Modeling expert players' behavior through data mining

Games are a field in which players are implicitly trained and compelled to solve hard problems optimally. By making an appropriate training experiment using a computer game, players can reach expert performance. A computer game tournament that contains Euclidian traveling salesman problem (ETSP) instances is developed and solutions made by expert players are analyzed with human-behavior hypotheses and data mining techniques. A model for the expert players' behavior that combines apparently different fields is proposed and tested with TSPLIB problems.

[1]  M. Lee,et al.  Intelligence and individual differences in performance on three types of visually presented optimisation problems , 2004 .

[2]  T. Ormerod,et al.  Human performance on the traveling salesman problem , 1996, Perception & psychophysics.

[3]  Mark A. Hall,et al.  Correlation-based Feature Selection for Machine Learning , 2003 .

[4]  Allen Newell,et al.  Heuristic Problem Solving: The Next Advance in Operations Research , 1958 .

[5]  A. Joshi,et al.  The traveling salesman problem: A hierarchical model , 2000, Memory & cognition.

[6]  D Epstein.  On the NP-completeness of cryptarithms , 1987, SIGA.

[7]  J. Bransford.  How people learn , 2000 .

[8]  Ann L. Brown,et al.  How people learn: Brain, mind, experience, and school. , 1999 .

[9]  R. Kaye.  Minesweeper is NP-complete , 2000 .

[10]  Gregory Gutin,et al.  The traveling salesman problem , 2006, Discret. Optim..

[11]  Robert J. Crutcher,et al.  The role of deliberate practice in the acquisition of expert performance. , 1993 .

[12]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.

[13]  A. M. Turing,et al.  Computing Machinery and Intelligence , 1950, The Philosophy of Artificial Intelligence.

[14]  G. Reinelt.  The traveling salesman: computational solutions for TSP applications , 1994 .

[15]  A. M. Turing,et al.  Computing Machinery and Intelligence , 1950, The Philosophy of Artificial Intelligence.

[16]  M. M. Flood.  The Traveling-Salesman Problem , 1956 .

[17]  Erik D. Demaine,et al.  Tetris is hard, even to approximate , 2002, Int. J. Comput. Geom. Appl..

[18]  Herbert A. Simon,et al.  THE MIND'S EYE IN CHESS , 1988 .

[19]  T. Ormerod,et al.  A model of human performance on the traveling salesperson problem , 2000, Memory & cognition.

[20]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .