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 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.

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.

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.