Flexible Knowledge Acquisition Through Explicit Representation of Knowledge Roles

Acquisition, Learning, and Demonstration: Automating Tasks for Users

Volume

Papers from the 1996 AAAI Spring Symposium

Contents

A system that acquires knowledge from a user should be able to reflect upon the knowledge that it has--at each moment--and understand what kinds of new knowledge it needs to learn. For the past two decades, research in the area of knowledge acquisition has been moving towards systems that have access to richer representations of knowledge about their task. This paper reviews some well-known knowledge acquisition tools representative of this trend. It also describes our recent work in EXPECT, a system with explicit representations of knowledge about the task and the domain that supports knowledge acquisition for a wider range of tasks and applications than its predecessors. We hope our observations will be useful to researchers in user interfaces and in machine learning concerned with acquiring information from users.