Evolving Minimalistic Control for Complex Behavior

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Traditionally, intelligence was thought to be associated with thinking, reasoning, planning etcetera. More recently there has been an increasing interest in ‘lowlevel’ behavioral responses. It is likely that people make use of a combination of a fast, ‘lower’, automatic system, and an adaptive, ‘higher’, deliberative system. How these systems are combined to produce behavior is uncertain however. One theory suggests the existence of a minimalistic control system that enables complex behavior by biasing the automatic system. Here this theory about how automatic and deliberative structures can be integrated is investigated. This is done by simulating the evolution of simple neural networks in an environment with two different states, during which the appropriateness of an action may also be different. It is expected that purely reactive agents are not able to perform optimally in such an environment. The theory predicts that a control structure/deliberative system will evolve that is inhibitory/modulatory in relation to the automatic system. The control system does not need to be active all the time, only in situations for which the automatic system alone is not adequate. In contrast to expectations, many evolved networks did not evolve hidden units, indicating that the task may not be difficult enough. However, the contextual input units evolved to regulate behavior in a manner similar to the hypothesized control structure, supporting the theory that natural control systems are minimalistic in nature. Further research to clarify the results is suggested.
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