Applying Monte-Carlo Tree Search to collaboratively controlling of a Ghost Team in Ms Pac-Man

Nguyen, Kien; Thawonmas, Ruck

doi:10.1109/igic.2011.6114952

Cited by 8 publications

(6 citation statements)

References 5 publications

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“…Aside from the above, there is not much work on developing ghost agents, and the Ms. Pac-Man versus ghost team competition [7] is motivating further research into this topic. Thus, in [38], Nguyen and Thawonmas present a Monte Carlo tree search team of ghost agents for the above competition.…”

Section: B Techniques Used For Creating Ghost Controllersmentioning

confidence: 99%

AntBot: Ant Colonies for Video Games

Recio

Emilio

Estébanez

et al. 2012

IEEE Trans. Comput. Intell. AI Games

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The video game industry is an emerging market which continues to expand. From its early beginning, developers have focused mainly on sound and graphical applications, paying less attention to developing game bots or other kinds of nonplayer characters (NPCs). However, recent advances in artificial intelligence offer the possibility of developing game bots which are dynamically adjustable to several difficulty levels as well as variable game environments. Previous works reveal a lack of swarm intelligence approaches to develop these kinds of agents. Considering the potential of particle swarm optimization due to its emerging properties and self-adaptation to dynamic environments, further investigation into this field must be undertaken. This research focuses on developing a generic framework based on swarm intelligence, and in particular on ant colony optimization, such as it allows general implementation of real-time bots that work over dynamic game environments. The framework has been adapted to allow the implementation of intelligent agents for the classical game Ms. Pac-Man. These were trialed at the Ms. Pac-Man competitions held during the 2011 International Congress on Evolutionary Computation.

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Section: B Techniques Used For Creating Ghost Controllersmentioning

confidence: 99%

AntBot: Ant Colonies for Video Games

Recio

Emilio

Estébanez

et al. 2012

IEEE Trans. Comput. Intell. AI Games

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“…Tree Search & Monte Carlo [20], [25], [26], [74], [13], [29], [30], [49], [51], [59], [56], [61] Evolutionary Algorithms [68], [69], [47], [45], [46], [48], [53], [50], [58], [57], [59], [60], [63] Neural Networks [70], [38], [75] Neuro-evolutionary [12], [36], [37], [44], [28], [31], [32], [33], [77], [62], [67], [64], [43] Reinforcement Learning [73], [21], [19], [22], [78], [41], [42], [34], [82], [92]…”

Section: Ai / CImentioning

confidence: 99%

“…5) Tree Search & Monte Carlo: Nguyen and Thawonmas [49], [56] introduce the use of Monte Carlo Tree Search (MCTS) to control the Ghost Team, presenting the bot that won the first Ms. Pac-Man Versus Ghost Team Competition at 2011 IEEE Congress on Evolutionary Computation (CEC). In this approach, one of the ghosts (Blinky) moves with a set of predefined rules, while the other three employ MCTS.…”

Section: Ghost Controllersmentioning

confidence: 99%

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Pac-Man Conquers Academia: Two Decades of Research Using a Classic Arcade Game

Rohlfshagen¹,

Liu

Peŕez-Liebana

et al. 2018

IEEE Trans. Games

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Pac-Man and its equally popular successor Ms Pac-Man are often attributed to being the frontrunners of the golden age of arcade video games. Their impact goes well beyond the commercial world of video games and both games have featured in numerous academic research projects over the last two decades. In fact, scientific interest is on the rise and many avenues of research have been pursued, including studies in robotics, biology, sociology and psychology. The most active field of research is computational intelligence, not least because of popular academic gaming competitions that feature Ms Pac-Man. This paper summarises peer-reviewed research that focuses on either game (or close variants thereof) with particular emphasis on the field of computational intelligence. The potential usefulness of games like Pac-Man for higher education is also discussed and the paper concludes with a discussion of prospects for future work.

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“…Nguyen and Thawonmas [21] used MCTS to create a ghost team. Each ghost used its own game tree with a specified, limited depth.…”

Section: Monte Carlo Tree Searchmentioning

confidence: 99%

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

Alhejali

Lucas

2013

2013 IEEE Conference on Computational Inteligence in Games (CIG)

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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.

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Applying Monte-Carlo Tree Search to collaboratively controlling of a Ghost Team in Ms Pac-Man

Cited by 8 publications

References 5 publications

AntBot: Ant Colonies for Video Games

AntBot: Ant Colonies for Video Games

Pac-Man Conquers Academia: Two Decades of Research Using a Classic Arcade Game

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

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