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

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

Ghost direction detection and other innovations for Ms. Pac-Man

Ms. Pac-Man was developed in the 1980s, becoming one of the most popular arcade games of its time. It still has a significant following today and has recently attracted the attention of artificial intelligence researchers, in part, due to the fact that the agent must react in real time in order to navigate its way through the maze. This pape

A Survey of Monte Carlo Tree Search Methods

Monte Carlo tree search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarize the results from the key game and nongame domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work

A simple tree search method for playing Ms. Pac-Man

Ms. Pac-Man is a challenging game for software agents that has been the focus of a significant amount of research. This paper describes the current state of a tree-search software agent that will be entered into the IEEE CIG 2009 screen-capture based Ms. Pac-Man software agent competition. While game-tree search is a staple technique for many games, this paper is, perhaps surprisingly, the first attempt we know of to apply it toMs. Pac-Man. The approach we take is to expand a route-tree based on possible moves that the Ms. Pac-Man agent can take to depth 40, and evaluate which path is best using hand-coded heuristics. On a simulator of the game our agent has achieved a high score of 40,000, but only around 15,000 on the original game using a screen-capture interface. Our next steps are focussed on using an improved screen-capture system, and on using evolutionary algorithms to tune the parameters of the agent. ©2009 IEEE

Monte-Carlo Tree Search Algorithm in Pac-Man Identification of commonalities in 2D video games for realisation in AI (Artificial Intelligence)

The research is dedicated to the game strategy, which uses the Monte-Carlo Tree Search algorithm for the Pac-Man agent. Two main strategies were heavily researched for Pac-Man’s behaviour (Next Level priority) and HS (Highest Score priority). The Pacman game best known as STPacman is a 2D maze game that will allow users to play the game using artificial intelligence and smart features such as, Panic buttons (where players can activate on or off when they want and when they do activate it Pacman will be controlled via Artificial intelligence). A Variety of experiments were provided to compare the results to determine the efficiency of every strategy. A lot of intensive research was also put into place to find a variety of 2D games (Chess, Checkers, Go, etc.) which have similar functionalities to the game of Pac-Man. The main idea behind the research was to see how effective 2D games will be if they were to be implemented in the program (Classes/Methods) and how well would the artificial intelligence used in the development of STPacman behave/perform in a variety of different 2D games. A lot of time was also dedicated to researching an ‘AI’ engine that will be able to develop any 2D game based on the users submitted requirements with the use of a spreadsheet functionality (chapter 3, topic 3.3.1 shows an example of the spreadsheet feature) which will contain near enough everything to do with 2D games such as the parameters (The API/Classes/Methods/Text descriptions and more). The spreadsheet feature will act as a tool that will scan/examine all of the users submitted requirements and will give a rough estimation(time) on how long it will take for the chosen 2D game to be developed. It will have a lot of smart functionality and if the game is not unique like chess/checkers it will automatically recognize it and alert the user of it

Developing an Effective and Efficient Real Time Strategy Agent for Use as a Computer Generated Force

Computer Generated Forces (CGF) are used to represent units or individuals in military training and constructive simulation. The use of CGF significantly reduces the time and money required for effective training. For CGF to be effective, they must behave as a human would in the same environment. Real Time Strategy (RTS) games place players in control of a large force whose goal is to defeat the opponent. The military setting of RTS games makes them an excellent platform for the development and testing of CGF. While there has been significant research in RTS agent development, most of the developed agents are only able to exhibit good tactical behavior, lacking the ability to develop and execute overall strategies. By analyzing prior games played by an opposing agent, an RTS agent can determine the opponent\u27s strengths and weaknesses and develop a strategy which neutralizes the strengths and capitalizes on the weaknesses. It can then execute this strategy in an RTS game. This research develops such an RTS agent called the Killer Bee Artificial Intelligence (KBAI). KBAI builds a classifier for an opposing RTS agent which allows it to predict game outcomes. It then takes this classifier, uses it to generate an effective counter-strategy, and executes the tactics required for the strategy. KBAI is both effective and efficient against four high-quality scripted agents: it wins 100% of the time, and it wins quickly. When compared to native artificial intelligence, KBAI has superior performance. It exhibits strategic behavior, as well as the tactics required to execute a developed strategy

Uma arquitetura de subsunção com capacidades adaptativas preditivas para o Pacman

Dissertação de mest., Engenharia Informática, Faculdade de Ciências e Tecnologia, Univ. do Algarve, 2012Os jogos de computador são um domínio de estudo muito importante na área da inteligência computacional. Essa importância advém das propriedades de seus ambientes: multi-agente, competitivos, estocásticos e dinâmicos; onde a verificação de sucesso ou fracasso é de fácil verificação. Para além disso, os jogos e o entretenimento digital em geral, são uma industria em expansão que gera um volume de negócios considerável. O objetivo deste trabalho é desenvolver um agente para controlar o famoso pacman, capaz de participar numa das mais populares competições, organizadas pela conferência IEEE em inteligência computacional e jogos. Ganha a competição quem conseguir a pontuação média mais elevada de 3 execuções por equipa de fantasmas. O objetivo das equipas de fantasmas é fazer diminuir essas pontuações A dificuldade do pacman deve-se ao facto de fornecer um ambiente estocástico, dinâmico, parcialmente observável, ser um jogo do tipo predador/presa com 4 predadores e ocorrer dentro de um labirinto, o que condiciona os movimentos. A abordagem proposta neste trabalho é estender a arquitetura reativa de Brooks, a chamada arquitetura de subsunção com capacidades adaptativas e preditivas. O agente assim construído deverá ser capaz de prever o movimento dos fantasmas ao longo de um horizonte temporal no futuro, baseando-se num modelo que é atualizado com informação recolhida no passado e usar essas previsões para decidir o que fazer a seguir

Artificial intelligence techniques towards adaptive digital games

Digital games rely on suspension of disbelief and challenge to immerse players in the game experience. Artificial Intelligence (AI) has a significant role in this task, both to provide adequate challenges to the player and to generate believable behaviours. An emerging area of application for digital games is augmented reality. Theme parks are expressing interest to augment the user experience via digital games. Bringing together cyber- and physical- aspects in theme parks will provide new avenues of entertainment to customers. This thesis contributes to the field of AI in games, in particular by proposing techniques aimed at improving players' experience. The technical contributions are in the field of learning from demonstration, abstraction in learning and dynamic difficulty adjustment. In particular, we propose a novel approach to learn options for the Options framework from demonstrations; a novel approach to handle progressively refined state abstractions for the Reinforcement Learning framework; a novel approach to dynamic difficulty adjustment based on state-action values, which in our experiments we compute via Monte Carlo Tree Search. All proposed techniques are tested in video games. The final contribution is an analysis of real-world data, collected in a theme park queuing area where we deployed an augmented reality mobile game; the data we collected suggests that digital games in such environments can benefit from AI techniques, which can improve time perception in players. Time perception, in fact, is altered when players enter the state of "flow", which can only happen if suspension of disbelief is maintained and if the level of challenge is adequate. The conclusion suggests this is a promising direction for investigation in future work