Modal Change Logic (MCL): Specifying the reasoning of knowledge-based systems

Data & Knowledge Engineering

1998

Self Cite

2,689

1,008

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 ...

Section: Types Of Psm Librariesmentioning

confidence: 99%

Section: Organisation Of Librariesmentioning

confidence: 99%

Knowledge engineering: Principles and methods

Studer

Benjamins

Data & Knowledge Engineering

1998

Self Cite

2,689

1,008

“…The combination of parallel updates and conditional choice make DDL very close to the more recently proposed language MLPM [8], with the parallel updates corresponding to MLPM's ! operator, and the conditional choice corresponding to MLPM's operator.…”

Section: The First-order Casementioning

confidence: 85%

“…However, whereas in DL, transitions can only be defined in terms of assignments to variables, PDDL and DDL define transitions in terms of truth-assignments to propositions (PDDL) or predicates (DDL). In this respect, DDL and PDDL are closely related to the more recent proposals for the language MLPM [8], which allows the explicit definition of transitions by assigning values to truth of predicates, and of MLCM [47], which also allows for the assignment of function values. We now turn to the discussion of PDDL and DDL, respectively.…”

Section: Dynamic Database Logicmentioning

confidence: 99%

See 1 more Smart Citation

A survey of languages for specifying dynamics: a knowledge engineering perspective

Eck

Engelfriet

IEEE Trans. Knowl. Data Eng.

et al. 2001

AbstractÐDuring the last years, a number of formal specification languages for knowledge-based systems has been developed. Characteristics for knowledge-based systems are a complex knowledge base and an inference engine which uses this knowledge to solve a given problem. Specification languages for knowledge-based systems have to cover both aspects. They have to provide the means to specify a complex and large amount of knowledge and they have to provide the means to specify the dynamic reasoning behavior of a knowledge-based system. This paper focuses on the second aspect. For this purpose, we survey existing approaches for specifying dynamic behavior in related areas of research. In fact, we have taken approaches for the specification of information systems (Language for Conceptual Modeling and TROLL), approaches for the specification of database updates and logic programming (Transaction Logic and Dynamic Database Logic) and the generic specification framework of Abstract State Machines.

The role of assumptions in knowledge engineering

Benjamins

1998

Int. J. Intell. Syst.

Self Cite

Problem‐solving methods are means of describing the inference process of knowledge‐based systems. In recent years, a number of these problem‐solving methods have been identified that can be reused for building new systems. However, problem‐solving methods require specific types of domain knowledge and introduce specific restrictions on the tasks that can be solved by them. These requirements and restrictions are assumptions that play a key role in the reuse of problem‐solving methods, in the acquisition of domain knowledge, and in the definition of the problem that can be tackled by knowledge‐based systems. In this paper we discuss the different roles assumptions play in the development of knowledge‐based systems and provide a survey of assumptions used in diagnostic problem solving. We show how such assumptions introduce targets and bias for goal‐driven machine learning and knowledge discovery techniques. © 1998 John Wiley & Sons, Inc.