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

Problem solving methods (PSMs) are domain-independent reasoning components, which specify patterns of behavior which can be reused across applications. While the availability of extensive PSM libraries and the emerging consensus on PSM specification languages indicate the maturity of the field, a number of important research issues are still open. In particular, very little progress has been achieved on foundational and methodological issues. Existing libraries of PSMs lack a clear theoretical basis and only provide weak support for the method development process, usually in the form of informal guidelines. In this paper we will address these issues by illustrating a framework which characterizes PSMs in terms of problem commitments, problem-solving paradigms and domain assumptions. This framework provides i) a theoretical foundation for situating PSM research and individual PSMs, as well as ii) an organization which allows us to characterize method development and selection as a process of navigating through a three-dimensional space (defined by the three components of our framework). Individual moves through this space are specified by means of adapters. In the paper we will illustrate these ideas in detail, with examples taken from parametric design problem solving. 1.

. This paper provides an overview of the OCML modelling language: it illustrates the underlying philosophy, describes the main modelling constructs provided, and compares it to other modelling languages. 1. INTRODUCTION OCML 1 was originally developed in the context of the VITAL project (Shadbolt et al., 1993) to provide operational modelling capabilities for the VITAL workbench (Domingue et al., 1993). Over the years the language has undergone a number of changes and improvements and in what follows we will provide an overview of the current version of the language (v5.1), illustrate its underlying philosophy and compare it to other knowledge modelling languages. 2. LANGUAGE TENETS A number of ideas/principles have shaped the development of the OCML language. These are discussed in the following sections. 2.1. Knowledge-level modelling support. The main goal of OCML is to support knowledge-level modelling (Newell, 1982; Fensel and Van Harmelen, 1994). In practice this role impl...

Problem-solving methods provide reusable architectures and components for implementing the reasoning part of knowledge-based systems.

. The paper introduces an approach for the specification and verification of knowledge-based systems combining conceptual and formal techniques. We identify four elements of the specification of a knowledge-based system: a task definition, a problem-solving method, a domain model, and an adapter that relates the other elements. We present abstract data types and a variant of dynamic logic as formal means to specify and verify these different elements. As a consequence of our conceptual model we can decompose the overall verification task of the knowledge-based systems into different proof obligations. Each proof obligation deals with a different aspect of the entire system. The use of the conceptual model in specification and verification improves understandability and reduces the effort for both activities. The modularization enables reuse of specifications and proofs. A knowledge-based system can be build by combing and adapting different components. 1 INTRODUCTION During the last ...

Abstract. We present the architecture of an intelligent broker for enabling the use of problem-solving methods via the World Wide Web (WWW). The core component of such a broker is realised by an ontologist and an adapter. Ontologies mediate between domainspecific requirements and knowledge, task-specific problem descriptions and methodspecific terms describing the competence and requirements of the reasoning components. The ontological reasoning for relating the different ontologies is supported by the ontologies. Adapters are necessary to provide domain knowledge and case data to problem-solving methods and to rephrase the output of problem-solving methods into domain-specific terms. Therefore, the ontologist mediates the selection and adaptation process of PSMs whereas the adapter mediates the execution of them. 1

This document is composed of three parts. The first part describes WebOnto, a tool providing web-based visualisation, browsing and editing support for developing and maintaining ontologies and knowledge models specified in OCML. The description is user-oriented, in the sense that it is meant to provide guidance to a user, rather than to describe the tool from a scholarly perspective. The second part of the document describes OCML, an operational knowledge modelling language, which provides the underlying representation for the ontologies and knowledge bases which can be developed using WebOnto. This second part is a revised version of chapter 4 of (Motta, 1999). The third part (Appendix 1), gives more details about the interpreters and reasoning facilities provided by OCML

. The paper introduces a formal approach for the specification and verification of knowledge-based systems. We identify different elements of such a specification: a task definition, a problem-solving method, a domain model, an adapter, and assumptions that relate these elements. We present abstract data types and a variant of dynamic logic as formal means to specify these different elements. Based on our framework we can distinguish several verification tasks. In the paper, we discuss the application of the Karlsruhe Interactive Verifier (KIV) for this purpose. KIV was originally developed for the verification of procedural programs but it fits well for our approach. We illustrate the verification process with KIV and show how KIV can be used as an exploration tool that helps to detect assumptions necessary to close the gap between the task definition and the competence of a problemsolving method. 1 Introduction During the last years, several conceptual and formal specification tech...

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)....

Task models and problem solving methods can be specified informally or formally. In recent years various approaches have formalized their notion of task model or problem solving method. Most modelling approaches concentrate on the form of a task model or problem solving method rather than on their precise semantics; a formalisation is often only a syntactical formalisation. A more precise definition of the semantics requires explication of the control of a system's behaviour. In this paper temporal semantics is defined for a compositional modelling approach to task models and problem solving methods. The semantics is a description of a compositional system's behaviour; a temporal approach provides a means to describe the dynamics involved. The formalisation of the semantics is based on compositional three-valued temporal models. The compositional structure of information states, transitions and reasoning traces provides a transparant model of the system's behaviour, both conceptually and formally.