In the field of Knowledge Engineering, dissatisfaction with the rapid-prototyping approach has led to a number of more principled methodologies for the construction of knowledgebased systems. Instead of immediately implementing the gathered and interpreted knowledge in a given implementation formalism according to the rapid-prototyping approach, many such methodologies centre around the notion of a conceptual model: an abstract, implementation independent description of the relevant problem solving expertise. A conceptual model should describe the task which is solved by the system and the knowledge which is required by it. Although such conceptual models have often been formulated in an informal way, recent years have seen the advent of formal and operational languages to describe such conceptual models more precisely, and operationally as a means for model evaluation. In this paper, we study a number of such formal and operational languages for specifying conceptual mode...

This paper reports on an investigation into a formal language for specifying kads models of expertise. After arguing the need for and the use of such formal representations, we discuss each of the layers of a kads model of expertise in the subsequent sections, and define the formal constructions that we use to represent the kads entities at every layer: order-sorted logic at the domain layer, meta-logic at the inference layer, and dynamic-logic at the task layer. All these constructions together make up (ml) 2 , the language that we use to represent models of expertise. We illustrate the use of (ml) 2 in a small example model. We conclude by describing our experience to date with constructing such formal models in (ml) 2 , and by discussing some open problems that remain for future work. 1 Introduction One of the central concerns of "knowledge engineering" is the construction of a model of some problem solving behaviour. This model should eventually lead to the construction of a...

Much of the work on validation and verification of knowledge based systems (KBSs) has been done in terms of implementation languages (mostly rule-based languages). Recent papers have argued that it is advantageous to do validation and verification in terms of a more abstract and formal specification of the system. However, constructing such formal specifications is a difficult task. This paper proposes the use of formal specification languages for KBS-development that are closely based on the structure of informal knowledge-models. The use of such formal languages has as advantages that (i) we can give strong support for the construction of a formal specification, namely on the basis of the informal description of the system; and (ii) we can use the structural correspondence to verify that the formal specification does indeed capture the informally stated requirements. This paper has been submitted to the Journal of Human Computer Studies (formerly the Journal of Man Machine Studies)....

This paper reports on the development of a realistic knowledge-based application using the MOBAL system. Some problems and requirements resulting from industrial-caliber tasks are formulated. A step-by-step account of the construction of a knowledge base for such a task demonstrates how the interleaved use of several learning algorithms in concert with an inference engine and a graphical interface can fulfill those requirements. Design, analysis, revision, refinement and extension of a working model are combined in one incremental process. This illustrates the balanced cooperative modeling approach. The case study is taken from the telecommunications domain and more precisely deals with security management in telecommunications networks. MOBAL would be used as part of a security management tool for acquiring, validating and refining a security policy. The modeling approach is compared with other approaches, such as KADS and stand-alone machine learning. What online ML can do for KA -...

The claim of TASK MODEL approach is that the construction of...

This paper presents a general discussion of the role of formal methods in Knowledge Engineering. We give an historical account of the development of the field of Knowledge Engineering towards the use of formal methods. Subsequently, we discuss the pro's and cons of formal methods. We do this by summarising the proclaimed advantages, and by arguing against some of the commonly heard objections against formal methods. We briefly summarise the current state of the art and discuss the most important directions that future research in this field should take. This paper presents a general setting for the other contributions in this issue of the Journal, which each deal with a specific issue in more detail. 1 Historical growth of Knowledge Engineering towards Formal Methods Although the history of KBS technology and Knowledge Engineering (KE) is well documented in a number of places in the literature ( e.g. [42, ch.2]), in this section we will give an account of the development of K...

This paper presents an overview of the REFLECT project. It defines the notion of knowledge level reflection that has been central to the project, it compares this notion with existing approaches to reflection in related fields, and investigates some of the consequences of the concept of knowledge level reflection: what is a general architecture for knowledge level reflection, how to model the object component in such an architecture, what is the nature of reflective theories, how can we design such architectures, and what are the results of our actual experiments with such systems?

. The knowledge level hypothesis formulated by Newell states the existence of knowledge independent of its representation. After a careful study of its implications, it turns out that the knowledge modelling theory, the knowledge representation techniques, and the knowledge acquisition process, involved in the overall knowledge engineering process, are nevertheless strongly interrelated. Thus, the claim is that the construction of knowledge based systems (KBS) at the knowledge level requires a coherent framework, which takes into account these interactions. In this article, such a framework including a knowledge modelling paradigm, the task oriented modelling, an architecture based on a high level representation language, the task model formalism, and a knowledge acquisition method, the task oriented acquisition method, is proposed. The two most significant knowledge engineering approaches, KADS and Components of Expertise, are first surveyed and compared according to these three dimen...

. We present (ML) 2 , a formal language for the representation of KADS models of expertise. (ML) 2 is a combination of first order predicate logic (for the declarative representation of domain knowledge) , meta-logic (for the representation of how to use the domain knowledge) and dynamic logic (for the representation of control information). After a brief summary of KADS, we describe how each of the four KADS layers is represented in (ML) 2 , and we compare our formalism to other formalisms that have been proposed for the formalisation of KADS models. 1 Introduction One of the central concerns of "knowledge engineering " is the construction of a model of problem solving behaviour. One of the prominent approaches in recent years to this problem (at least in Europe) has been the KADS methodology for knowledge engineering [9]. KADS is centered around a so-called model of expertise which describes the problem solving expertise of the system to be modelled independent of a possi...

MoMo is a language for operationalizing KADS-type models of expertise. It is embedded in Common Lisp such that systems described in MoMo can be developed and run in the Lisp environment. MoMo relies on the Common Lisp Object System (CLOS) and its integration into the Common Lisp type system to describe problem solving methods in a domain-independent way. To connect such a problem-solving method to a particular domain,the abstract types and their generic operations have to be mapped to concrete ones. The semantics of inference structures was to some extent influenced by ideas from colored Petri nets [ Jensen, 1987 ] . This report contains a larger example, the syntax, its interpretation, and the design rationale for MoMo. Contents 1 Background 3 2 A short glimpse on MoMo 3 3 An example: Diagnosis of home stereo equipment 5 3.1 The domain knowledge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 3.2 Knowledge about inferences . . . . . . . . . . . . . . . . . . . . . . ...