Research Repository

Using genetic programming to evolve heuristics for a Monte Carlo Tree Search Ms Pac-Man agent

Alhejali, AM and Lucas, SM (2013) Using genetic programming to evolve heuristics for a Monte Carlo Tree Search Ms Pac-Man agent. In: UNSPECIFIED, ? - ?.

Full text not available from this repository.

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. © 2013 IEEE.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: IEEE Conference on Computatonal Intelligence and Games, CIG
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 15 Jul 2015 13:29
Last Modified: 17 Aug 2017 17:35
URI: http://repository.essex.ac.uk/id/eprint/14374

Actions (login required)

View Item View Item