Maintenance Of Knowledge Based Systems Research Articles

A Survey on State-of-the-Art Knowledge-based System Development and Issues

The human brain can store countless folds of knowledge. Still, we cannot fully utilize a single human brain to solve a specific problem. Knowledge-based systems (KBSs) are computer programs specifically developed to perform problem solving like human experts. These systems effectively expand efficiency and flawlessly solve problems in various fields. The structure of a KBS can be divided into five standard components, which are described in detail. The component used for storage is called a knowledgebase, while an inference engine is a software module that processes the knowledge stored in the knowledgebase. Different tools, shells and programming languages can be used to develop and utilize KBSs. The most popular development languages are List Programming (Lisp), Prolog, Java Expert Systems Shell (JESS) and C Language Integrated Production System (CLIPS). This paper highlights various known issues in the phases of the development, deployment and maintenance of KBSs. Developers, as well as end users, may face various issues in the life cycle of these systems. The key personalities involved in the development and maintenance of KBSs are knowledge engineers and domain knowledge experts. Most issues are related to the experts’ availability, their behavior and long-term cooperation.

A refinement framework to support validation and maintenance of knowledge-based systems

In this article, we argue that a refinement tool capable of assisting the maintainer of a Knowledge-Based System (KBS) with updating and upgrading of its knowledge base can substantially expand the scope of his activities and prolong the life of the system. We show that refinement tasks involved in KBS maintenance are similar to those taking place at the development stage, and thus a uniform refinement framework can be designed to support both activities. After defining refinement tasks expected to take place at different stages of a KBS's life cycle, we present a refinement framework capable of supporting them. It utilizes a small number of domain-independent heuristics to search for possible rule revisions which are expected to eliminate errors detected during KBS validation. An extended example is given to illustrate how different refinement tasks are carried out.

Goal-directed reasoning with ACE-SSM

The goal of knowledge-based systems (KBSs) is not only to produce a solution to a problem that these systems face but also to construct, implicitly or explicitly, a situation-specific model (SSM) that explicates the rationale behind that solution. This paper focuses on how KBSs can benefit from the availability of explicit goal knowledge that reflects the underlying structure (ontology) of SSMs constructed for an application task. It first shows how goal knowledge can be captured. Then, it explains how ACE-SSM-an architecture for constructing explicit SSMs-uses this knowledge to direct the construction of explicit SSMs. Finally, it discusses benefits that KBSs can derive from the availability of explicit SSMs and their underlying goal knowledge. Some of these benefits pertain to ways to simplify the construction and maintenance of KBSs through reuse, while others relate to ways to endow KBSs with more robust problem solving and explanation capabilities. These benefits are illustrated using concrete examples.

ENHANCING THE SOFTWARE LIFE CYCLE OF KNOWLEDGE-BASED SYSTEMS USING A TASK-BASED SPECIFICATION METHODOLOGY

Several methodologies have been developed to enhance the software life cycle of knowledge-based systems by emphasizing on the use of both prototypes and specifications. However, these methodologies focus on the development phase of knowledge-based systems. The roles of prototypes and specifications in the maintenance phase has not been fully explored. Because a suitable problem specification for a knowledge-based system is often difficult to acquire, validating changes to non-executable solution specification during the maintenance phase can be a problem. To address this, we propose an alternative paradigm in which the prototype complements the specification throughout the life cycle. The traceability between them is facilitated by organizing both types of artifacts using a common functional decomposition structure. Based on our task-based specification methodology (TBSM), we have also developed a knowledge engineering tool (called TAME) to facilitate the acquisition and the organization of the specification and the prototype. The proposed methodology and the tool together can thus enhance the verification, validation, and the maintenance of knowledge-based systems through their life cycles.

An Assessment of Tools for Building Large Knowledge-Based Systems

A number of tools that support the development, execution, and maintenance of knowledge-based systems are marketed commercially. Many of these tools, however, are designed for applications that can be executed on personal computers and are not suitable for building large knowledge-based systems. The market for knowledge engineering tools designed for applications that require the computational power of a Lisp machine or an engineering workstation is dominated by a few vendors. This article is an assessment of the current state of tools used to build large knowledge-based systems. This assessment is based on the collective strengths and weaknesses of several tools that have been evaluated. In addition, an estimate is made of the features that will be required in the next generation of tools.