TFL: an algebraic language to specify the dynamic behaviour of knowledge-based systems

Pierret-Golbreich, Christine; Talon, Xavier

doi:10.1017/s0269888900007918

Cited by 16 publications

(12 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[46], [154]) that provide well-studied means for the mathematical definitions of the functionality of software systems. Some of the existing specification languages rely directly on these techniques (e.g., [134], [117]), other use similar ideas. The main extensions that are necessary to use them for KBSs are:…”

Section: Comparison With Traditional Specification Approaches In Sementioning

confidence: 99%

Knowledge engineering: Principles and methods

Studer

Benjamins

Fensel

1998

Data & Knowledge Engineering

2,735

1,008

View full text Add to dashboard Cite

This paper gives an overview about the development of the field of Knowledge Engineering over the last 15 years. We discuss the paradigm shift from a transfer view to a modeling view and describe two approaches which considerably shaped research in Knowledge Engineering: Role-limiting Methods and Generic Tasks. To illustrate various concepts and methods which evolved in the last years we describe three modeling frameworks: CommonKADS, MIKE, and PROTÉGÉ-II. This description is supplemented by discussing some important methodological developments in more detail: specification languages for knowledge-based systems, problem-solving methods, and ontologies. We conclude with outlining the relationship of Knowledge Engineering to Software Engineering, Information Integration and Knowledge Management. Key WordsKnowledge Engineering, Knowledge Acquisition, Problem-Solving Method, Ontology, Information Integration IntroductionIn earlier days research in Artificial Intelligence (AI) was focused on the development of 2 formalisms, inference mechanisms and tools to operationalize Knowledge-based Systems (KBS). Typically, the development efforts were restricted to the realization of small KBSs in order to study the feasibility of the different approaches.Though these studies offered rather promising results, the transfer of this technology into commercial use in order to build large KBSs failed in many cases. The situation was directly comparable to a similar situation in the construction of traditional software systems, called "software crisis" in the late sixties: the means to develop small academic prototypes did not scale up to the design and maintenance of large, long living commercial systems. In the same way as the software crisis resulted in the establishment of the discipline Software Engineering the unsatisfactory situation in constructing KBSs made clear the need for more methodological approaches.So the goal of the new discipline Knowledge Engineering (KE) is similar to that of Software Engineering: turning the process of constructing KBSs from an art into an engineering discipline. This requires the analysis of the building and maintenance process itself and the development of appropriate methods, languages, and tools specialized for developing KBSs.Subsequently, we will first give an overview of some important historical developments in KE: special emphasis will be put on the paradigm shift from the so-called transfer approach to the so-called modeling approach. This paradigm shift is sometimes also considered as the transfer from first generation expert systems to second generation expert systems [43]. Based on this discussion Section 2 will be concluded by describing two prominent developments in the late eighties: Role-limiting Methods [99] and Generic Tasks [36]. In Section 3 we will present some modeling frameworks which have been developed in recent years:, and PROTÈGÈ-II [123]. Section 4 gives a short overview of specification languages for KBSs. Problem-solving methods have been a major research ...

show abstract

Section: Comparison With Traditional Specification Approaches In Sementioning

confidence: 99%

Knowledge engineering: Principles and methods

Studer

Benjamins

Fensel

1998

Data & Knowledge Engineering

2,735

1,008

View full text Add to dashboard Cite

show abstract

“…It has been used to model several applications, like Wielinga et al (1992, p. 24) task systematic-diagnosis goal find the smallest components with inconsistent behaviour, if one control-terms differential"set of currently active hypotheses inconsistent-sub-system"sub-part of the system with inconsistent behaviour task-structure systematic-diagnosis (complaint P inconsistent-sub-system)" select (complaint P system-model) generate-hypotheses (system-model P differential) repeat test-hypotheses (differentialPinconsistent-sub-system) generate-hypothesis (inconsistent-sub-system P differential) until differential" supervision of air traffic, home automation, emergency situation (Sebillotte & Fallah, 1995), etc. Successive operational languages, TASK (Pierret-Golbreich & Delouis, 1991), LISA (Delouis, 1993), TASK# (Talon & Pierret-Golbreich, 1996) have next been developed to enable an efficient execution of the tasks. LISA has been used to realize different systems for real applications in the field of electric networks at the French Electricity company EDF, like COPILOTE (Krivine & Delouis, 1991), AUSTRAL (Bredillet & Delouis-Jacob, 1994).…”

Section: A Task-methods Frameworkmentioning

confidence: 99%

Supporting organization and use of problem-solving methods libraries by a formal approach

Pierret-Golbreich¹

1998

International Journal of Human-Computer Studies

Self Cite

View full text Add to dashboard Cite

“…Prolog), algebraic specification techniques (e.g. TFL [20]) or model-based approaches (like B [21] and DESIRE [22]) which describe a system in terms of states and operations working on these states.…”

Section: Languages and Modeling For Problem Solvingmentioning

confidence: 99%

Knowledge formalization in experience feedback processes: An ontology-based approach

Foguem

Coudert

Béler

et al. 2008

Computers in Industry

View full text Add to dashboard Cite

Because of the current trend of integration and interoperability of industrial systems, their size and complexity continue to grow making it more difficult to analyze, to understand and to solve the problems that happen in their organizations. Continuous improvement methodologies are powerful tools in order to understand and to solve problems, to control the effects of changes and finally to capitalize knowledge about changes and improvements. These tools involve suitably represent knowledge relating to the concerned system. Consequently, Knowledge Management (KM) is an increasingly important source of competitive advantage for organizations. Particularly, the capitalization and sharing of knowledge resulting from experience feedback are elements which play an essential role in the continuous improvement of industrial activities. In this paper, the contribution deals with semantic interoperability and relates to the structuring and the formalization of an Experience Feedback (EF) process aiming at transforming information or understanding gained by experience into explicit knowledge. The reuse of such knowledge has proved to have significant impact on achieving the missions of companies. However, the means of describing the knowledge objects of an experience generally remain informal. Based on an experience feedback process model and conceptual graphs, this paper takes domain ontology as a framework for the clarification of explicit knowledge and know-how, the aim of which is to get lessons learned descriptions that are significant, correct and applicable.

show abstract

TFL: an algebraic language to specify the dynamic behaviour of knowledge-based systems

Cited by 16 publications

References 17 publications

Knowledge engineering: Principles and methods

Knowledge engineering: Principles and methods

Supporting organization and use of problem-solving methods libraries by a formal approach

Knowledge formalization in experience feedback processes: An ontology-based approach

Contact Info

Product

Resources

About