Predicting player behavior in Tomb Raider : Underworld

This paper presents the results of an explorative study on predicting aspects of playing behavior for the major commercial title Tomb Raider: Underworld (TRU). Various supervised learning algorithms are trained on a large-scale set of in-game player behavior data, to predict when a player will stop playing the TRU game and, if the player completes the game, how long will it take to do so. Results reveal that linear regression models and other non-linear classification techniques perform well on the tasks and that decision tree learning induces small yet well-performing and informative trees. Moderate performance is achieved from the prediction models, which indicates the complexity of predicting player behavior based on a constrained set of gameplay metrics and the noise existent in the dataset examined, a generic problem in large-scale data collection from millions of remote clients.peer-reviewe

Benchmarky pro projekt Pogamut

Práca sa zaoberá tvorbou a použitím testovacích prostredí pre pocítacom ovládaných agentov (botov) do hry Unreal Tournament 2004. V prvej casti sa snaží identifikovat klúcové prvky botov, ktoré je možné exaktne testovat v prostredí hry a následne vyhodnocovat ich efektivitu. Druhá cast spocíva v návrhu a tvorbe prostredí, v ktorých by sa vybrané prvky otestovali. Cielom tretej casti je vytvorenie referencných botov pre jednotlivé prostredia, ktorí budú slúžit ako základ pre budúci vývoj.In the present work we design and develop the benchmarks for the computer controlled agents (bots) into the game Unreal Tournament 2004. In the first part of the work, we try to identify the most important aspects of the bots which should be tested. In the second part we design and develop the environments for testing the selected aspects. In the third part we implement the reference solutions (bots) for each of the created environments.Department of Software and Computer Science EducationKatedra softwaru a výuky informatikyFaculty of Mathematics and PhysicsMatematicko-fyzikální fakult

API para desenvolvimento de agentes de póquer online

A construção de agentes que simulem o comportamento de jogadores humanos para jogos que se baseiam em informação escondida e de natureza não determinista são uma área muito ativa na investigação em Inteligência Artificial. A variante Texas Hold'em do jogo de Poker fornece um contexto para o estudo da eficácia de várias técnicas de implementação e teorias por causa das propriedades que esta possui bem como a necessidade de lidar com informação escondida e aleatoriedade.A forma mais efetiva de testar, avaliar e comparar as metodologias utilizadas na construção do agente de poker seria poder colocar esse agente a jogar contra humanos em situação de igualdade jogando em ambiente online, em tempo real e com as mesmas restrições de tempo e de dinheiro. Desta forma, esta dissertação consiste no desenvolvimento de uma API que forneça aos agentes as estruturas necessária para jogar poker online, providenciando como input ao agente o estado do jogo e efetuando as jogadas de acordo com o output recebido do agente.Com o estudo do estado da arte realizado, algumas abordagens e técnicas de visão por computador, poderão servir de base e fornecem promissoras esperanças na concretização dos objetivos pretendidos.The creation of agents that simulate human player's behaviour for games that are based on hidden information and non-deterministic nature is a very active area on Artificial Intelligence research domain. The Texas Hold'em variant of poker provides a context for studying the effectiveness of various implementation techniques and theories because of the properties it possesses such as the need to deal with hidden information and stochasticity.The most effective way to test, evaluate and compare methodologies used in the creation of poker agents would be the ability to put those agents playing against humans on an equal footing in a online playing environment at real time and with the same time and money constraints. The focus of this dissertation follows this idea, the creation of an API that provides the agents the necessary structures to play poker online, providing the state of the game as input to the agent and making the moves according to the output received from that agent.With the study of the state of the art, some approaches and techniques of computer vision, could form the basis and provide promising hopes on achieving the intended goals

Selected Computing Research Papers Volume 1 June 2012

An Evaluation of Anti-phishing Solutions (Arinze Bona Umeaku) ..................................... 1 A Detailed Analysis of Current Biometric Research Aimed at Improving Online Authentication Systems (Daniel Brown) .............................................................................. 7 An Evaluation of Current Intrusion Detection Systems Research (Gavin Alexander Burns) .................................................................................................... 13 An Analysis of Current Research on Quantum Key Distribution (Mark Lorraine) ............ 19 A Critical Review of Current Distributed Denial of Service Prevention Methodologies (Paul Mains) ............................................................................................... 29 An Evaluation of Current Computing Methodologies Aimed at Improving the Prevention of SQL Injection Attacks in Web Based Applications (Niall Marsh) .............. 39 An Evaluation of Proposals to Detect Cheating in Multiplayer Online Games (Bradley Peacock) ............................................................................................................... 45 An Empirical Study of Security Techniques Used In Online Banking (Rajinder D G Singh) .......................................................................................................... 51 A Critical Study on Proposed Firewall Implementation Methods in Modern Networks (Loghin Tivig) .................................................................................................... 5

Application of information theory and statistical learning to anomaly detection

In today\u27s highly networked world, computer intrusions and other attacks area constant threat. The detection of such attacks, especially attacks that are new or previously unknown, is important to secure networks and computers. A major focus of current research efforts in this area is on anomaly detection.;In this dissertation, we explore applications of information theory and statistical learning to anomaly detection. Specifically, we look at two difficult detection problems in network and system security, (1) detecting covert channels, and (2) determining if a user is a human or bot. We link both of these problems to entropy, a measure of randomness information content, or complexity, a concept that is central to information theory. The behavior of bots is low in entropy when tasks are rigidly repeated or high in entropy when behavior is pseudo-random. In contrast, human behavior is complex and medium in entropy. Similarly, covert channels either create regularity, resulting in low entropy, or encode extra information, resulting in high entropy. Meanwhile, legitimate traffic is characterized by complex interdependencies and moderate entropy. In addition, we utilize statistical learning algorithms, Bayesian learning, neural networks, and maximum likelihood estimation, in both modeling and detecting of covert channels and bots.;Our results using entropy and statistical learning techniques are excellent. By using entropy to detect covert channels, we detected three different covert timing channels that were not detected by previous detection methods. Then, using entropy and Bayesian learning to detect chat bots, we detected 100% of chat bots with a false positive rate of only 0.05% in over 1400 hours of chat traces. Lastly, using neural networks and the idea of human observational proofs to detect game bots, we detected 99.8% of game bots with no false positives in 95 hours of traces. Our work shows that a combination of entropy measures and statistical learning algorithms is a powerful and highly effective tool for anomaly detection

Enhancing trustability in MMOGs environments

Massively Multiplayer Online Games (MMOGs; e.g., World of Warcraft), virtual worlds (VW; e.g., Second Life), social networks (e.g., Facebook) strongly demand for more autonomic, security, and trust mechanisms in a way similar to humans do in the real life world. As known, this is a difficult matter because trusting in humans and organizations depends on the perception and experience of each individual, which is difficult to quantify or measure. In fact, these societal environments lack trust mechanisms similar to those involved in humans-to-human interactions. Besides, interactions mediated by compute devices are constantly evolving, requiring trust mechanisms that keep the pace with the developments and assess risk situations. In VW/MMOGs, it is widely recognized that users develop trust relationships from their in-world interactions with others. However, these trust relationships end up not being represented in the data structures (or databases) of such virtual worlds, though they sometimes appear associated to reputation and recommendation systems. In addition, as far as we know, the user is not provided with a personal trust tool to sustain his/her decision making while he/she interacts with other users in the virtual or game world. In order to solve this problem, as well as those mentioned above, we propose herein a formal representation of these personal trust relationships, which are based on avataravatar interactions. The leading idea is to provide each avatar-impersonated player with a personal trust tool that follows a distributed trust model, i.e., the trust data is distributed over the societal network of a given VW/MMOG. Representing, manipulating, and inferring trust from the user/player point of view certainly is a grand challenge. When someone meets an unknown individual, the question is “Can I trust him/her or not?”. It is clear that this requires the user to have access to a representation of trust about others, but, unless we are using an open source VW/MMOG, it is difficult —not to say unfeasible— to get access to such data. Even, in an open source system, a number of users may refuse to pass information about its friends, acquaintances, or others. Putting together its own data and gathered data obtained from others, the avatar-impersonated player should be able to come across a trust result about its current trustee. For the trust assessment method used in this thesis, we use subjective logic operators and graph search algorithms to undertake such trust inference about the trustee. The proposed trust inference system has been validated using a number of OpenSimulator (opensimulator.org) scenarios, which showed an accuracy increase in evaluating trustability of avatars. Summing up, our proposal aims thus to introduce a trust theory for virtual worlds, its trust assessment metrics (e.g., subjective logic) and trust discovery methods (e.g., graph search methods), on an individual basis, rather than based on usual centralized reputation systems. In particular, and unlike other trust discovery methods, our methods run at interactive rates.MMOGs (Massively Multiplayer Online Games, como por exemplo, World of Warcraft), mundos virtuais (VW, como por exemplo, o Second Life) e redes sociais (como por exemplo, Facebook) necessitam de mecanismos de confiança mais autónomos, capazes de assegurar a segurança e a confiança de uma forma semelhante à que os seres humanos utilizam na vida real. Como se sabe, esta não é uma questão fácil. Porque confiar em seres humanos e ou organizações depende da percepção e da experiência de cada indivíduo, o que é difícil de quantificar ou medir à partida. Na verdade, esses ambientes sociais carecem dos mecanismos de confiança presentes em interacções humanas presenciais. Além disso, as interacções mediadas por dispositivos computacionais estão em constante evolução, necessitando de mecanismos de confiança adequados ao ritmo da evolução para avaliar situações de risco. Em VW/MMOGs, é amplamente reconhecido que os utilizadores desenvolvem relações de confiança a partir das suas interacções no mundo com outros. No entanto, essas relações de confiança acabam por não ser representadas nas estruturas de dados (ou bases de dados) do VW/MMOG específico, embora às vezes apareçam associados à reputação e a sistemas de reputação. Além disso, tanto quanto sabemos, ao utilizador não lhe é facultado nenhum mecanismo que suporte uma ferramenta de confiança individual para sustentar o seu processo de tomada de decisão, enquanto ele interage com outros utilizadores no mundo virtual ou jogo. A fim de resolver este problema, bem como os mencionados acima, propomos nesta tese uma representação formal para essas relações de confiança pessoal, baseada em interacções avatar-avatar. A ideia principal é fornecer a cada jogador representado por um avatar uma ferramenta de confiança pessoal que segue um modelo de confiança distribuída, ou seja, os dados de confiança são distribuídos através da rede social de um determinado VW/MMOG. Representar, manipular e inferir a confiança do ponto de utilizador/jogador, é certamente um grande desafio. Quando alguém encontra um indivíduo desconhecido, a pergunta é “Posso confiar ou não nele?”. É claro que isto requer que o utilizador tenha acesso a uma representação de confiança sobre os outros, mas, a menos que possamos usar uma plataforma VW/MMOG de código aberto, é difícil — para não dizer impossível — obter acesso aos dados gerados pelos utilizadores. Mesmo em sistemas de código aberto, um número de utilizadores pode recusar partilhar informações sobre seus amigos, conhecidos, ou sobre outros. Ao juntar seus próprios dados com os dados obtidos de outros, o utilizador/jogador representado por um avatar deve ser capaz de produzir uma avaliação de confiança sobre o utilizador/jogador com o qual se encontra a interagir. Relativamente ao método de avaliação de confiança empregue nesta tese, utilizamos lógica subjectiva para a representação da confiança, e também operadores lógicos da lógica subjectiva juntamente com algoritmos de procura em grafos para empreender o processo de inferência da confiança relativamente a outro utilizador. O sistema de inferência de confiança proposto foi validado através de um número de cenários Open-Simulator (opensimulator.org), que mostrou um aumento na precisão na avaliação da confiança de avatares. Resumindo, a nossa proposta visa, assim, introduzir uma teoria de confiança para mundos virtuais, conjuntamente com métricas de avaliação de confiança (por exemplo, a lógica subjectiva) e em métodos de procura de caminhos de confiança (com por exemplo, através de métodos de pesquisa em grafos), partindo de uma base individual, em vez de se basear em sistemas habituais de reputação centralizados. Em particular, e ao contrário de outros métodos de determinação do grau de confiança, os nossos métodos são executados em tempo real

Using trails to improve map generation for virtual agents in large scale, online environments

This thesis looks at improving the generation of maps for intelligent virtual agents in large scale environments. Virtual environments are growing larger in size and becoming more complex. There is a major challenge in providing agents that are able to autonomously generate their own map representations of the environment for use in navigation. Currently, map generation for agents in large scale virtual environments is performed either by hand or requires a lengthy pre-processing step where the map is built online. We are interested in environments where this process is not possible, such as those that encourage user generated content. We look at improving map generation in these environments by using trails. Trails are a set of observations of how a user navigates an environment over time. By observing trails an agent is able to identify free space in an environment and how to navigate between points without needing to perform any collision checking. We found that trails in a virtual environments are a useful source of information for an agent's map building process. Trails can be used to improve rapidly exploring randomised tree and probabilistic roadmap generation, as well as being used as a source of information for segmenting maps in very large scale environments

Game Bot Detection Based on Avatar Trajectory

Abstract. In recent years, online gaming has become one of the most popular Internet activities, but cheating activity, such as the use of game bots, has increased as a consequence. Generally, the gaming community disagrees with the use of game bots, as bot users obtain unreasonable rewards without corresponding efforts. However, bots are hard to detect because they are designed to simulate human game playing behavior and they follow game rules exactly. Existing detection approaches either interrupt the players ’ gaming experience, or they assume game bots are run as standalone clients or assigned a specific goal, such as aim bots in FPS games. In this paper, we propose a trajectory-based approach to detect game bots. It is a general technique that can be applied to any game in which the avatar’s movement is controlled directly by the players. Through real-life data traces, we show that the trajectories of human players and those of game bots are very different. In addition, although game bots may endeavor to simulate players ’ decisions, certain human behavior patterns are difficult to mimic because they are AI-hard. Taking Quake 2 as a case study, we evaluate our scheme’s performance based on real-life traces. The results show that the scheme can achieve a detection accuracy of 95 % or higher given a trace of 200 seconds or longer