Erkin Bahceci
Ph.D. Student
Erkin's work focuses on understanding how multiple agents search for the same peaks on a common fitness landscape, or Competitive MultiAgent Search (CMAS). This problem is motivated by innovation search in the real world, i.e. how high-tech companies search for new products. Erkin simulates these processes in NK landscapes, uses evolutionary computation to discover optimal strategies, and verifies the discoveries using human subject data. He is currently trying to finish up his dissertation writing while working full-time at Google, Inc.
Competitive Multi-Agent Search Erkin Bahceci PhD Thesis, Department of Computer Science, The University of Texas at Austin, Austin, TX 78712, Dec... 2014

Transfer of Evolved Pattern-Based Heuristics in Games Erkin Bahceci and Risto Miikkulainen In IEEE Symposium On Computational Intelligence and Games (CIG 2008), 220-227, Perth, Austral... 2008

Coevolving Strategies for General Game Playing Joseph Reisinger, Erkin Bahceci, Igor Karpov and Risto Miikkulainen In Proceedings of the {IEEE} Symposium on Computational Intelligence and Games, 320-327, Pis... 2007

NKVis This package contains a 3D visualization tool for NK fitness landscapes. Two types of visualizations are provided: a 2011

NEAT C++ The NEAT package contains source code implementing the NeuroEvolution of Augmenting Topologies method. The source code i... 2010

rtNEAT C++ The rtNEAT package contains source code implementing the real-time NeuroEvolution of Augmenting Topologies method. In ad... 2006