172 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
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 minerÃa
de datos sacados de registros de juegos MMORPG (videojuego de rol multijugador masivo en
lÃnea) para predecir el compromiso económico de los jugadores, en la competición de minerÃa de
datos; el desarrollo de IA para desafÃos 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 hÃbrida 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
Influence map-based pathfinding algorithms in video games
Path search algorithms, i.e., pathfinding algorithms, are used to solve shortest path problems
by intelligent agents, ranging from computer games and applications to robotics. Pathfinding
is a particular kind of search, in which the objective is to find a path between two nodes. A
node is a point in space where an intelligent agent can travel. Moving agents in physical or
virtual worlds is a key part of the simulation of intelligent behavior. If a game agent is not able
to navigate through its surrounding environment without avoiding obstacles, it does not seem
intelligent. Hence the reason why pathfinding is among the core tasks of AI in computer games.
Pathfinding algorithms work well with single agents navigating through an environment. In realtime
strategy (RTS) games, potential fields (PF) are used for multi-agent navigation in large
and dynamic game environments. On the contrary, influence maps are not used in pathfinding.
Influence maps are a spatial reasoning technique that helps bots and players to take decisions
about the course of the game. Influence map represent game information, e.g., events and
faction power distribution, and is ultimately used to provide game agents knowledge to take
strategic or tactical decisions. Strategic decisions are based on achieving an overall goal, e.g.,
capture an enemy location and win the game. Tactical decisions are based on small and precise
actions, e.g., where to install a turret, where to hide from the enemy.
This dissertation work focuses on a novel path search method, that combines the state-of-theart
pathfinding algorithms with influence maps in order to achieve better time performance and
less memory space performance as well as more smooth paths in pathfinding.Algoritmos de pathfinding são usados por agentes inteligentes para resolver o problema do caminho
mais curto, desde a à rea jogos de computador até à robótica. Pathfinding é um tipo
particular de algoritmos de pesquisa, em que o objectivo é encontrar o caminho mais curto
entre dois nós. Um nó é um ponto no espaço onde um agente inteligente consegue navegar.
Agentes móveis em mundos fÃsicos e virtuais são uma componente chave para a simulação de
comportamento inteligente. Se um agente não for capaz de navegar no ambiente que o rodeia
sem colidir com obstáculos, não aparenta ser inteligente. Consequentemente, pathfinding faz
parte das tarefas fundamentais de inteligencia artificial em vÃdeo jogos.
Algoritmos de pathfinding funcionam bem com agentes únicos a navegar por um ambiente. Em
jogos de estratégia em tempo real (RTS), potential fields (PF) são utilizados para a navegação
multi-agente em ambientes amplos e dinâmicos. Pelo contrário, os influence maps não são usados
no pathfinding. Influence maps são uma técnica de raciocÃnio espacial que ajudam agentes
inteligentes e jogadores a tomar decisões sobre o decorrer do jogo. Influence maps representam
informação de jogo, por exemplo, eventos e distribuição de poder, que são usados para
fornecer conhecimento aos agentes na tomada de decisões estratégicas ou táticas. As decisões
estratégicas são baseadas em atingir uma meta global, por exemplo, a captura de uma zona
do inimigo e ganhar o jogo. Decisões táticas são baseadas em acções pequenas e precisas, por
exemplo, em que local instalar uma torre de defesa, ou onde se esconder do inimigo.
Esta dissertação foca-se numa nova técnica que consiste em combinar algoritmos de pathfinding
com influence maps, afim de alcançar melhores performances a nÃvel de tempo de pesquisa e
consumo de memória, assim como obter caminhos visualmente mais suaves
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
Study of Computational Intelligence Algorithms to Detect Behaviour Patterns
In order to achieve the game flow and increase player retention, it is important that games
difficulty matches player skills. As a consequence, to evaluate how people play a game
is a crucial component, because detecting gamers strategies in video-games, it is possible
to fix the game difficulty. The main problem to detect the strategies is whether attributes
selected to define the strategies correctly detect the actions of the player. To study the
player strategies, we will use a Real Time Stategy (RTS) game. In a RTS the players make
use of units and structures to secure areas of a map and/or destroy the opponents resources.
In this work, we will extract the real-time information about the players strategies using
a platform base on the RTS game. After gathering information, the attributes that define
the player strategies are evaluated using unsupervised learning algorithm (K-Means and
Spectral Clustering). Finally, we will study the similitude among several gameplays where
players use different strategies.A fin de lograr que el flujo del juego mejore y la captación de jugadores aumente, es importante
que la dificultad del juego se ajuste a las habilidades del jugador. Como consecuencia,
evaluar como juega la gente un juego es un aspecto importante, porque detectando las estrategias
de los jugadores en los vÃdeo juegos, permite adapta la dificultad del juego. El
problema principal para detectar las estrategias es si los atributos seleccionados para definir
las estrategias definen correctamente las acciones del jugador. Para estudiar las estrategias
de los jugadores, usaremos un juego de estrategia en tiempo real (Reat Time Strategy (RTS)
en inglés). En un RTS los jugadores hacen uso de unidades y estructuras para asegurar áreas
del mapa y/o destruir los recursos de los oponentes. En este trabajo, extraeremos información
en tiempo real acerca de las estrategias usando una plataforma basada en un juego
de RTS. Después de recoger la información, los atributos que definen las estrategias de los
jugadores son evaluados mediante algoritmos de aprendizaje no supervisado (K-Means y
Spectral Clustering). Finalmente, estudiaremos la similitud entre diversas partidas donde
los jugadores utilizar diferentes estrategias.Este trabajo ha sido financiado por Airbus Defence & Space (Proyecto Savier: FUAM-076914) y parcialmente por TIN2010-19872
Multi-Agent Systems
This Special Issue ""Multi-Agent Systems"" gathers original research articles reporting results on the steadily growing area of agent-oriented computing and multi-agent systems technologies. After more than 20 years of academic research on multi-agent systems (MASs), in fact, agent-oriented models and technologies have been promoted as the most suitable candidates for the design and development of distributed and intelligent applications in complex and dynamic environments. With respect to both their quality and range, the papers in this Special Issue already represent a meaningful sample of the most recent advancements in the field of agent-oriented models and technologies. In particular, the 17 contributions cover agent-based modeling and simulation, situated multi-agent systems, socio-technical multi-agent systems, and semantic technologies applied to multi-agent systems. In fact, it is surprising to witness how such a limited portion of MAS research already highlights the most relevant usage of agent-based models and technologies, as well as their most appreciated characteristics. We are thus confident that the readers of Applied Sciences will be able to appreciate the growing role that MASs will play in the design and development of the next generation of complex intelligent systems. This Special Issue has been converted into a yearly series, for which a new call for papers is already available at the Applied Sciences journal’s website: https://www.mdpi.com/journal/applsci/special_issues/Multi-Agent_Systems_2019
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
AI and IoT Meet Mobile Machines: Towards a Smart Working Site
Infrastructure construction is society's cornerstone and economics' catalyst. Therefore, improving mobile machinery's efficiency and reducing their cost of use have enormous economic benefits in the vast and growing construction market. In this thesis, I envision a novel concept smart working site to increase productivity through fleet management from multiple aspects and with Artificial Intelligence (AI) and Internet of Things (IoT)
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