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Towards Implementing Free Will - Bruce Edmonds


Such a set-up does mean that the strategy that is selected by an agent is very unpredictable; what the currently selected strategy is can depend upon the history of the whole population of strategies due to the result of crossover in shuffling sections of the strategies around and the contingency of the evaluation of strategies depending upon the past circumstances of the agent. However the method by which new strategies are produced is not dependent upon the past populations of strategies, so there is no backward recursion of the choice property whereby the presence of free choice at one stage can be `amplified' in the next.

 Thus the next stage is to include the operators of variation in the evolutionary process. In the Koza's original GP algorithm there are only two operators: propagation and tree-crossover. Instead of these two operators I suggest that the population of operators themselves are specified as trees following [4]. These operators are computationally interpreted so they act upon strategies in the base population to produce new variations. The operators are allocated fitness indirectly from the fitnesses of the strategies they produce using the "bucket-brigade" algorithm of Holland [9] or similar (such as that of Baum [1], which is better motivated).

 To complete the architecture we set the population of operators to also operate on themselves in order to drive the production of new operators. Now the decision making processes (including the processes to produce the processes etc.) are generated internally, in response to the twin evolutionary pressures of deciding what to do to further the agents goals (in this case profit) and avoiding being predictable to other agents. This is illustrated in figure 1.

Figure 1One step of a meta-genetic evolutionary process

Towards Implementing Free Will - Bruce Edmonds - 16 MAR 0

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