By Rosemary Laurey
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Extra info for Waiting at Eros
Example text
One solution to this problem is to wrap the legacy components, providing them with an "agent layer" functionality. 1 Agent Taxonomy Several different classifications of agents have been proposed in the literature [92, 100, 101]. The most widely accepted classifications are grouped under the following headings: • Interaction with the user − − Interface agents: users can interact with the system by means of commands. Autonomous agents: there is some interaction with the user but it is the agent who decides whether actions must be taken according to changes in the user behaviour.
In contrast, for unsupervised learning only the first two elements (input and internal dynamics of the network) are available. No external information is used to check on the weight setting process. Human beings appear to be able to learn without explicit supervision. Thus, unsupervised learning mimics this aspect of human learning; hence this type of learning tends to use more biologically plausible methods than those using error descent methods. A pertinent example is the local processing at each synapse in these algorithms, which involves no global information passing through the network.
The activations of the output neurons are fed back via the same weights to the input neurons as inhibition and are therefore subtracted. Then a (nonlinear) function of the weights is calculated and used to update the weights by applying the simple Hebbian learning rule. It was shown in [72] that the use of a nonlinear function f () in the above equations creates an algorithm to find those values of W that maximize that function, subject to the constraint that W is an orthogonal matrix. This idea was applied in [79] to the previously described network in the context of the network performing EPP.
Waiting at Eros by Rosemary Laurey
by Robert
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