Problem-solving methods: Making assumptions for efficiency reasons

Fensel, Dieter; Straatman, Remco

doi:10.1007/3-540-61273-4_2

Cited by 12 publications

(8 citation statements)

References 18 publications

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“…Inference engines like unification, forward or backward resolution, and inheritance captured the dynamic part of deriving new information. However, human experts have exploit knowledge about the dynamics of the problem-solving process and such knowledge is required to enable problem-solving in practice and not only in principle [60]. [38] provided several examples where knowledge engineers implicitly encoded control knowledge by ordering production rules and premises of these rules that together with the generic inference engine, delivered the desired dynamic behaviour.…”

Section: Problem-solving Methodsmentioning

confidence: 99%

“…In this way, these ontologies help to solve the 'interaction problem' [33], which states that domain knowledge cannot be independently represented from how it will be used in problem solving, and vice versa. Method and task ontologies enable to make explicit the interaction between problem-solving and domain knowledge through assumptions ( [17], [60], [18]). …”

Section: Types Of Ontologiesmentioning

confidence: 99%

“…"In simple terms this means that analysis is not simply interested in what happens, as in conventional systems, but also with how and why" [31]. One must not only acquire knowledge about what a solution for a given problem is, but also knowledge about how to derive such a solution in an efficient manner [60]. Already at the knowledge level there must be a description of the domain knowledge and the problem-solving method which is required by an agent to solve the problem effectively and efficiently.…”

Section: The What and The How: Specification Of Reasoningmentioning

confidence: 99%

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Knowledge engineering: Principles and methods

Studer

Benjamins

Fensel

1998

Data & Knowledge Engineering

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2,689

1,008

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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 ...

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Section: Problem-solving Methodsmentioning

confidence: 99%

Section: Types Of Ontologiesmentioning

confidence: 99%

Section: The What and The How: Specification Of Reasoningmentioning

confidence: 99%

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Knowledge engineering: Principles and methods

Studer

Benjamins

Fensel

1998

Data & Knowledge Engineering

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2,689

1,008

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“…1 A characteristic property of these specification languages results from the fact that they do not aim at a purely functional specification. In general, most problems tackled with KBSs are inherently complex and intractable (see e.g., [13] and [14]). A specification has to describe not just a realization of the functionality, but one which takes into account the constraints of the reasoning process and the complexity of the task.…”

Section: Introductionmentioning

confidence: 99%

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

Eck

Engelfriet

Fensel

et al. 2001

IEEE Trans. Knowl. Data Eng.

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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.

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“…[11], [21]). A PSM can only solve such tasks with reasonable computational effort by introducing assumptions that restrict the complexity of the problem or by strengthening the requirements on domain knowledge.…”

Section: Adapter: Connecting Task and Psmmentioning

confidence: 99%

Using KIV to specify and verify architectures of knowledge-based systems

Fensel¹,

Schnogge²

Proceedings 12th IEEE International Conference Automated Software Engineering

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examples. E.g. we have verified with KIV theorems of [3] used to distinguish different complexity classes in abduction. 7 Notice that these proofs are in no way trivial: the informal proof for theorem 5.3 of [3] took one page. It states that for the class of ordered monotonic abduction problems using a specific preference criterion, there is a polynomial algorithm for finding a best explanation. This is proved by presenting an algorithm that in polynomial time returns such a best explanation. Formalizing the informal arguments of the correctness proof for this algorithm results in several hundred (machine checkable) proof steps.Roughly, the interactive theorem proving system of KIV is comparable with systems as PVS [5] and Isabelle [22]. For our purpose, the KIV system is especially well suited due to its facilities for structuring specifications and software modules (including automatic generation of proof obligations), its proof engineering facilities (like an elaborated graphical user interface and reuse mechanisms), and the underlying dynamic logic.[20] identifies two kinds of approaches in software reuse: Supporting the software development process with reusable components or making parts of the development process reusable via program transformation techniques. Our approach provide support by formally specified and verified building blocks i.e. components. The latter approach is taken by KIDS/SPECWARE [28], [29] which provides support in the derivation of efficient implementations from formal specifications. Here, problem-solving methods are not "first-order citizens" that describe reusable components or architectures but secondorder transformation rules working on specifications. As in our approach, system development is viewed as a semiautomatic activity. At the technical level, the main differences are the use of dynamic logic for the declarative specification of procedural constructs in KIV and the use of category theory and sheaf theory to express transformations of algebraic specifications in SPECWARE.AMPHION ([18], [19]) is a knowledge-based software engineering system for the formal specification and automatic deductive synthesis of programs which consist of calls of subroutines from a library. It is specialized to application domains by means of a declarative domain theory and a library of subroutines. This specialization allows the automatic synthesis of programs from specifications. Our approach is more general-purpose (but, of course, less automatic): the programs developed are combinations and instantiations of (mostly domain-7. Theorem 4.4 and (the more difficult) theorem 5.3. For both theorems only the total correctness of the algorithms and, of course, not their complexity bounds have been proven. independent) problem-solving methods rather then simply a sequence of calls of subroutines from a library. Furthermore the (normal) user of the AMPHION system is not intended to create or modify the domain theory or the subroutine library. In our approach, the verification of user-defined problem-...

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Problem-solving methods: Making assumptions for efficiency reasons

Cited by 12 publications

References 18 publications

Knowledge engineering: Principles and methods

Knowledge engineering: Principles and methods

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

Using KIV to specify and verify architectures of knowledge-based systems

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