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.

As knowledge-based systems are addressing increasingly complex domains, their roles are shifting from classical expert systems to interactive assistants. To develop and maintain such systems, an integration of thorough knowledge acquisition procedures and sustained learning from experience is called for. A knowledge level modeling perspective has shown to be useful for analyzing the various types of knowledge related to a particular domain and set of tasks, and for constructing the models of knowledge contents needed in an intelligent system. To be able to meet the requirements of future systems with respect to robust competence and adaptive learning behavior, particularly in open and weak theory domains, a stronger emphasis should be put on the combined utilization of casespecific and general domain knowledge. In this chapter we present a framework for integrating KA and ML methods within a total knowledge modeling cycle, favoring an iterative rather than a top down approac...

This paper is concerned with the task of incrementally acquiring and refining the knowledge and algorithms of a knowledge-based system in order to improve its performance over time. In particular, we present the design of DE-KART, a tool whose goal is to provide increasing levels of assistance in acquiring and refining indexing and retrieval knowledge for a knowledge-based retrieval system. DE-KART starts with knowledge that has been entered manually, and increases its level of assistance in acquiring and refining that knowledge, both in terms of the increased level of automation in interacting with users, and in terms of the increased generality of the knowledge. DE-KART is at the intersection of machine learning and knowledge acquisition: it is a first step towards a system which moves along a continuum from interactive knowledge acquisition to increasingly automated machine learning as it acquires more knowledge and experience.

This position paper presents preliminary results on defining a method and architecture for building agents that monitor compliance to specified plans or protocols in the healthcare domain. In the paper, we discuss an architectural pattern for a class of autonomous agents known as compliance agents, and the methods used to analyze the problem domain to identify the agents and select their problem-solving architecture. Compliance agents work to insure compliance to a set of prescribed guidelines. Object-oriented analysis methods are used to determine the behaviors of the agents. The principal observation about identifying agent behaviors is that compliance agents track “artifacts ” of compliance through a lifecycle, and that deviations from prescribed compliance indicate opportunities for compliance agents to take remedial action. We use a number of graphical notations as a basis for articulating and exploring the problem-solving architecture for this system. Finally, we demonstrate how this methodology and architecture have been applied in a compliance domain for occupational safety in the healthcare environment. 1.

Recent work within the CBR community has studied more systematic means of interrelating different types of domain knowledge, tasks, and methods for building and maintaining CBR systems. This paper reviews work from the knowledge acquisition community, targeted at methodologies and tools for analysis, modeling, and maintenance of knowledge components. It is argued that the knowledge level is the appropriate level for describing the behavior of an intended CBR system, and for identifying the contents of its knowledge components. A framework is outlined in which a knowledge-level modeling methodology is adapted for the modeling and maintenance of CBR knowledge contents.

A central purpose of knowledge acquisition technology is to assist with the formulation of domain models that underlie knowledge systems. In this article we examine the model formulation process itself as a problem-solving task. Drawing from AI research in qualitative reasoning about physical systems, we characterize the model formulation task in terms of the inputs, the reasoning subtasks, and the knowledge needed to perform the problem solving. We describe the elements of a high-level representation of modeling knowledge, and techniques for providing intelligent assistance to the model builder. Applying the results from engineering modeling to knowledge acquisition in general, we identify properties of the representation that facilitate the construction of knowledge systems from libraries of reusable models. Contente 1. The model formulation task .................................................................................. 2 1.1 The Model Formulation Problem for Engineering .....

Second generation knowledge-based systems have raised the focus of research from rulebased to model-based systems. Model-based systems allow knowledge to be separated into target domain model knowledge and problem solving knowledge. This work supports and builds on the hypothesis that fully object-oriented knowledge-bases provide the best properties for managing large amounts of target domain model knowledge. The ease by which object-oriented representations can be mapped to efficient graphical user interfaces is also beneficial for building interactive graphical knowledge acquisition and maintenance tools. These allow experts to incrementally enter and maintain larger quantities of knowledge in knowledge-bases without support from a knowledge engineer. The thesis points to recent advances in the conception of knowledge-based systems. It shows the need for efficient user interfaces for management of large amounts of heterogeneous knowledge components. It describes a user interface soft...

This report presents a brief review of work on the development of knowledge based tools for requirements capture. A number of different approaches are considered, and an attempt is made to draw these together into a common framework. On the basis of this review, a number of suggestions are made regarding the possibility of providing knowledge based support for a generic approach to requirements capture such as GMARC. 4 1 Introduction Requirements engineering is a knowledge intensive activity: according to Curtis [Cur87], `knowledge application is the key to effective software development'. The incorporation of facilities to provide intelligent or knowledge-based support into the current generation of computer-based tools for requirements capture is therefore likely to lead to enormous benefits in terms of productivity gains. A number of different kinds of knowledge and a number of different AI techniques are likely to be of use. This report presents a brief review of techniques ...

Introduction This position paper presents preliminary results on defining a method and architecture for building agents that monitor compliance to specified plans or protocols in the healthcare domain. This work is being pursued as a precursor to constructing an automated, problem-solving task-specific, knowledge acquisition environment for agent-based compliance monitoring software applications for healthcare. The status of our work is as follows. One agent system has been developed and evaluated for treatment compliance monitoring in a disease management sub-domain, while a second agent system is being developed for compliance monitoring of occupational safety in the healthcare workplace. We believe that our work in creating an architecture for compliance monitoring problems can be used as a basis for building a knowledge-level, taskspecific agent development tool set, along the lines of those created for generic-task based knowledge acquisition environments (Musen 1989; Tu et

considerations for effective knowledge acquisition: