n recent years there has been increasing interest in describing complicated information processing systems in terms of the knowledge they have, rather than by the details of their implementation. This requires a means of modeling the knowledge in a system. Several different approaches to knowledge modeling have been developed by researchers working in Artificial Intelligence (AI). Most of these approaches share the view that knowledge must be modeled with respect to a goal or task. In this article , we outline our modeling approach in terms of the notion of a task-structure, which recursively links a task to alternative methods and to their subtasks. Our emphasis is on the notion of modeling domain knowledge using tasks and methods as mediating concepts. We begin by tracing the development of a number of different knowledge-modeling approaches. These approaches share many features, but their differences make it difficult to compare systems that have been modeled using different approaches. We present these approaches and describe their similarities and differences. We then give a detailed description , based on the task structure, of our knowledge-modeling approach and illustrate it with task structures for diagnosis and design. Finally, we show how the task structure can be used to compare and unify the other approaches. A knowledge-based system (KBS) has explicit representations of knowledge as well as inference processes that operate on these representations to achieve a goal. An inference process consists of a number of inference steps, each step creating additional knowledge. The process of applying inference steps is repeated until the information needed to fulfill the requirements of the problem-solving goal or task is generated. Typically, both domain knowledge and possible inference steps have to be modeled and represented in some form. In one sense, knowledge is of general utility-the same piece can be utilized in different contexts and problems; so, unlike traditional procedural approaches, knowledge should not be tied to one task or goal. On the other hand, it is difficult to know what knowledge to put in a system without having an idea of the tasks the KBS will confront. In spite of claims of generality, all KBSs are designed with some task or class of tasks in mind. Similarly, they are designed to be operational across some range of domains. Thus, a clear understanding of the relationship between tasks, knowledge and inferences required to perform the task is needed before knowledge in …

Task-structure analysis for knowledge modeling

Task-Structure Analysis for Knowledge Modeling

We have for several years been working on an approach to knowledge system building that argues for the existence of a close connection between the tasks which the knowledge system is intended to solve, the methods chosen for them and the vocabulary in which knowledge is to be modeled and represented. We trace the historical origins of the idea that we have called Generic Tasks, and outline their evolution and accomplishments based on them. We then critique their original implementations from the perspective of flexible integration. We follow this with an outline of our current generalization of the view in the form of a theory of task structures. We describe the architectural implications of this view and outline some research directions.

Generic tasks and task structures: history, critique and new directions

http://miwalab.cog.human.nagoya-u.ac.jp/database/resume/2006-01-31.pdf

Use of current explanations in multicausal abductive reasoning

Computers as clinicians: an update.

The primary goal of our research is to build an intelligent tutoring system for red blood cell antibody identification. In this paper, we describe the basis for a tutoring system--an expert system called RedSoar. RedSoar is built from two task-specific architectures that were designed for building flexible systems (i.e. systems that can use a variety of problem-solving strategies, and to which knowledge can easily-be added). RedSoar solves the antibody identification task correctly 81% of the time and new knowledge can be added in a straightforward manner. The system is capable of exhibiting human-like behavior which we believe is a necessary condition for building a successful tutoring system.

RedSoar--a system for red blood cell antibody identification.

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RedSoar--a system for red blood cell antibody identification.