Decision trees and neural networks for reasoning and knowledge acquisition for autonomous agents

Abstract

This paper addresses the problem of developing the domain knowledge base for autonomous manufacturing agents (subsystems). In an organizational and behavioural context, autonomous agents consist of elements (people, machines, robots, etc.) tied by the flow of information between an agent and its external environment as well as within an agent. The paper focuses on non-quantitative support that can be used for reasoning and retrieval of knowledge describing such a flow of information in various decision situations. The formal quantitative model can be used to generate some examples of the above domain knowledge. Quantitative models of an information flow evaluation, however, are often too complex to serve as the tools useful in the knowledge retrieval process. In the paper, the application of such artificial intelligence tools as decision trees and neural networks is investigated and illustrated with examples