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Towards Implementing Free Will - Bruce Edmonds

Open-ended strategy evolution

In a standard Genetic Algorithm (GA) following Holland [9], the genome is a fixed length string composed of symbols taken from a finite alphabet. Such a genome can encode only a finite number of strategies. This finiteness imposes a ceiling upon the possible elaboration of strategy. This can be important where individuals are involved in the sort of modelling "arms-race" that can occur in situations of social competition, where the whole panoply of social manoeuvres is possible: alliances, bluff, double-crossing, lies, flattery etc. The presence of a complexity ceiling in such a situation (as would happen with a GA) can change the outcomes in a qualitatively significant way, for example by allowing the existence of a unique optimal strategy that can be discovered.

 This sort of ceiling can be avoided using an open-ended genome structure as happens in Genetic Programming (GP) or messy genetic algorithms. Within these frameworks, strategies can be indefinitely elaborated so that is it possible that any particular strategy can be bettered with sufficient ingenuity. Here I use the GP paradigm, since it provides a sufficiently flexible framework for the purpose in hand. It is based upon a tree-structure which is expressive enough to encode almost any structure including neural-networks, Turing complete finite automata, and computer programs [14]. GP paradigm means that the space of possible strategies is limited only by computational resources. It also has other properties which make it suitable for my purposes:

  1. The process is a path-dependent one since the development of new strategies depends upon the resource of present strategies, providing a continuity of development. This means that not only can completely different styles of strategy be developed but also different ways of approaching (expressing) strategies with similar outcomes.

  3. The population provides an implicit sensitivity to the context of action - different strategies will `surface' at different times as their internal fitnesses change with the entities circumstances. They will probably remain in the population for a while even when they are not the fittest, so that they can `re-emerge' when they become appropriate again. Thus agents using a GP-based decision-making algorithm can appear to `flip' rapidly between strategies as circumstances make this appropriate.

Towards Implementing Free Will - Bruce Edmonds - 16 MAR 0

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