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.