Dynamic concept model learns optimal policies

Dynamic concept model (DCM) is a goal-oriented neural controller, that builds an internal representation of events and chains of events in the form of a directed graph and uses spreading activation for decision making. It is shown, that a special case of DCM is equivalent to reinforcement learning (RL) and is capable of learning the optimal policy in a probabilistic world. The memory and computational requirements of both DCM and RL are analyzed and a special algorithm is introduced, that ensures intentional behavior.<
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