Conditioning and Concept Formation in Embodied Agents

Terry Stewart and Sharon Wood

Learning algorithms typically model the acquisition of conceptual knowledge from some start state to some fixed learned end state. Natural associative learning demonstrates a more comprehensive range of processes which complement this static view of learning. An experimental regimen is presented for evaluating leaming algorithms against this wider remit. This approach provides a general basis for analysing performance and measuring concept formation. We use it here to examine the Distributed Adaptive Control (DAC2) model.


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