Erkin Bahçeci

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The General Game Playing Competition (Genesereth et al., 2005) poses a unique challenge for artificial intelligence. To be successful, a player must learn to play well in a limited number of example games encoded in first-order logic and then generalize its game play to previously unseen games with entirely different rules. Because good opponents are(More)
When one attempts to use artificial evolution to develop behaviors for a swarm robotic system, he is faced with decisions to be made regarding the parameters of the evolution. In this paper, aggregation behavior is chosen as a case, where performance and scalability of aggregation behaviors of perceptron controllers that are evolved for a simulated swarm(More)
In this study we investigate two approachees for aggregation behavior in swarm robotics systems: Evolutionary methods and probabilistic control. In first part, aggregation behavior is chosen as a case, where performance and scalability of aggregation behaviors of perceptron controllers that are evolved for a simulated swarm robotic system are systematically(More)
Learning is key to achieving human-level intelligence. Transferring knowledge that is learned on one task to another one speeds up learning in the target task by exploiting the relevant prior knowledge. As a test case, this study introduces a method to transfer local pattern-based heuristics from a simple board game to a more complex one. The patterns are(More)
The paper reports the development of a software platform, named PES (Parallelized Evolution System), that parallelizes the fitness evaluations of evolutionary methods over multiple computers connected via a network. The platform creates an infrastructure that allows the dispatching of fitness evaluations onto a group of computers, running both Windows or(More)
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