Modulating Learning Through Expectation in a Simulated Robotic Setup
In order to survive in an unpredictable and changing environment, an agent has to continuously make sense and adapt to the incoming sensory information and extract those that are behaviorally relevant. At the same time, it has to be able to learn to associate specific actions to these different percepts through reinforcement. Using the biologically grounded Distributed Adaptive Control (DAC) robot-based neuronal model, we have previously shown how these two learning mechanisms (perceptual and behavioral) should not be considered separately but are tightly coupled and interact synergistically via the environment. Through the use of a simulated setup and the unified framework of the DAC architecture, which offers a pedagogical model of the phases that form a learning process, we aim to analyze this perceptual-behavioral binomial and its effects on learning.
KeywordsDistributed Adaptive Control Autonomous synthetic agents Rule learning
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