Knowledge Component of a Multiagent Distributed Decision Support System

We have developed a distributed DSS capable to working in a dynamic way. That is, when a domain of an organization needs a new kind of information, the system looks for this information. This system is based on the usage of mobile agents, which receive the user's queries and visit the appropriate DSS domains to gather the required information. The system itself must analyze where this information can be generated. To make this decision there is an intelligent agent (the Router) with a knowledge base (KB) where the information managed by each domain is represented. In this work, we present a strategy to obtain the initial data to be stored in the KB, a knowledge retrieval mechanism from the KB, and a learning mechanism so that the KB and the DSS operation can be continually improved. The proposed learning process is an interpretative case-based reasoning, which uses a set of rules to analyze the results of the information retrieval process and modifies the content of the router KB. Some examples are presented to illustrate the learning mechanism