Artificial Intelligence Techniques Applied To Draughts

This thesis documents the work done to develop a draughts playing program that learns game strategies utilising various Artificial Intelligence (AI) techniques with the goal of being able to play draughts at a reasonably high skill level as a result of having played against itself without external guidance. Context/Background: AI is a fast evolving field of study. The motivation being programming computers to learn from experience should eventually eliminate the need for this detailed, time consuming, and costly programming effort currently required to program solutions to problems. Aims: The aim is to investigate a variety of AI techniques. The program’s effectiveness will be assessed in both evaluating moves and playing a computationally intensive game. Minimax based algorithms together with a basic scoring heuristic are used to evaluate enough of the game tree to pick high utility moves. Later the scoring heuristic is augmented using artificial intelligence techniques. As a result of this training “smart scoring behaviour” the program is expected to learn how to best assign values to each of the squares on the draughts board enabling it to play at an adequately high skill level. Method: In this thesis a version of the board game Draughts is implemented in the Java programming language. Players were developed using a variety of techniques. These algorithms were tested by comparing running times, number of nodes of the game tree searched and the utility of the moves picked. In addition an algorithm is developed to assign scores to given board states using a genetic algorithm. Results: The project was a success for the most part permitting the creation of the game of draughts in the JAVA programming language. Four out of the five proposed move selection techniques were successfully tested in isolation. Finally the genetic algorithm demonstrated the ability to augment the scoring heuristic without the benefit of external guidance in the form of human experience

Ms Pac-Man versus Ghost Team CEC 2011 competition

Games provide an ideal test bed for computational intelligence and significant progress has been made in recent years, most notably in games such as Go, where the level of play is now competitive with expert human play on smaller boards. Recently, a significantly more complex class of games has received increasing attention: real-time video games. These games pose many new challenges, including strict time constraints, simultaneous moves and open-endedness. Unlike in traditional board games, computational play is generally unable to compete with human players. One driving force in improving the overall performance of artificial intelligence players are game competitions where practitioners may evaluate and compare their methods against those submitted by others and possibly human players as well. In this paper we introduce a new competition based on the popular arcade video game Ms Pac-Man: Ms Pac-Man versus Ghost Team. The competition, to be held at the Congress on Evolutionary Computation 2011 for the first time, allows participants to develop controllers for either the Ms Pac-Man agent or for the Ghost Team and unlike previous Ms Pac-Man competitions that relied on screen capture, the players now interface directly with the game engine. In this paper we introduce the competition, including a review of previous work as well as a discussion of several aspects regarding the setting up of the game competition itself. © 2011 IEEE

Evolving Neural Networks for the Capture Game

Postprin

Ludii -- The Ludemic General Game System

While current General Game Playing (GGP) systems facilitate useful research in Artificial Intelligence (AI) for game-playing, they are often somewhat specialised and computationally inefficient. In this paper, we describe the "ludemic" general game system Ludii, which has the potential to provide an efficient tool for AI researchers as well as game designers, historians, educators and practitioners in related fields. Ludii defines games as structures of ludemes -- high-level, easily understandable game concepts -- which allows for concise and human-understandable game descriptions. We formally describe Ludii and outline its main benefits: generality, extensibility, understandability and efficiency. Experimentally, Ludii outperforms one of the most efficient Game Description Language (GDL) reasoners, based on a propositional network, in all games available in the Tiltyard GGP repository. Moreover, Ludii is also competitive in terms of performance with the more recently proposed Regular Boardgames (RBG) system, and has various advantages in qualitative aspects such as generality.Comment: Accepted at ECAI 202

Fast Approximate Max-n Monte Carlo Tree Search for Ms Pac-Man

We present an application of Monte Carlo tree search (MCTS) for the game of Ms Pac-Man. Contrary to most applications of MCTS to date, Ms Pac-Man requires almost real-time decision making and does not have a natural end state. We approached the problem by performing Monte Carlo tree searches on a five player maxn tree representation of the game with limited tree search depth. We performed a number of experiments using both the MCTS game agents (for pacman and ghosts) and agents used in previous work (for ghosts). Performance-wise, our approach gets excellent scores, outperforming previous non-MCTS opponent approaches to the game by up to two orders of magnitude. © 2011 IEEE