A Multi-Agent Architecture for Knowledge Acquisition

Cesar A. Tacla and Jean-Paul Barthès

This paper concerns a multi-agent system for knowledge management (KM) in research and development projects. R&D teams have no time to organize project information, or to articulate the rationale behind the actions that generated the information. Our aim is to provide a system for helping team members to make knowledge explicit, and to allow them to share their experiences, i.e., lessons learned (LL), without asking them too much extrawork. The article focuses on how we intend to help the team members to feed the system with LL, using the day-to-day operations they perform on desktop computers, and how we intend to exploit the LL by using a case-based reasoning engine.

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