: Because knowledge is a critical factor affecting the performance of an expert system, knowledge acquisition is a priori a fundamental task of any knowledge engineering enterprise. A number of methodologies, tools and techniques have been proposed in order to address the problem of knowledge acquisition. In this paper we describe knowledge engineering from a functional point of view which emphasizes the stages involved. In particular, we stress the need for tools supporting empirical data analysis as well as facilities for allowing a smooth transition from data analysis to domain conceptualization and finally to the system implementation. The strengths and weaknesses of our earlier system, KEATS-1, have led us to embark upon the design and implementation of a new system, KEATS-2, which provides a more coherent and integrated framework for performing both bottom-up and top-down knowledge acquisition. This paper introduces the first of our redesigned KEATS-2 modules, entitled Acquist, a...

: The numerous tasks required by the knowledge engineering process and their inherent complexity combine to make building knowledge-based systems both a time consuming and arduous activity. The key to reducing the complexity of the problem is to provide a methodological framework which can clarify the nature of the intermediate steps required to effectively encode knowledge into a performance system. Such a framework can then be used to drive the design of a comprehensive knowledge engineering toolkit. This is the approach we adopted in the Keats project. In this paper, we provide an overview of the Keats knowledge engineering methodology, which is based on a view of knowledge engineering as iterative refinement of qualitatively and teleologically different models, and we show how these ideas have driven the design of the Keats toolkit. 1. INTRODUCTION The construction of large, knowledge-based applications is a complex task that comprises a number of activities and involves various p...

: The 'Knowledge Engineer's Assistant' (KEATS) is a software environment suitable for constructing knowledge based systems. In this paper, we discuss its role in supporting the knowledge engineer in the tasks of knowledge elicitation and domain understanding. KEATS is based upon our own investigations of the behaviour and needs of knowledge engineers and provides two enhancements to other modern 'shells', 'toolkits', and 'environments' for knowledge engineering: (i) transcript analysis facilities, and (ii) a sketchpad on which the KE may draw a freehand representation of the domain, from which code is automatically generated. KEATS uses a hybrid representation formalism that includes a frame based language and a rule interpreter. We describe the novel components of KEATS in detail, and present an example of how KEATS was used to build an electronic fault diagnosis system. Acknowledgements: The KEATS project is conducted jointly by the Human Cognition Research Laboratory at the Open Uni...

In this paper, we demonstrate an important role for model-based reasoning in case adaptation. Model-based reasoning can allow a case-based reasoner to apply cases to a wider range of problems than would otherwise be possible. We focus on case adaptation in brainstormer, a planner that uses abstract advice to help it plan in the domain of political and military policy as it relates to terrorism. We show that by equipping a case adapter with an explicit causal model of the planning process, cases presented as advice can be flexibly applied to difficulties that arise at a variety of different stages of planning. Publishing Information This report appeared in the Proceedings AAAI-92 Tenth National Conference on Artificial Intelligence, San Mateo, U. S. A. July 1992. 1 This research was conducted at the Institute for the Learning Sciences at Northwestern University, and was supported in part by the Air Force Office of Scientific Research (AFOSR). The Institute for the Learning Sciences wa...

: The strengths and weaknesses of our earlier system, KEATS-1, have led us to embark upon the design and implementation of a new knowledge engineering environment, KEATS-2, which provides a novel, integrated framework for performing both bottom-up and top-down knowledge acquisition. In this paper we discuss the nature of the knowledge acquisition activities and we introduce the support tools embedded in KEATS-2. We characterize knowledge acquisition as the composition of knowledge elicitation, data analysis and domain conceptualization and we emphasize that a knowledge engineering tool has to support these activities as well as bridging the gap between acquiring the data and implementing the final system. Acknowledgement: This research is supported by a grant from British Telecommunications, plc. Steven Rose and Mike Stewart of the Open University's Brain Research Group provided valuable domain expertise. 1. THE PROBLEM OF KNOWLEDGE ACQUISITION The most popular principle in knowledge...

: In this paper the architecture of the VITAL-KR is described. Author: Enrico Motta and Arthur Stutt Collaborators (VITAL Partners): SYSECA - SYSECA TEMPS REEL (Coordinator) * NOTT - UNIVERSITY OF NOTTINGHAM * BULL - BULL CEDIAG AC - ANDERSEN CONSULTING ONERA - ONERA PTT - ROYAL PTT NEDERLAND NV * OU - THE OPEN UNIVERSITY * NOKIA - NOKIA RESEARCH CENTER * marked partners are involved in this task page 1 _____________________________________________________________________ The Open University 1991 1. INTRODUCTION As discussed in (Motta, 1991) both the current practice of industrial KBS, and the consensus among researchers (Frisch & Cohn, 1991) suggest that hybrid architectures, embedding a number of specialized representations/reasoners, are required to enable knowledge engineers to build efficient and powerful KBs. This is due to the fact that it has been recognized that no universal knowledge representation language exists, which can efficiently model all types of proble...