Coevolving Partial Strategies for the Game of Go

A welding robot is provided with a first joint member in addition to a welding gun. A work holder provided with a second joint member adapted to be connected with the first joint member and a clamp mechanism for gripping a workpiece is positioned in a predetermined waiting position. The welding robot accomplishes welding on a workpiece transferred to a first station and when the welding is completed, the welding robot approaches the work holder in the waiting position to connect the first and second joint members with each other. Then the robot returns to the first station carrying the work holder and permits the clamp mechanism of the work holder to grip the workpiece at the first station. Thereafter, the robot moves to a second station to transfer the workpiece from the first station to the second station. Then, the clamp mechanism releases the workpiece and the robot moves away from the second station to the waiting position with the workpiece left at the second station. In the waiting position the first and second joint members are disconnected from each other and the robot returns to the first station with the work holder left in the waiting position.

[1]  A. E. Eiben,et al.  Introduction to Evolutionary Computing , 2003, Natural Computing Series.

[2]  Rémi Coulom,et al.  Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search , 2006, Computers and Games.

[3]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[4]  Richard K. Belew,et al.  Methods for Competitive Co-Evolution: Finding Opponents Worth Beating , 1995, ICGA.

[5]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[6]  David Silver,et al.  Combining online and offline knowledge in UCT , 2007, ICML '07.

[7]  Richard K. Belew,et al.  Coevolutionary search among adversaries , 1997 .

[8]  T. Cazenave,et al.  On the Parallelization of UCT , 2007 .

[9]  Thomas Wolf,et al.  Optimizing GoTools' Search Heuristics using Genetic Algorithms , 2003, J. Int. Comput. Games Assoc..

[10]  Risto Miikkulainen,et al.  Evolving a Roving Eye for Go , 2004, GECCO.

[11]  Alastair Channon,et al.  The N-Strikes-Out Algorithm: A Steady-State Algorithm for Coevolution , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[12]  Bruno Bouzy,et al.  Computer Go: An AI oriented survey , 2001, Artif. Intell..

[13]  Helmut A. Mayer,et al.  Coevolution of neural Go players in a cultural environment , 2005, 2005 IEEE Congress on Evolutionary Computation.

[14]  John R. Koza,et al.  Genetic programming 2 - automatic discovery of reusable programs , 1994, Complex Adaptive Systems.

[15]  J. Pollack,et al.  Coevolutionary dynamics in a minimal substrate , 2001 .

[16]  Annie S. Wu,et al.  Empirical Studies of the Genetic Algorithm with Noncoding Segments , 1995, Evolutionary Computation.

[17]  Elwyn R. Berlekamp,et al.  Mathematical Go - chilling gets the last point , 1994 .

[18]  Csaba Szepesvári,et al.  Bandit Based Monte-Carlo Planning , 2006, ECML.

[19]  Nir Oren,et al.  Evolving Neural Networks for the Capture Game , 2002 .

[20]  Peter Lewis,et al.  MOVE ORDERING VS HEAVY PLAYOUTS : WHERE SHOULD HEURISTICS BE APPLIED IN MONTE CARLO GO ? , 2007 .

[21]  C. Thain Way to go. , 2000, Nursing standard (Royal College of Nursing (Great Britain) : 1987).