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Step 1 of the generation phase within Atlas is the “Sketch” step. This involves a high-level FlowMap (shape and flow) of the entire level. Until now, I had been treating flow as an undirected, weighted graphical model, and using a formal, context-sensitive, parametric L-System grammar to generate it. The parameters of the L-system grammar were to be induced from the sample level during the Analysis Phase.

However, I see now that there might be a more intuitive method of generating this level “Sketch”: An evolutionary algorithm.

Problems with GAs

I'd be loathe to call a classical Genetic Algorithm “creative”, even though it possesses a fitness function, and thus is in a sense 'appreciative'. However, In general, Creativity is actually quite similar to an evolutionary search, albeit with highly deliberate and intelligent 'mutations' and 'crossover'. We tend to like Genetic Algorithms because they can sometimes efficiently search a space via basic operations without needing to actually understand the space. Unfortunately, Genetic Algorithms often have the following drawbacks:

  • GAs do not scale well with complexity: In theory, given a good fitness function and genotype representation, a GA will eventually produce a good result. However when the complexity of the task is great, the time required to converge on a good solution may be completely unreasonable.
    • In part, we will seek to mitigate this in Atlas by creating MANY different GAs which are focused on smaller, specific, narrowly-scoped tasks. It would not work well to use a GA to generate the entire level tile-by-tile, but using it JUST for a high-level sketch might be effective.
  • GAs generally cannot sacrifice short-term loss in order to gain long-term benefit (no ability to plan, anticipate).
    • We will seek to mitigate this in Atlas by introducing
mind/atlas-evolutionary-investigation-1.1462246417.txt.gz · Last modified: 2016/05/02 21:33 by martindm
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