Thesis (M. Phil.)--University of Queensland, 2002. / Includes bibliographical references.

The goal of this thesis is to explore the use of reinforcement learning (RL) in commercial computer games. Although RL has been applied with success to many types of board games and non-game simulated environments, there has been little work in applying RL to the most popular genres of games: first-person shooters, role-playing games, and real-time strategies. In this thesis we use a first-person shooter environment to create computer players, or bots, that learn to play the game using reinforcement learning techniques. / We have created three experimental bots: ChaserBot, ItemBot and HybridBot. The two first bots each focus on a different aspect of the first-person shooter genre, and learn using basic RL. ChaserBot learns to chase down and shoot an enemy player. ItemBot, on the other hand, learns how to pick up the items---weapons, ammunition, armor---that are available, scattered on the ground, for the players to improve their arsenal. Both of these bots become reasonably proficient at their assigned task. Our goal for the third bot, HybridBot, was to create a bot that both chases and shoots an enemy player and goes after the items in the environment. Unlike the two previous bots which only have primitive actions available (strafing right or left, moving forward or backward, etc.), HybridBot uses options. At any state, it may choose either the player chasing option or the item gathering option. These options' internal policies are determined by the data learned by ChaserBot and ItemBot. HybridBot uses reinforcement learning to learn which option to pick at a given state. / Each bot learns to perform its given tasks. We compare the three bots' ability to gather items, and ChaserBot's and HybridBot's ability to chase their opponent. HybridBot's results are of particular interest as it outperforms ItemBot at picking up items by a large amount. However, none of our experiments yielded bots that are competitive with human players. We discuss the reasons for this and suggest improvements for future work that could lead to competitive reinforcement learning bots.

Thesis (M.A.)--Villanova University, 2008. / Communication Dept. Includes bibliographical references.

Thesis (Ph. D.)--University of Washington, 1997. / Vita. Includes bibliographical references (leaves [41]-45).

Thesis (MA(Psychology))-University of Pretoria, 2006. / Includes bibliographical references.

Thesis (Ph.D.) - University of Queensland, 2006. / Includes bibliography.

Thesis (M.A.)--University of Alberta, 2010. / Title from PDF file main screen (viewed on July 29, 2010). A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of Arts, Department of Humanities Computing, University of Alberta. Includes bibliographical references.

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Yes / Dementia is a serious, progressive, and often debilitating illness with no known cure, having a severe adverse effect on memory, behaviour, reasoning, and communication. A comprehensive review of current refereed research material in the use of games in this area is scarce and suffers from being orientated towards commercially available games or derivatives such as “Dr. Kawashima’s brain training.” There is much lesser concern for bespoke research grade alternatives. This review will attempt to assess the current state of the art in research orientated games for dementia, importantly identifying systems capable of prediction before the onset of the disease. It can be ascertained from the literature reviewed that there are clearly a large number of interactive computer game based mechanisms used for dementia. However, these are each highly intrusive in terms of affecting normal living and the patient is aware of being tested; furthermore their long-term or real benefits are unknown as is their effect over conventional tests. It is important to predict cognitive impairment at a stage early enough to maximise benefit from treatment and therapeutic intervention. Considering the availability, use, and increasing power of modern mobile smartphones, it is logically plausible to explore this platform for dementia healthcare.

Raising the awareness of information security has been the focus of DOD and other government agencies in recent years. There is a need for an effective means of educating and training personnel in the topic of Information Assurance. CyberCIEGE offers an approach to training by engaging the personnel in an interactive simulation-based network security game. Each game scenario in CyberCIEGE is designed to impart some network security principles and Information Assurance concepts to the players. This research developed a scenario definition file for the CyberCIEGE game engine to illustrate and train players on matters related to information protection using compartmentalized Mutlilevel Secure (MLS) systems. The specific area of research is on the protection of sensitive information and operational commands for command and control systems. Through playing this military-based scenario, players can learn about the importance of physical security, the different strategies to protect sensitive information, and the use of MLS systems to provide controlled access to sensitive information. Testing of this game scenario was conducted through the creation of detailed solutions and incorrect gameplay examples.