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...

The Knowledge Acquisition and Representation Language (KARL) combines a description of a knowledge-based system at the conceptual level (a so-called model of expertise) with a description at a formal and executable level. Thus, KARL allows the precise and unique specification of the functionality of a knowledge-based system independent of any implementation details. A KARL model of expertise contains the description of domain knowledge, inference knowledge, and procedural control knowledge. For capturing these different types of knowledge KARL provides corresponding modeling primitives based on Frame-logic and Dynamic Logic. A declarative semantics for a complete KARL model of expertise is given by a novel combination of these two types of logic. In addition, an operational definition of this semantics, which relies on a fixpoint approach, is given. This operational semantics defines the basis for the implementation of the KARL interpreter which includes appropriate algorithms for efficiently executing KARL specifications. This enables the evaluation of KARL specifications by means of testing. 1

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)....

TFL, the Task Formal Language, has been developed for integrating the static and dynamic aspects of Knowledge Based Systems. This paper focuses on the formal specification of dynamic behaviour. Although fundamental in Knowledge Based System, the strategic reasoning was rather neglected until now by the existing formal specifications. Most languages were generally more focused on the domain and problem-solving knowledge specification than on the control. The formalisation presented here differs from previous ones in several aspects. First, a different representation of dynamic knowledge is proposed : TFL is based on Algebraic Data Types, as opposed to dynamic or temporal logic. Second, dynamic strategic reasoning is emphasised, whereas existing languages only offer to specify algorithmic control. Then, TFL does not only provide the specification of the problem-solving knowledge of the object system, but also of its strategic knowledge. Finally, the dynamic knowledge of the meta-system i...

This paper focuses on the formal specification of the dynamic behavior of Knowledge Based Systems (KBS). Although fundamental in KBS, the notion of control is nearly absent in most of the formal specifications proposed until now. The formalisation presented here differs from previous ones in several aspects. First, a different representation of dynamic knowledge is proposed, since neither dynamic nor temporal logic have been chosen for this purpose but Algebraic Data Types. Second, the specification is not only dedicated to the problem-solving knowledge but it also takes the strategic knowledge and the control process into account. Finally, modularization is another major feature of the specification. The paper is structured as follows. First, the primitives used for modelling the categories of knowledge according to the TASK methodology are described. In particular, the notion of process is clarified, and an hybrid control combining opportunistic and hierarchical approaches is present...

This paper presents the TASK framework which is intended to cover the life cycle of a Knowledge-Based System. TASK provides (i) a conceptual language which enable an informal specification at the knowledge level, (ii) a formal language TFL which permits an unambiguous specification and (iii) an operational shell TASK + which allows an efficient execution even for bad structured problems. This paper presents the different languages, the links between them and emphasizes the implementation stage. We show how TASK proposes a nice compromise solution between efficiency and expressivity. Introduction Now, the modeling approach [15] where the KnowledgeBased System (KBS) life-cycle is decomposed into several stages, is generally adopted in Knowledge Acquisition. Three main stages are considered: ffl the conceptualization stage allows to produce the model of expertise (ME). This model is an abstract description of an agent's problem-solving behavior (human or artifact). Different methodolog...

The Knowledge Acquisition and Representation Language (KARL) combines a description of a knowledge­based system (kbs) at the conceptual level (a so­called model of expertise) with a description at a formal and executable level. It is a specification language which allows the precise and unique description of a kbs independently from implementational details. In the paper, KARL is mainly discussed as a formal language. That is, the paper introduces a formal semantics for KARL. Because KARL allows the representation of static and dynamic (i.e., procedural) knowledge, its semantics must integrate both types of knowledge. First, an object­ oriented logic L­KARL was developed which can be used to specify static knowledge. Second, dynamic logic was used to develop P­KARL for specifying knowledge about dynamics. Third, both languages had to be combined to represent a complete model of expertise. As a result, the integrated description of static and dynamic knowledge based on a well­defined declarative framework becomes possible.

. 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...

: the experience of the French central Bank in terms of developing knowledge-based systems have led us to elaborate an environment of knowledge modelisation called CERISE. In this approach, we introduce a notion of module, issued from software engineering. This notion is fully formalised using algebraic abstract types. The main difference between this formalisation and the earlier ones (see (ML)2, KbsSF, KARL, among others) is that the formalisation process does not take place after but during the knowledge acquisition process. This paper shows the effects of using this formalisation both in the acquisition and reusability of knowledge. I INTRODUCTION A library of reusable problem-solving methods was seen as a strong advantage in favour of the KADS methodology ([WIE92], [WIE93]). Reusability is a significant argument to justify the investments necessited by employing such an abstract and complex methodology. At the French Central Bank, half a dozen of knowledge-based systems deal wit...

During the last years several formal development methodologies became available in the area of software engineering. However, only recently formal methods entered the field of knowledgebased system development. Some of these methods support a formal functional specification of knowledge-based systems. In order to make an effective and efficient use of formal functional models, we believe a simulation tool is needed which displays the specified functionality. We present a formal development methodology for knowledge-based systems called VITAL, and we describe the design and implementation of a simulator for K BS SF, the formal functional specification language of VITAL. In the design and implementation of the K BS SF simulator we took a transformational approach which makes use of existing programming languages (C ++ and Prolog) and a transformator generator. Keywords: knowledge-based systems, formal development methodologies, system validation, simulation. 1 Introduction K BS SF is ...