One very successful approach to developing knowledge acquisition tools use expectations of what the user has to add or may want to add, based on how new knowledge fits within a knowledge base that already exists. When a knowledge base is first created or undergoes significant extensions and changes, these tools cannot provide much support. This paper presents an approach to creating expectations when a new knowledge base is built, and describes a knowledge acquisition tool that we implemented using this approach that supports users in creating problem-solving knowledge. As the knowledge base grows, the knowledge acquisition tool derives more frequent and more reliable expectations that result from enforcing constraints in the knowledge representation system, looking for missing pieces of knowledge in the knowledge base, and working out incrementally the inter-dependencies among the different components of the knowledge base. Our preliminary evaluations show a thirty percent time...

In contrast to current intelligent systems, which must be laboriously programmed for each task they are meant to perform, instructable agents can be taught new tasks and associated knowledge. This thesis presents a general theory of learning from tutorial instruction and its use to produce an instructable agent. Tutorial instruction is a particularly powerful form of instruction, because it allows the instructor to communicate whatever kind of knowledge a student needs at whatever point it is needed. To exploit this broad flexibility, however, a tutorable agent must support a full range of interaction with its instructor to learn a full range of knowledge. Thus, unlike most machine learning tasks, which target deep learning of a single kind of knowledge from a single kind of input, tutorability requires a breadth of learning from a broad range of instructional interactions. The theory of learning from tutorial...

A formal analysis of the problem of parametric design is given on the basis of the competence-theory approach to problem solving methods. In particular, the ontological commitments and assumptions underlying some problem solving methods as used in the VT task are investigated.

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.

Rapid prototyping and tool reusability have pushed knowledge acquisition research to investigate method-specific knowledge acquisition tools appropriate for predetermined problem-solving methods. We believe that method-dependent knowledge acquisition is not the only approach. The aim of our research istodevelop powerful yet versatile machine learning mechanisms that can be incorporated into general-purpose but practical knowledge acquisition tools. This paper shows through examples the practical advantages of this approach. In particular, we illustrate how existing knowledge can be used to facilitate knowledge acquisition through analogy mechanisms within a domain and across domains. Our sample knowledge acquisition dialogues with a domain expert illustrate which parts of the process are addressed by the human and which parts are automated by the tool, in a synergistic cooperation for knowledge-base extension and re nement. The paper also describes briefly the expect problem-solving architecture that facilitates this approach toknowledge acquisition.

The area of knowledge acquisition research concerned with the developmentof knowledge acquisition #KA# tools is in need of a methodological approachtoevaluation. E#orts such as the Sisyphus experiments have been useful to illustrate particular approaches, but have not served in practice as testbeds for comparing and evaluating di#erent alternative approaches. This paper describes our experimental methodology to conduct studies and experiments of users modifying knowledge bases with KA tools. We also report the lessons learned from several experiments that wehave performed. Our hope is that it will help others design or improve future user evaluations of KA tools. We found that performing these experiments is particularly hard because of di#culties in controlling factors that are unrelated to the particular claims being tested. We discuss our ideas for improving our current methodology and some open issues that remain.

Original version submitted to the publisher. An elevator configuration task, the VT task, is modelled within DESIRE as a design task. DESIRE is a framework within which complex reasoning tasks are modelled as compositional architectures. Compositional architectures are based on a task decomposition, acquired during task analysis. An earlier developed generic task model of design, based on a logical analysis and synthesis of task models devised for diverse applications, has been refined for the elevator configuration task. The resulting task model includes a description of the ontology of the elevator domain and a description of the task model. 1.

. In this paper we investigate the reuse of tasks and problem solving methods and we propose a model of how to organize a library of reusable components for knowledge based systems. In our approach we first describe a class of problems by means of a task ontology. Then we instantiate a generic model of problem solving as search in terms of the concepts in the task ontology, to derive a task-specific, but method-independent, problem solving model. Individual problem solving methods can then be (re-)constructed from the generic problem solving model through a process of ontology/method specialization and configuration. The resulting library of reusable components enjoys a clear theoretical basis and has been tested successfully on a number of applications. In the paper we illustrate the approach in the area of parametric design. 1. INTRODUCTION A problem solving method (PSM) is a domain-independent specification of the reasoning process of a knowledge-based system (KBS). PSMs play an ...

: 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...

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