A Multi-Agent Architecture for Knowledge Acquisition
César Augusto Tacla, Angéla Barthes · 2003
This paper concerns a multi-agent system for knowledge management (KM) in research and development (R&D) 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 extra-work. 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.