Online Gamers Classification using K-means

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-10422-5_22In order to achieve flow and increase player retention, it is important that games di culty matches player skills. Being able to evaluate how people play a game is a crucial component for detecting gamers strategies in videogames. One of the main problems in player strategy detection is whether attributes selected to define strategies correctly detect the actions of the player. In this paper, we will study a Real Time Strategy (RTS) game. In RTS the participants make use of units and structures to secure areas of a map and/or destroy the opponents resources. We will extract real-time information about the players strategies at several gameplays through a Web Platform. After gathering enough information, the model will be evaluated in terms of unsupervised learning (concretely, KMeans). Finally, we will study the similitude between several gameplays where players use di erent strategies

Genetic Algorithms Optimized Potential Fields For Decentralized Group Tasking

Maneuvering autonomous agents to accomplish complex tasks is a difficult and typically NP-hard optimization problem with many real-world applications. In this thesis, we use potential fields based on task and agent properties to control the movement of groups of agents and use a genetic algorithm (GA) to optimize potential field parameter values to lead to complex task achieving behaviors. More specifically, we control autonomous unmanned aerial vehicles (UAVs) in search and rescue scenarios to find and help people in need, in wildfire coverage scenarios to monitor a wildfire's perimeter, and game agents in real-time strategy (RTS) games to win skirmishes. In all three applications, potential fields control agent movement, genetic algorithms optimize potential field parameters, and a simulation evaluates task performance to guide genetic optimization. Experimental results show that our potential field representation and problem formulation works well across the three problems. We used UAVs as flying access points and controlled their movement using genetic algorithms optimized potential fields to generate wireless networks. These ad-hoc wireless networks outperformed the current state of the art ad-hoc network deployment algorithm. The same representation with a different set of potential fields was used for successful deployment of UAVs to track the spread of wildfire boundaries and results show that with enough UAVs, complete fire boundary coverage was achieved. Lastly, we used two different RTS game platforms to evolve tactics for a team of heterogeneous game agents by formulating the problem as a multi objective optimization problem. Again using potential fields, a genetic algorithm evolved a diverse set of high quality skirmish tactics ranging from attacking to fleeing against test opponents. Results show that with aggressive attacking tactics, a team of friendly agents was able to eliminate the majority of opponents but suffered significant damage. On the other hand, fleeing tactics resulted in less damage to friendlies but also inflicted less damage to opponents. We also observed the emergence of cooperation between friendly game agents. These results indicate that genetic algorithms optimized potential fields are a viable approach to decentralized group tasking

EEG-Based User Reaction Time Estimation Using Riemannian Geometry Features

Riemannian geometry has been successfully used in many brain-computer interface (BCI) classification problems and demonstrated superior performance. In this paper, for the first time, it is applied to BCI regression problems, an important category of BCI applications. More specifically, we propose a new feature extraction approach for Electroencephalogram (EEG) based BCI regression problems: a spatial filter is first used to increase the signal quality of the EEG trials and also to reduce the dimensionality of the covariance matrices, and then Riemannian tangent space features are extracted. We validate the performance of the proposed approach in reaction time estimation from EEG signals measured in a large-scale sustained-attention psychomotor vigilance task, and show that compared with the traditional powerband features, the tangent space features can reduce the root mean square estimation error by 4.30-8.30%, and increase the estimation correlation coefficient by 6.59-11.13%.Comment: arXiv admin note: text overlap with arXiv:1702.0291

Evolving card sets towards balancing dominion

In this paper we use the popular card game Dominion as a complex test-bed for the generation of interesting and balanced game rules. Dominion is a trading-card-like game where each card type represents a different game mechanic. Each playthrough only features ten different cards, the selection of which can form a new game each time. We compare and analyse three different agents that are capable of playing Dominion on different skill levels and use three different fitness functions to generate balanced card sets. Results reveal that there are particular cards of the game that lead to balanced games independently of player skill and behaviour. The approach taken could be used to balance other games with decomposable game mechanics.peer-reviewe

Using genetic algorithms for real-time dynamic difficulty adjustment in games

Dynamic Difficulty Adjustment is the area of research that seeks ways to balance game difficulty with challenge, making it an engaging experience for all types of players, from novice to veteran, without making it frustrating or boring. In this dissertation we propose an approach that aims to evolve agents, in this case predators, as a group and in real time, in a way that they adapt to a changing environment. We showcase our approach after using a generic genetic algorithm in two scenarios, pitting the predators vs passive prey in one scenario and pitting the predators vs aggressive prey in another, this is done to create a basis for our approach and then test our algorithm in four different scenarios, the first two are the same as the generic genetic algorithm and in the next two we switch prey in the middle of the experience progressively from passive to aggressive or vice versa.Adaptação Dinâmica de Dificuldade é a área de pesquisa que procura formas de equilibrar a dificuldade do jogo com o desafio, tornando-o uma experiência envolvente para todos os tipos de jogadores, desde principiantes a veteranos, sem o tornar frustrante ou aborrecido. Nesta dissertação propomos uma abordagem que visa evoluir os agentes, neste caso predadores, como um grupo e em tempo real, de forma a que estes se adaptem a um ambiente em mudança. Nós mostramos a nossa abordagem depois de usar um algoritmo genético genérico em dois cenários, colocando os predadores versus presas passivas num cenário e colocando os predadores versus presas agressivas noutro, isto é feito para criar uma base para a nossa abordagem e depois testamos o nosso algoritmo em quatro cenários diferentes, os dois primeiros são os mesmos que o algoritmo genético genérico e nos dois seguintes trocamos as presas a meio da experiência progressivamente de passivas para agressivas ou vice-versa

Evolving Agents using NEAT to Achieve Human-Like Play in FPS Games

Artificial agents are commonly used in games to simulate human opponents. This allows players to enjoy games without requiring them to play online or with other players locally. Basic approaches tend to suffer from being unable to adapt strategies and often perform tasks in ways very few human players could ever achieve. This detracts from the immersion or realism of the gameplay. In order to achieve more human-like play more advanced approaches are employed in order to either adapt to the player's ability level or to cause the agent to play more like a human player can or would. Utilizing artificial neural networks evolved using the NEAT methodology, we attempt to produce agents to play a FPS-style game. The goal is to see if the approach produces well-playing agents with potentially human-like behaviors. We provide a large number of sensors and motors to the neural networks of a small population learning through co-evolution. Ultimately we find that the approach has limitations and is generally too slow for practical application, but holds promise for future developments. Many extensions are presented which could improve the results and reduce training times. The agents learned to perform some basic tasks at a very rough level of skill, but were not competitive at even a beginner level

Bayesian opponent modeling in adversarial game environments.

This thesis investigates the use of Bayesian analysis upon an opponent¿s behaviour in order to determine the desired goals or strategy used by a given adversary. A terrain analysis approach utilising the A* algorithm is investigated, where a probability distribution between discrete behaviours of an opponent relative to a set of possible goals is generated. The Bayesian analysis of agent behaviour accurately determines the intended goal of an opponent agent, even when the opponent¿s actions are altered randomly. The environment of Poker is introduced and abstracted for ease of analysis. Bayes¿ theorem is used to generate an effective opponent model, categorizing behaviour according to its similarity with known styles of opponent. The accuracy of Bayes¿ rule yields a notable improvement in the performance of an agent once an opponent¿s style is understood. A hybrid of the Bayesian style predictor and a neuroevolutionary approach is shown to lead to effective dynamic play, in comparison to agents that do not use an opponent model. The use of recurrence in evolved networks is also shown to improve the performance and generalizability of an agent in a multiplayer environment. These strategies are then employed in the full-scale environment of Texas Hold¿em, where a betting round-based approach proves useful in determining and counteracting an opponent¿s play. It is shown that the use of opponent models, with the adaptive benefits of neuroevolution aid the performance of an agent, even when the behaviour of an opponent does not necessarily fit within the strict definitions of opponent ¿style¿.Engineering and Physical Sciences Research Council (EPSRC

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