Antonios Liapis — Page 4

arXiv2017

Surprise Search for Evolutionary Divergence

Inspired by the notion of surprise for unconventional discovery we introduce a general search algorithm we name surprise search as a new method of evolutionary divergent search. Surprise search is grounded in the divergent search paradigm and is fabricated within the principles of evolutionary search. The algorithm mimics the self-surprise cognitive process and equips evolutionary search with the ability to seek for solutions that deviate from the algorithm's expected behaviour. The predictive model of expected solutions is based on historical trails of where the search has been and local information about the search space. Surprise search is tested extensively in a robot maze navigation task: experiments are held in four authored deceptive mazes and in 60 generated mazes and compared against objective-based evolutionary search and novelty search. The key findings of this study reveal that surprise search is advantageous compared to the other two search processes. In particular, it outperforms objective search and it is as efficient as novelty search in all tasks examined. Most importantly, surprise search is faster, on average, and more robust in solving the navigation problem compared to any other algorithm examined. Finally, our analysis reveals that surprise search explores the behavioural space more extensively and yields higher population diversity compared to novelty search. What distinguishes surprise search from other forms of divergent search, such as the search for novelty, is its ability to diverge not from earlier and seen solutions but rather from predicted and unseen points in the domain considered.

cs.NE↗

arXiv2014

The Case for a Mixed-Initiative Collaborative Neuroevolution Approach

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.

cs.NE↗