By Gordon Pask, Warren S. McCulloch

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Lf r/ fr . c- oj E'"'lrkd 65 CONTROL SYSTEMS , design will nearly always reduce a many humped distribution like Figure 16 (i) to a single hump like F igure 16 (ii). Given the single peak it is not hard to see how the overall controller should maximize O. It must make trial adj ustments and choose whichever of these gives the greatest positive increment of 9, continuing until the increment of 0 resulting from a (;) (ii) Fig. 16 (1). Multiple maxima in 6 contour on phase space with control parameters as co-ordinates.

Er. - - ~. ~ .. 6J . changing transition probabilities that govern her behaviour) and learns what instructions induce a fruitful altitude. This in an , , IOdustnal system is the method of changing parameters in a subcontroller. Finally, learning may be distributed throughout the system. The hierarchy stili has a real logical status, but no physical location &e Figure 15 (i). J ... Et -yt ".. Fig. I S (ii). ""oo. Fr,. ~ "~'~OL"''' r'£, I I- 9 UAlUATOR IS 0). Equivalent views of an adaptive controller.

Recall Figure 19 of the artifact as a controller coupled to an environment and provided by an external arbiter with a reward variable Let us arrange that increase in (J increases the density of elements that can become active (which is the most general kind of'reward'). Manipulation of 0 will lead to natural selection, favouring the reproduction of those patterns which entail an approved behaviour. · Thenetwork itself cannot be said to evolve (its possible states are always evident). On the other hand, the active regiOn in the network which is the system we refer to as 'learning' docs evolve (an evolutionary network is an assembly which acts as the immediate environment and the material substance of an evolutionary system).

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