<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">David Leake</style></author><author><style face="normal" font="default" size="100%">Kristian Hammond</style></author><author><style face="normal" font="default" size="100%">Lawrence Birnbaum</style></author><author><style face="normal" font="default" size="100%">Cameron Marlow</style></author><author><style face="normal" font="default" size="100%">Howard Yang</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Task-based knowledge management</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the AAAI-99 Workshop Exploring Synergies of Knowledge Management and Case-Based Reasoning</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">artificial intelligence</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1999</style></year></dates><publisher><style face="normal" font="default" size="100%">AAAI Press</style></publisher><pub-location><style face="normal" font="default" size="100%">Orlando, FL</style></pub-location><pages><style face="normal" font="default" size="100%">35-39</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Case-based reasoning is receiving much attention as a technology for building knowledge repositories that can be queried for task-relevant information. Taking the CBR problem-solving model seriously, however, suggests the value of a much stronger integration between knowledge management systems and the tasks that they serve. In this integrated view, knowledge management systems should be designed to do ıt just-in-time retrieval,} anticipating task-based information needs and satisfying them automatically before the user requests information, and should learn unobtrusively by monitoring the user's task performance. Key issues include how to integrate knowledge access into the user's problem-solving process, how to automatically provide the user with task-relevant information from multiple sources, and how to build up knowledge for transmission between task phases and for long-term storage. This paper describes how these issues are addressed in the Stamping Advisor, a system to aid the design of stamped automotive parts. This system automatically presents the designer with needed information in a natural way, uses CBR and task-focused information retrieval to access useful information, and automatically captures relevant information to support downstream task processes and build its memory of cases.</style></abstract></record></records></xml>