hybrid architecture for problem resolution and diagnostic types of applications.

An integrated case-based reasoning approach for intelligent help desk fault management

A case-based reasoning system (CBRS) is appropriate for an experience-rich domain, while a rule-based system performs reasonably well in a knowledge-rich application environment. A CBRS mainly uses past experiences as problem-solving tools, and therefore its capability can be limited when previous experiences are not a good representation of the whole population. On the other hand, a rule-based reasoning system needs a well constructed domain theory as its reasoning basis, and it does not make use of the knowledge embedded in past experiences.

In the paper, a multiagent cooperative reasoning system (MCRS), which integrates an inductive-reasoning agent (case-based reasoning system) and a deductive-reasoning agent (rule-based reasoning system), is proposed to solve problems through the cooperation of both agents. The architecture and the inference mechanism of an MCRS are discussed in this paper. A personnel-evaluation support system, based on the proposed MCRS architecture and implemented in prolog, is presented to demonstrate the feasibility of using the MCRS to solve problems in a knowledge-poor and experience-poor domain.

R. T. H. Chi

Papers

Reasoning by coordination: an integration of case-based and rule-based reasoning systems