Autonomous systems with explanatory internal models

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Autonomous systems with internal models requires learning algorithms based on variety of trials, methods for extracting general knowledge out of trained results, and hierarchical control structure and its switching mechanisms. To meet these requisites, the following have been studied.As a tool for learning based on variety of trials, the following have been devised : a control mechanism of chaotic behaviors which can produce non-deterministic trials, an optimization method which controls the search range as required, genetic symbiosis algorithm that finds various(sub-)optimal solutions with desirable features, and an optimization procedure that tunes its own design parameters. Their validity has been examined by examples.To achieve extraction of general knowledge, the following have been proposed : neural networks with input gates and a method to merge nodes in Radial Basis Function networks. These enables us to represent the knowledge in a generally applicable way which has a fewer number of inputs(conditions). This has been verfied through learning of behaviors of autonomous moving agents.Neural networks with node gates have been also devised which can realize hierarchical control structure as well as its switching mechanism at the same time. This has been confirmed by examples of nonlinear system control. In addition, in learning of behaviors of autonomous moving agents, a mechanism has been added that extract rules applicable to general environments to are. This makes it possible for the agents to adapt new environment more quickly, which has been demonstrated in examples.