The use of predefined models of problem-solving methods is receiving considerable attention from researchers in the area of knowledge acquisition. Using these models, developers of knowledge-acquisition tools are able to prescribe the roles in which knowledge is used in completing a given task. A number of method-oriented architectures based on a single problem-solving method have been developed by various research groups. Because the methods are domain-independent, method-oriented architectures are limited by the fact that knowledge roles that depend on domain-specific considerations cannot be represented using the model of problem solving. In addition, the interface between the knowledge-acquisition tool and the application expert cannot adequately convey the role of each knowledge type in the task model. PROTG-II is a knowledge-acquisition shell that we are building to generate knowledge-acquisition tools automatically without presupposing a specific model of problem-solving. The shell manages a library of mechanisms---procedures of grain size smaller than that of problem-solving methods. Mechanisms can be combined in PROTG-II to construct problem-solving methods and to define the roles of knowledge that depend on domain considerations. Furthermore, PROTG-II utilizes the concept of adaptation in interfaces to allow the knowledge engineer to produce interfaces that are task- and domain-specific. In this paper, we present the PROTG-II shell and examine the components of its architecture. We also demonstrate the use of PROTG-II with a running example, and discuss the design techniques used to overcome the limitations of method-specific architectures.

Metalevel tools can support the knowledge-engineering process by assisting developers in the design and implementation of domain-oriented knowledge-acquisition tools. The use of ontologies as a basis for automatic generation of knowledge-acquisition tools simplifies the tool-specification process by taking advantage of ontologies defined as part of the knowledge-engineering process. One of the drawbacks of this approach is that it separates ontology definition (in ontology editors) from instance editing (in knowledge-acquisition tools). Because many application tasks require ontology definition by domain experts, we have experimented with extending the Protege framework to generate ontology editors in addition to knowledgeacquisition tools for instances. We have explored di#erent approaches to ontology-editor specification in a series of prototype extensions to Protege. Here, metaclass and metaslot definitions are the basis for ontology editors, which can be embedded in know...

Metalevel tools can support the software development process by automating the design of task- and application-specific tools. Dash is a metalevel tool that allows developers to generate domain-specific knowledge-acquisition tools from domain ontologies. Domain specialists use the knowledge-acquisition tools generated by dash to instantiate the concepts and relationships defined in the domain ontologies. The output of the knowledge-acquisition tools is a collection of instances that constitute the knowledge base for a knowledge-based system.

This paper describes how we applied the PROTG-II architecture to build a knowledgebased system that configures elevators. The elevator-configuration task was solved originally with a system that employed the propose-and-revise problem-solving method (VT; Marcus, Stout & McDermott, 1988). A variant of this task, here named the Sisyphus-2 problem, is used by the knowledge-acquisition community for comparative studies. PROTG-II is a knowledge-engineering environment that focuses on the use of reusable ontologies and problem-solving methods to generate task-specific knowledge-acquisition tools and executable problem solvers. The main goal of this paper is to describe in detail how we used PROTG-II to model the elevator-configuration task. This description provides a starting point for comparison with other frameworks that use abstract problem-solving methods. Starting from a detailed description of the elevator-configuration knowledge (Yost, 1992), we analyzed the domain knowledge and developed a general, reusable domain ontology. We selected, from PROTG-II's library of preexisting methods, a propose-and-revise method based on chronological backtracking. We then configured this method to solve the elevator-configuration task in a knowledge-based system named ELVIS. We entered domain-specific knowledge about elevator configuration into the knowledge base with the help of a task-specific knowledge-acquisition tool that was generated from the ontologies. After we constructed mapping relations to connect the domain and method ontologies, PROTG-II generated the executable problem solver. We have found that the development of ELVIS has provided a valuable test case for evaluating PROTG-II's suite of system-building tools.

Building a problem solver and acquiring the knowledge needed to operate it are the two central goals of knowledge engineering. To achieve these goals, knowledge engineers construct models of the domain and of the task of interest. The various approaches used for modeling, however, have so far failed to define methods and techniques that can be applied across domains and tasks, and to produce models that can be reused in future applications. In this paper, we propose that both of these objectives can be achieved by the use of building blocks called mechanisms. We examine the composition of mechanisms and also show how these mechanisms can be manipulated to construct problem-solving methods. We present PROTG-II, a knowledge-acquisition shell that uses problem-solving methods to drive the modeling of tasks, the automatic generation of knowledge-acquisition tools, and the control flow of the problem solver. The modeling of tasks, within the context of PROTG-II, is illustrated with two examples: one from the game domain and another from the medical-therapy domain. In addition, we introduce the conceptual basis for a library of mechanisms that serves as a repository of reusable knowledge components.

In this paper we present the following view on problem-solving methods: Problemsolving methods describe an efficient reasoning strategy to achieve a goal by introducing assumptions about the available domain knowledge and the "functionality" of the task. These assumptions characterize a problem-solving method. This differs from current views on problem-solving methods. Current work does not explicitly take the role of efficiency of problem-solving methods into consideration, whereas we regard it as the primary principle that drives the development of problem-solving methods. A consequence of our view is that development of problem-solving can no longer be seen as hierarchical refinement of a functional specication. 1 Introduction The concept of a problem-solving method (PSM) is present in a large part of current knowledge-engineering frameworks (e.g. generic tasks [Cha86], Role-limiting Methods [Mar88, Pup93], kads [SWB93], method-to-task approach [Mus92], Components of expertise [St...

Abstract. During the past decade a number of modelling frameworks for knowledge based systems have been developed. In this paper an approach to the comparison of modelling frameworks is proposed, based on the aims and purposes behind the frameworks. A purpose oriented comparison of the frameworks DESIRE, CommonKADS, PROTÉGÉ-II, MIKE, VITAL and KARL provides insight in their differences and similarities. 1

This paper reflects on experiences designing, developing, and working with users of a variety of interactive computer systems. The authors propose, based on these experiences, that the cause of a number of unexpected difficulties in human-computer interaction lies in users' unwillingness or inability to make structure, content, or procedures explicit. Besides recounting experiences with system use, this paper discusses why users reject or circumvent formalisms which require such explicit expression, and suggests how system designers can anticipate and compensate for problems users have in making implicit aspects of their tasks explicit. The authors propose computational approaches that address this problem, including incremental and system-assisted formalization mechanisms and methods for recognizing and using undeclared structure; they also propose non-computational solutions that involve designers and users reaching a shared understanding of the task situation and the methods that motivate the formalisms. This paper poses that, while it is impossible to remove all formalisms from computing systems, system designers need to match the level of formal expression entailed with the goals and situation of the users -- a design criteria not commonly mentioned in current interface design.

Method-oriented knowledge-acquisition tools are based on a model, or method, of problem solving and can acquire knowledge for the class of tasks that can be solved with that particular problem-solving method. The capture of knowledge takes place in knowledge editors. These editors are typically based on the individual tool's domainindependent method; they fail to reflect task- and domainspecific characteristics and have no ability to adapt to user requirements. Mecano is a user-interface management system that generates automatically adaptable knowledge editors for the PROTG-II knowledge-acquisition shell. Mecano allows knowledge engineers to specify the components of a knowledge editor independently of any underlying problem-solving method. It also provides facilities for constraining the operations allowed on the components, for selecting interaction styles for each component, and for linking components to coordinate their simultaneous display. Knowledge editors generated by Mecano take into account the needs and requirements of given tasks, domains, and users, and guide the users through the knowledge-editing process by providing visual cues and by limiting the permissible editing operations to those relevant in the domain of interest.

descriptions from a set of cases which have been assigned to categories by an expert. The systems generalise values for descriptors (fields) for each of the categories to create category descriptions, and suggest to the user various means by which any inconsistencies can be removed. In this proposal we put forward a suggestion for an expert system, R4, which will be the fourth iteration of the Refiner series. Unlike previous iterations, R4 will include a non-deterministic feature which will enable it to better handle noisy datasets by generating category descriptions with ‘fuzzy ’ boundaries. The R4 system will be based on a case-based algorithm focussed on performing a classification task, in particular medical (differential) diagnosis; R4 will be aimed primarily at medical students and junior doctors (i.e., novice diagnosticians). The R4 algorithm will incorporate concepts from case-based reasoning, hypothetico-deductive reasoning (HDR) and illness scripts, and will perform diagnosis by means of a diagnostic web, which is an expansion of the illness script concept. This system supports the novice user as they develop and navigate through a medical knowledge web. 1