Title :
Using genetic programming to evolve heuristics for a Monte Carlo Tree Search Ms Pac-Man agent
Author :
Alhejali, Atif M. ; Lucas, Simon M.
Author_Institution :
Sch. of Comput. Sci. & Electron. Eng., Univ. of Essex, Colchester, UK
Abstract :
Ms Pac-Man is one of the most challenging test beds in game artificial intelligence (AI). Genetic programming and Monte Carlo Tree Search (MCTS) have already been successful applied to several games including Pac-Man. In this paper, we use Monte Carlo Tree Search to create a Ms Pac-Man playing agent before using genetic programming to enhance its performance by evolving a new default policy to replace the random agent used in the simulations. The new agent with the evolved default policy was able to achieve an 18% increase on its average score over the agent with random default policy.
Keywords :
Monte Carlo methods; artificial intelligence; computer games; genetic algorithms; tree searching; Al; MCTS; Monte Carlo tree search Ms Pac-Man agent; evolved default policy; game artificial intelligence; genetic programming; random agent; random default policy; Equations; Games; Genetic programming; Mathematical model; Monte Carlo methods; Sociology; Monte Carlo Tree Search; Pac-Man; genetic programming;
Conference_Titel :
Computational Intelligence in Games (CIG), 2013 IEEE Conference on
Conference_Location :
Niagara Falls, ON
Print_ISBN :
978-1-4673-5308-3
DOI :
10.1109/CIG.2013.6633639
