Acquisition of knowledge for autonomous cooperating agents

In an organizational context autonomous agents consist of groups of people, machines, robots, and/or guided vehicles tied by the flow of information between an agent and its external environment as well as within an agent. Mathematical modeling is used to evaluate such an information flow. The evaluation of an information flow is performed for different types of external and internal environments. Two major cases are taken into account, i.e., static and dynamic processes describing the external environment. Only actions that are described by real numbers and utility functions that are twice differentiable are considered. The results of the model-based evaluation of an information flow in different decision situations are formulated as IF...AND...THEN rules that provide some useful knowledge about autonomous agents functioning. To support the development of a bridge between the distributed systems and artificial intelligence, an approach is suggested that combines knowledge expressed by traditional IF...THEN rules with machine learning technique based on the training of a neural network. A three-layer neural configuration is used. The concepts included are illustrated with examples providing interpretation and relation to real situations.<
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