Specific acquisition of collective belief knowledge for socially motivated multiagent systems

Abstract

Shared beliefs and knowledge are an integral part of an organisation's identity and a prerequisite for collective func­tioning. Multiagent systems that support or emulate cooperative problem solving in such a context cannot be constructed without the explicit acquisition and representation of such collective belief knowledge since it is this knowledge that gov­erns coordination, problem decomposition and task allocation. These are complex issues that have a great influence on the overall effectiveness of multiagent systems. However, in much of Distributed Artificial Intelligence (DAI) research and applications, the mechanisms to deal with these issues are directly related to the data abstraction of the problem to be solved or related to the spatial, hierarchical or other structure inherent in the problem. In other words, there is no explicit knowledge acquisition process to identify knowledge for coordination, problem decomposition or task allocation as is the case with domain knowledge in other knowledge-based system development. This paper illustrates the use of congrega­tive cognitive mapping as a technique to elicit and represent collective belief knowledge and shows that it can be used as metaknowledge for coordination, problem decomposition and task allocation. The development of a prototype multiagent system for strategic vigilance is used to illustrate the technique.