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Identifying Autonomous Agents for Capitalizing Knowledge in R&D environments

Whilst we have a number of techniques for building multiagent systems, such techniques assume that the number and role of each agent in the system is known a priori. Unfortunately it is rarely the case in practice, in particular when trying to build multiagent knowledge management systems in R&D environments exhibiting a high rate of turnover. Thus, we need specific analysis and design guidelines. The paper presents a new method, SAAS, for identifying and selecting a collectively agreed upon set of autonomous agents necessary to implement a system for capitalizing collective knowledge. The method borrows concepts from another method proposed by Grundstein for identifying strategic knowledge in production processes. SAAS was used to determine the number of agents necessary for capitalizing the knowledge of a research group in the MEMOLAB project at UTC. In 1994 we were developing the OSACA1 environment as a set of tools for facilitating the construction of open systems of cognitive agents [Scalabrin et al 96]. The OSACA approach was to be tested in the design of a system for capitalizing the knowledge of a research laboratory, in the MEMOLAB project. The main objective of MEMOLAB was to record part of the knowledge used in a research laboratory for helping the researchers to do a better work, but also for helping the newcomers to be integrated faster, while avoiding to bother the old timers with too many questions. One of the main problems was thus to accommodate a population varying over time in a very dynamic environment, in particular as found in laboratories with a high turnover of Ph.D. students and a small number of permanent researchers. The main approach chosen in the MEMOLAB project was to delegate the management of parts of the knowledge to specific automatic agents, and to develop a system of such agents interacting with human people (through additional proxy agents). A priori and considering our previous experience in building multiagent systems, we thought that it would be relatively straightforward to set up agents as knowledge providers. Our approach to the global problem of knowledge management followed the guidelines developed at the IIIA Institute, and summarized on Fig. 1 which displays one way of looking at the process of knowledge management. The process consists of four steps grouped around a central part referring to strategic or crucial knowledge: (i) identifying and localizing the relevant knowledge; (ii) formalizing it; (iii) distributing it (i.e., providing access to it); (iv) maintaining it. Transversal to this approach was the use of cognitive agents as a supporting technology. 1 Open System for Asynchronous Cognitive Agents