Title :
Using genetic programming to evolve board evaluation functions
Author :
Ferrer, Gabriel J. ; Martin, W.N.
Author_Institution :
Dept. of Comput. Sci., Virginia Univ., Charlottesville, VA, USA
fDate :
29 Nov-1 Dec 1995
Abstract :
Employs the genetic programming paradigm to enable a computer to learn to play strategies for the ancient Egyptian boardgame Senet by evolving board evaluation functions. Formulating the problem in terms of board evaluation functions made it feasible to evaluate the fitness of game playing strategies by using tournament-style fitness evaluation. The game has elements of both strategy and chance. Our approach learns strategies which enable the computer to play consistently at a reasonably skillful level
Keywords :
game theory; games of skill; genetic algorithms; learning (artificial intelligence); Senet; ancient Egyptian boardgame; board evaluation functions evolution; chance; game playing strategy learning; genetic programming; skill level; tournament-style fitness evaluation; Algorithm design and analysis; Artificial intelligence; Computer science; Functional programming; Genetic programming; Machine intelligence; Random number generation; Roads; Strategic planning; Testing;
Conference_Titel :
Evolutionary Computation, 1995., IEEE International Conference on
Conference_Location :
Perth, WA
Print_ISBN :
0-7803-2759-4
DOI :
10.1109/ICEC.1995.487479
