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

Ms. Pac-Man Versus Ghost Team CIG 2016 competition

This paper introduces the revival of the popular Ms. Pac-Man Versus Ghost Team competition. We present an updated game engine with Partial Observability constraints, a new Multi-Agent Systems approach to developing Ghost agents, and several sample controllers to ease the development of entries. A restricted communication protocol is provided for the Ghosts, providing a more challenging environment than before. The competition will debut at the IEEE Computational Intelligence and Games Conference 2016. Some preliminary results showing the effects of Partial Observability and the benefits of simple communication are also presented

Artificial intelligence in co-operative games with partial observability

This thesis investigates Artificial Intelligence in co-operative games that feature Partial Observability. Most video games feature a combination of both co-operation, as well as Partial Observability. Co-operative games are games that feature a team of at least two agents, that must achieve a shared goal of some kind. Partial Observability is the restriction of how much of an environment that an agent can observe. The research performed in this thesis examines the challenge of creating Artificial Intelligence for co-operative games that feature Partial Observability. The main contributions are that Monte-Carlo Tree Search outperforms Genetic Algorithm based agents in solving co-operative problems without communication, the creation of a co-operative Partial Observability competition promoting Artificial Intelligence research as well as an investigation of the effect of varying Partial Observability to Artificial Intelligence, and finally the creation of a high performing Monte-Carlo Tree Search agent for the game Hanabi that uses agent modelling to rationalise about other players

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

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

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

An Empirical Study on Collective Intelligence Algorithms for Video Games Problem-Solving

Computational intelligence (CI), such as evolutionary computation or swarm intelligence methods, is a set of bio-inspired algorithms that have been widely used to solve problems in areas like planning, scheduling or constraint satisfaction problems. Constrained satisfaction problems (CSP) have taken an important attention from the research community due to their applicability to real problems. Any CSP problem is usually modelled as a constrained graph where the edges represent a set of restrictions that must be verified by the variables (represented as nodes in the graph) which will define the solution of the problem. This paper studies the performance of two particular CI algorithms, ant colony optimization (ACO) and genetic algorithms (GA), when dealing with graph-constrained models in video games problems. As an application domain, the "Lemmings" video game has been selected, where a set of lemmings must reach the exit point of each level. In order to do that, each level is represented as a graph where the edges store the allowed movements inside the world. The goal of the algorithms is to assign the best skills in each position on a particular level, to guide the lemmings to reach the exit. The paper describes how the ACO and GA algorithms have been modelled and applied to the selected video game. Finally, a complete experimental comparison between both algorithms, based on the number of solutions found and the levels solved, is analysed to study the behaviour of those algorithms in the proposed domain

Study of artificial intelligence algorithms applied to the generation of non-playable characters in arcade games

En la actualidad, el auge de la Inteligencia Artificial en diversos campos está llevando a un aumento en la investigación que se lleva a cabo en ella. Uno de estos campos es el de los videojuegos. Desde el inicio de los videojuegos, ha primado la experiencia del usuario en términos de jugabilidad y gráficos, sobre todo, prestando menor atención a la Inteligencia Artificial. Ahora, debido a que cada vez se dispone de mejores máquinas que pueden realizar acciones computacionalmente más caras con menor dificultad, se están pudiendo aplicar técnicas de Inteligencia Artificial más complejas y que aportan mejor funcionamiento y dotan a los juegos de mayor realismo. Este es el caso, por ejemplo, de la creación de agentes inteligentes que imitan el comportamiento humano de una manera más realista. En los últimos años, se han creado diversas competiciones para desarrollar y analizar técnicas de Inteligencia Artificial aplicadas a los videojuegos. Algunas de las técnicas que son objeto de estudio son la generación de niveles, como en la competición de Angry Birds; la minería de datos sacados de registros de juegos MMORPG (videojuego de rol multijugador masivo en línea) para predecir el compromiso económico de los jugadores, en la competición de minería de datos; el desarrollo de IA para desafíos de los juegos RTS (estrategia en tiempo real) tales como la incertidumbre, el procesado en tiempo real o el manejo de unidades, en la competición de StarCraft; o la investigación en PO (observabilidad parcial) en la competición de Ms. Pac-Man mediante el diseño de controladores para Pac-Man y el Equipo de fantasmas. Este trabajo se centra en esta última competición, y tiene como objetivo el desarrollo de una técnica híbrida consistente en un algoritmo genético y razonamiento basado en casos. El algoritmo genético se usa para generar y optimizar un conjunto de reglas que los fantasmas utilizan para jugar contra Ms. Pac-Man. Posteriormente, se realiza un estudio de los parámetros que intervienen en la ejecución del algoritmo genético, para ver como éstos afectan a los valores de fitness obtenidos por los agentes generados.Recently, the increase in the use of Arti cial Intelligence in di erent elds is leading to an increase in the research being carried out. One of these elds is videogames. Since the beginning of videogames, the user experience in terms of gameplay and graphics has prevailed, paying less attention to Arti cial Intelligence for creating more realistic agents and behaviours. Nowadays, due to the availability of better machines that can perform computationally expensive actions with less di culty, more complex Arti cial Intelligence techniques that provide games with better performance and more realism can be implemented. This is the case, for example, of creating intelligent agents that mimic human behaviour in a more realistic way. Di erent competitions are held ever Some of the techniques that are object for study are level generation, such as in the Angry Birds AI Competition, data mining from MMORPG (massively multiplayer online role-playing game) game logs to predict game players' economic engagement, in the Game Data Mining Competition; the development of RTS (Real-Time Strategy) game AI for solving challenging issues such as uncertainty, real-time process and unit management, in the StarCraft AI Competition; or the research into PO (Partial Observability) in the Ms. Pac-Man Vs Ghost Team Competition by designing agents for Ms. Pac-Man and the Ghost Team. This work is focused on this last competition, and has the objective of designing a hybrid technique consisting of a genetic algorithm and case-based reasoning. The genetic algorithm is used to generate and optimize set of rules that the Ghosts use ty year for research into AI techniques through videogames.o play against Ms. Pac-Man. Later, we perform an analysis of the parameters that intervene in the execution of the genetic algorithm to see how they a ect the tness values that the generated agents obtain by playing the game