45 research outputs found
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
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
Optimising Agent Behaviours and Game Parameters to Meet Designer’s Objectives
The game industry is one of the biggest economic sector in the entertainment business whose product rely heavily on the quality of the interactivity to stay relevant. Non-Player Character (NPC) is the main mechanic used for this purpose and it has to be optimised for its designated behaviour. The development process iteratively circulates the results among game designers, game AI developers, and game testers. Automatic optimisation of NPCs to designer’s objective will increase the speed of each iteration, and reduce the overall production time. Previous attempts used entropy evaluation metrics which are difficult to translate the terms to the optimising game and a slight misinterpretation often leads to incorrect measurement. This thesis proposes an alternative method which evaluates generated game data with reference result from the testers. The thesis first presents a reliable way to extract information for NPCs classification called Relative Region Feature (RRF). RRF provides an excellent data compression method, a way to effectively classify, and a way to optimise objective-oriented adaptive NPCs. The formalised optimisation is also proved to work on classifying player skill with the reference hall-of-fame scores. The demonstration are done on the on-line competition version of Ms PacMan. The generated games from participating entries provide challenging optimising problems for various evolutionary optimisers. The thesis developed modified version of CMA-ES and PSO to effectively tackle the problems. It also demonstrates the adaptivity of MCTS NPC which uses the evaluation method. This NPC performs reasonably well given adequate resources and no reference NPC is required
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
Recommended from our members
Machine learning methods for public policy : simulation, optimization, and visualization
Society faces many complex management problems, particularly in the area of shared public resources such as ecosystems. Existing decision making processes are often guided by personal experience and political ideology rather than state-of-the-art scientific understanding. This dissertation envisions a future in which multiple stakeholders are provided with computational tools for formalizing their management preferences and computing optimal solutions based on state-of-the-art computational simulations. To make this vision a reality, advances are required in optimization and visualization, and this dissertation presents research on both topics within the formalism of the Markov decision process (MDP). First, it describes an interactive visualization system for understanding the MDP under user-defined management policies, reward functions, and transition dynamics. Second, it presents a method for optimizing management policies for the user-parameterized MDPs. The research is illustrated and validated using a combination of benchmark MDPs and an application to the management of wildfire in ponderosa pine forests. For the wildfire problem, an excellent high-fidelity model of forest growth and wildfire behavior is employed. However, this model is extremely slow, which prevents interactive visualization and optimization. To address simulation computational expense, the dissertation also presents a method for creating a fast surrogate model and shows that this model is sufficiently accurate to support policy optimization and visualization.Keywords: direct policy search, reinforcement learning, testing, artificial intelligence, markov decision processes, public policy, visualization, wildfire, optimization, model-free Monte Carl
Invasive Species in Forests and Rangelands of the United States
This open access book describes the serious threat of invasive species to native ecosystems. Invasive species have caused and will continue to cause enormous ecological and economic damage with ever increasing world trade. This multi-disciplinary book, written by over 100 national experts, presents the latest research on a wide range of natural science and social science fields that explore the ecology, impacts, and practical tools for management of invasive species. It covers species of all taxonomic groups from insects and pathogens, to plants, vertebrates, and aquatic organisms that impact a diversity of habitats in forests, rangelands and grasslands of the United States. It is well-illustrated, provides summaries of the most important invasive species and issues impacting all regions of the country, and includes a comprehensive primary reference list for each topic. This scientific synthesis provides the cultural, economic, scientific and social context for addressing environmental challenges posed by invasive species and will be a valuable resource for scholars, policy makers, natural resource managers and practitioners
The International Linear Collider Technical Design Report - Volume 4: Detectors
The International Linear Collider Technical Design Report (TDR) describes in
four volumes the physics case and the design of a 500 GeV centre-of-mass energy
linear electron-positron collider based on superconducting radio-frequency
technology using Niobium cavities as the accelerating structures. The
accelerator can be extended to 1 TeV and also run as a Higgs factory at around
250 GeV and on the Z0 pole. A comprehensive value estimate of the accelerator
is give, together with associated uncertainties. It is shown that no
significant technical issues remain to be solved. Once a site is selected and
the necessary site-dependent engineering is carried out, construction can begin
immediately. The TDR also gives baseline documentation for two high-performance
detectors that can share the ILC luminosity by being moved into and out of the
beam line in a "push-pull" configuration. These detectors, ILD and SiD, are
described in detail. They form the basis for a world-class experimental
programme that promises to increase significantly our understanding of the
fundamental processes that govern the evolution of the Universe.Comment: See also http://www.linearcollider.org/ILC/TDR . The full list of
signatories is inside the Repor