The case for a mixed-initiative collaborative neuroevolution approach

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Risi, Sebastian, Zhang, J., Taarnby, R., Greve, P., Piskur, J., Liapis, A., Togelius, J.: The case for a mixed-initiative collaborative neuroevolution approach. In: Proceedings of the ALIFE Workshop on Artificial Life and the Web (2014)



It is clear that the current attempts at using algorithms to create artificial neural networks have had mixed success at best when it comes to creating large networks and/or complex behavior. This should not be unexpected, as creating an artificial brain is essentially a design problem. Human design ingenuity still surpasses computational design for most tasks in most domains, including architecture, game design, and authoring literary fiction. This leads us to ask which the best way is to combine human and machine design capacities when it comes to designing artificial brains. Both of them have their strengths and weaknesses; for example, humans are much too slow to manually specify thousands of neurons, let alone the billions of neurons that go into a human brain, but on the other hand they can rely on a vast repository of common-sense understanding and design heuristics that can help them perform a much better guided search in design space than an algorithm. Therefore, in this paper we argue for a mixed-initiative approach for collaborative online brain building and present first results towards this goal.

Extended Abstract


 author    = {Sebastian Risi and
              Jinhong Zhang and
              Rasmus Taarnby and
              Peter Greve and
              Jan Piskur and
              Antonios Liapis and
              Julian Togelius},
 title     = {The Case for a Mixed-Initiative Collaborative Neuroevolution Approach},
 journal   = {CoRR},
 volume    = {abs/1408.0998},
 year      = {2014},
 url       = {, },
 timestamp = {Fri, 12 Sep 2014 12:44:21 +0200},
 biburl    = {},
 bibsource = {dblp computer science bibliography,}

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