166 research outputs found
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
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
Predicting Dominance Rankings for Score-Based Games
Game competitions may involve different player roles and be score-based rather than win/loss based. This raises the issue of how best to draw opponents for matches in ongoing competitions, and how best to rank the players in each role. An example is the Ms Pac-Man versus Ghosts Competition which requires competitors to develop software controllers to take charge of the game's protagonists: participants may develop software controllers for either or both Ms Pac-Man and the team of four ghosts. In this paper, we compare two ranking schemes for win-loss games, Bayes Elo and Glicko. We convert the game into one of win-loss ("dominance") by matching controllers of identical type against the same opponent in a series of pair-wise comparisons. This implicitly creates a "solution concept" as to what a constitutes a good player. We analyze how many games are needed under two popular ranking algorithms, Glicko and Bayes Elo, before one can infer the strength of the players, according to our proposed solution concept, without performing an exhaustive evaluation. We show that Glicko should be the method of choice for online score-based game competitions
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
Neuroevolution in Games: State of the Art and Open Challenges
This paper surveys research on applying neuroevolution (NE) to games. In
neuroevolution, artificial neural networks are trained through evolutionary
algorithms, taking inspiration from the way biological brains evolved. We
analyse the application of NE in games along five different axes, which are the
role NE is chosen to play in a game, the different types of neural networks
used, the way these networks are evolved, how the fitness is determined and
what type of input the network receives. The article also highlights important
open research challenges in the field.Comment: - Added more references - Corrected typos - Added an overview table
(Table 1
AI Researchers, Video Games Are Your Friends!
If you are an artificial intelligence researcher, you should look to video
games as ideal testbeds for the work you do. If you are a video game developer,
you should look to AI for the technology that makes completely new types of
games possible. This chapter lays out the case for both of these propositions.
It asks the question "what can video games do for AI", and discusses how in
particular general video game playing is the ideal testbed for artificial
general intelligence research. It then asks the question "what can AI do for
video games", and lays out a vision for what video games might look like if we
had significantly more advanced AI at our disposal. The chapter is based on my
keynote at IJCCI 2015, and is written in an attempt to be accessible to a broad
audience.Comment: in Studies in Computational Intelligence Studies in Computational
Intelligence, Volume 669 2017. Springe
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
Learning to Teach Reinforcement Learning Agents
In this article we study the transfer learning model of action advice under a
budget. We focus on reinforcement learning teachers providing action advice to
heterogeneous students playing the game of Pac-Man under a limited advice
budget. First, we examine several critical factors affecting advice quality in
this setting, such as the average performance of the teacher, its variance and
the importance of reward discounting in advising. The experiments show the
non-trivial importance of the coefficient of variation (CV) as a statistic for
choosing policies that generate advice. The CV statistic relates variance to
the corresponding mean. Second, the article studies policy learning for
distributing advice under a budget. Whereas most methods in the relevant
literature rely on heuristics for advice distribution we formulate the problem
as a learning one and propose a novel RL algorithm capable of learning when to
advise, adapting to the student and the task at hand. Furthermore, we argue
that learning to advise under a budget is an instance of a more generic
learning problem: Constrained Exploitation Reinforcement Learning
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