Erkin Bahçeci

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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)
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)
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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