Knowledge Engineering is the aspect of systems engineering which addresses uncertain process requirements by emphasising the acquisition of knowledge about a process and representing this knowledge in a Knowledge Based System. The discipline has moved from one producing small expert systems to one producing embedded KBS in larger computational solutions. This transition has resulted in the development of methodologies that guide the knowledge engineering of a product. The KADS methodology is described to show how structured knowledge engineering of a problem can be based on documented techniques for project management, analysis, and design. However, such techniques still result in the development of independent systems in isolated domains and do not optimise the re-use of general knowledge. The CYC project to represent a portable set of general knowledge that can be used as a basis for any novel system is described. MMI 2 is the third project described which addresses the engineering of knowledge about communication itself, rather than a discrete problem domain, into the interface of computer systems to improve the interaction with the user, as well as the solving of domain problems.

Knowledge Engineering

Intelligent user interfaces promise to improve the interaction for all. Drawing upon material from the recently completed Readings in Intelligent User Interfaces (IUI) (Maybury and Wahlster, 1998), this tutorial will define terms, outline the history, describe key subfields, and exemplify and demonstrate intelligent user interfaces in action.

Intelligent user interfaces: an introduction

This document has been prepared in the ESPRIT BRA No. 8579, Multimodal Integration for Advanced Multimedia Interfaces — in the following referred to as MIAMI — in order to serve as a basis for future work. The basic terms which will be used in MIAMI will be defined and an overview on man-machine-interfaces will be given. The term " taxonomy " is used in the following sense, adapted from [217]: " 1: the study of the general principles of scientific classification: SYSTEMATICS; 2: CLASSIFICATION; specif: orderly classification of plants and animals according to their presumed natural relationships " ; but instead of plants and animals, we attempt to classify input and output modalities.

A taxonomy of Multimodal Interaction in the Human Information Processing System

Techniques for online gesture recognition on workstations

The Web grew in five years from a development project to a global business. In contrast the semantic Web has spent ten years developing from a plan to introduce metadata to the Web to a suite of technologies that are used in niche markets, but are far from the global commodity business of the Web. There remain fundamental problems in implementing the vision of a semantic Web, which require both original technical research and considerable consensus building to reach agreed solutions. Many of the successes of the semantic Web are in small technologies such as RSS, Dublin Core and FOAF, while the main thrust of research is in big technologies such as ontological modeling and inference engines. The links between the small and large, as well as an understanding of the resulting benefits are required to move the semantic Web into the mainstream Web.

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The semantic Web: prospects and challenges

This paper describes the architecture and the first implementation results of a multimodal dialogue interface for knowledge based systems developed in the context of Esprit II project MMI2 The paper reviews the basic principles of the architecture of the system and the approach taken by the project with respect to user modeling issues, then describes individually each communication mode

B. Peroche

Papers

Architecture of a Multimodal Dialogue Interface for Knowledge-Based Systems