Abstract not found

A new domain-independent knowledge-based inference structure is presented, specific to the task of abstracting higher-level concepts from time-stamped data. The framework includes a model of time, parameters, events, and contexts. A formal specification of a domains temporal-abstraction knowledge supports acquisition, maintenance, reuse, and sharing of that knowledge.

Problem-solving methods for knowledge-based systems establish the behavior of such systems by defining the roles in which domain knowledge is used and the ordering of inferences. Developers can compose problem-solving methods that accomplish complex application tasks from primitive, reusable methods. The key steps in this development approach are task analysis, method selection "from a library", and method configuration.

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.

The software and knowledge engineering literature defines maintenance strategies for seven main types of knowledge: words; sentences; behavioural knowledge; and meta-knowledge. Meta-knowledge divides into problem solving methods; quality knowledge; fix knowledge; social knowl- 5 edge; and processing activities. There are five main ways in which these seven knowledge types are processed: acquire; operationalise; fault; fix; and preserve. We review systems that contribute to these 7 5 = 35 types of knowledge maintenance. 1 Introduction 10 A general trend in the twentieth century is an increasing level of doubt about the things we speak or write or try to enter into programs. Popper argues that all knowledge is an hypothesis since nothing can ever be ultimately proved; Submitted to the Knowledge Engineering Review page 2 of 73 our currently believed ideas are merely those that have survive active attempts to refute them [89]. Knowledge representation theorists stress that KBs are...

. This paper suggests that a distinction between knowledge acquisition methods should be made. On the one hand there are methods which aim to help the expert and knowledge engineer analyse what knowledge is involved in solving a particular type of problem and how this problem solving is carried out. These methods are concerned with classifying the different types of problem solving and providing tools and methods to help the knowledge engineer identify the appropriate approach and ensure nothing is omitted.. A different approach to knowledge acquisition focuses on ensuring incremental addition of validated knowledge as mistakes are discovered (validated knowledge here means only that the earlier performance of the system is not degraded by the addition of new knowledge). The organisation of this knowledge is managed by the system rather than the expert and knowledge engineer. This would seem to correspond to human incremental development of expertise. From this perspective...

patterns developed by different developers can be different. The number of abstract patterns seems unbounded; practioners keep inventing new one. Practioners don't reuse each others' supposedly reusable abstractions. When we actually experiment with supposedly reusable patterns and productivity, (e.g. the Corbridge study) we see evidence to support the counter-intuitive conclusion that well-formed mature supposedly reusable patterns are less productive than no pattern at all. So, what is the appropriate use of the reuse patterns offered by (e.g.) GOF, GOV, Fowler, KADS, etc? We make two suggestions. Firstly, we should monitor OO patterns for -type problems. Secondly, we not use them as objective canonical versions of truth, but as an assistant in analysis and design. Monitoring Potential Problems with Patterns Reuse We hope we have, at the very least, motivated the need for experimentation to test if patterns are indeed reusable. This section describes a series of suc...

For many years, researchers in knowledge-based systems have worked toward the development of sharable and reusable problem-solving methods and knowledge bases. The aim is to reduce development and maintenance costs, and to build flexible, component-based systems that can be adapted to changing environments. Unfortunately, despite conceptual progress in building and connecting components, there has been little success with large-scale, cross-platform implementations of sharable component libraries.

Reusable problem-solving methods as provided by the PROTÉGÉ-II improve knowledge engineering by allowing developers to design reasoners quickly from pre-existing components. The PROTÉGÉ-II approach allows developers to select methods from a library, and to map the methods to a domain ontology. Still, these methods lack a clear conceptual and formal description that would enable their reuse through matching their competence and assumptions with the available domain knowledge and the given task. KARL is a conceptual and formal knowledge-specification language that provides modeling primitives for specifying problem-solving methods. In this paper, we show how the code and informal descriptions of problem-solving methods in PROTÉGÉ-II can be complemented with the conceptual and formal method definitions in KARL. For our case study we choose two methods from the PROTÉGÉ-II framework: chronological backtracking and a task-specific refinement, the board-game method. In addition to the concept...

Abstract. One approach to providing affordable operator training in the workplace is to augment applications with intelligent embedded training systems (ETS). Intelligent embedded training is highly interactive: trainees practice realistic problem-solving tasks on the prime application with guidance and feedback from the training system. This article makes three contributions to the theory and technology of ETS design. First, we describe a framework based on Norman’s “stages of user activity ” model for defining the instructional objectives of an ETS. Second, we demonstrate a non-invasive approach to instrumenting software applications, thereby enabling them to collaborate with an ETS. Third, we describe a method for interpreting observed user behavior during problem solving, and using that information to provide task-oriented hints on demand.