Peer-to-peer networks are types of computer networks where each computer (a peer) may have several direct communication channels with other computers. This is similar to how people know and interact with each other, including the problems of how communications among each other take place. Depending on the resources being shared among peers, various protocols have been developed to propagate these resources. These protocols include routing the resources based on similarities between the resources and peers as well as forcing the topology of peers to control different types of resources. Peer-to-peer networks help simulate societies, but communication routing is dependent on the medium being passed among the peers. Games have been a part of human culture for a long time and have not only provided entertainment to people, both individuals and groups, but also a means to better understand the real world by practising on a model world instead. Such models have become more prevalent through the advent of computer games, were virtual worlds can imitate the real world even further with each technological advancement. As these progressions advance, so to does the expectation of multiple persons interacting with each other in a virtual world as they do in the real world. This leads us to the question: “How can computer games be augmented to take advantage of peer-to-peer models?” In this thesis, we explore the possibilities and requirements of running computer games over peer-to-peer networks. We accomplish this by proposing models and mechanisms to be used by all peers to allow a game to be played over a peer-to-peer network. We evaluate our solution to illustrate how well it performs in various scenarios, including the type of peer-to-peer network used, the quality of knowledge models used for our mechanisms and the behaviours of the players themselves.

The abundance of technologies around our children, provides us with resources that can be used in second and foreign language classrooms. Often, children do not have the opportunity to practice Spanish in an authentic way, due to limited Spanish instruction that some institutions or public schools offer at the elementary level. Therefore, the limited time that is allowed to teach should be used wisely by means of computer games in the target language in conjunction with language tasks may offer the opportunity to learn and practice the second language (L2). The purpose of the present report is to: present existing literature on tasks and computer games in foreign/second language learning; suggest how they can be incorporated in a task-based approach in terms of teaching Spanish as an L2 to young learners; show examples of computer games in company with various language tasks that can be used for L2 learning; and provide an example of a lesson plan based on the suggested approach. Also, some of the benefits of this Spanish task-based approach will be discussed. Finally, important teaching implications are offered based on the existing literature on tasks and the task-based approach using computer games that is proposed in the this report. / text

Computer game-playing agents are almost as old as computers themselves, and people have been developing agents since the 1950's. Unfortunately the techniques for game-playing agents have remained basically the same for almost half a century -- an eternity in computer time. Recently developed approaches have shown that it is possible to develop game playing agents with the help of learning algorithms. This study is based on the concept of algorithms that learn how to play board games from zero initial knowledge about playing strategies. A coevolutionary approach, where a neural network is used to assess desirability of leaf nodes in a game tree, and evolutionary algorithms are used to train neural networks in competition, is overviewed. This thesis then presents an alternative approach in which particle swarm optimization (PSO) is used to train the neural networks. Different variations of the PSO are implemented and compared. The results of the PSO approaches are also compared with that of an evolutionary programming approach. The performance of the PSO algorithms is investigated for different values of the PSO control parameters. This study shows that the PSO approach can be applied successfully to train game-playing agents. / Dissertation (MSc)--University of Pretoria, 2007. / Computer Science / Unrestricted

This study investigates the perceptions of prospective computer teachers, who have been studying at the Computer Education and Instructional Technology (CEIT) departments of four different universities, toward the use of computer games with educational features in education. It also examines the future plans of the participants regarding the use of computer games with educational features in their courses or in learning environments that they will design and it explores the participants&rsquo / computer game playing characteristics as well. The subjects of this study were 116 students from the Computer Education and Instructional Technology departments of four universities: Ankara, Gazi, Hacettepe and the Middle East Technical University. The data were collected through a questionnaire and interviews. The data were analyzed by using descriptive statistics and qualitative analysis methods. This study reveals that the prospective computer teachers who participated in this study have positive perceptions toward the use of computer games with educational features in education. Moreover, most of the participants plan to use such games in their future professions according to their responses. However, it is revealed that participants also have doubts about some issues regarding the use of such games in education, although this is a rare case.

This project describes Automine, a program intended to aid in the solving of the Minesweeper computer game. Automine is based on the Linux xwindow C program with xwindow graphic library. The program uses heuristics and probability statistics to help in determining safe squares and squares concealing mines with the goal of allowing a player to achieve minimal time performance. The source code for Automine and for a game simulation is provided in the appendices.

This thesis introduces the original idea that it is possible, and productive, to consider the ‘blending’ of (or deliberate creative combining of methods from) the fields of fine art practice and science practice, using selected empirical research methods to investigate constructions of self and self-identity that emerge between disciplines. In particular, the thesis investigates how the scientific aspects of modern computer games, for instance, can be seen to affect emotional responses from viewers and how those responses are, in turn, affected by the ‘blending’ of aesthetic concerns with consideration of alternative cognitive processes that induce relaxation to connect with participant-players’ self-identities. This process created a method to access cognitive processes, hitherto unexplored by computer-game developers. This research locates its arguments primarily in and between the disciplines, Art and Game Studies and supports the findings with examples taken from art practice and with theories of Psychology and Gaming. This thesis documents the creation of the author’s original hybrid ‘art- work-game’, known as ‘Star World’. It describes the process of ‘Star World’s’ creation, with analysis of the efficacy of this environment as a space where the mapping of narrative, and where perceptual and interactive ‘blendings’ of self and self-identity were employed and tested, with both qualitative and empirical studies of the experiences and perceptions of participant-players. The research focuses on how the distinctive abstract environment, ‘Star World’, affords and facilitates personal expression and interaction for computer-game players. It reveals specific cognitive processes undergone by participant-players; evidence that supports and validates the conjecture that participant-players use personal frames of reference when navigating, exploring and interpreting computer games. Teach-back protocols and their impact are shown to improve the interactivity and immersive potential of the environment. Overall, this thesis classifies ‘haute game’ rules that are formulated to identify virtual environments creating unique, alternative ‘blendings’ with participant-players and assembles a framework for developers to pursue, when producing original computer-game genres. It offers an innovative case study of value to future scholars of Game Studies, as well as to game developers, with cautionary examples provided to assist in dealing with situations where emotional states are accessed by game play. This thesis highlights the potential of interactive art and game design to produce beneficial outcomes for its participant-players, moreover, it demonstrates, with empirical evidence, the effect of the virtual environment on its participant-players.

Approved for public release; distribution is unlimited. / Note: the appendix file for this item is not available. / As the number of computer users continues to grow, attacks on assets stored on computer devices have increased. Despite an increase in computer security awareness, many users and policy makers still do not implement security principles in their daily lives. Ineffective education and the lack of personal experience and tacit understanding might be a main cause. The CyberCIEGE game can be used to convey requisite facts and to generate tacit understanding of general computer security concepts to a broad audience. This thesis asked if a Scenario Definition File (SDF) for the CyberCIEGE game could be developed to educate and train players in Information Assurance on matters related to information integrity in a networking environment. The primary educational concern is the protection of stored data. Another goal was to test whether the game engine properly simulates real world behavior. The research concluded that it is possible to create SDFs for the CyberCIEGE game engine to teach specifically about integrity issues. Three specific SDFs were developed for teaching purposes. Several SDFs were developed to demonstrate the game engine's ability to simulate real world behavior for specific, isolated educational goals. These tests led to recommendations to improve the game engine. / Lieutenant, German Navy

Currently, games with rich environments allowing a wide range of possible interactions and supporting a large number of physical simulations make use of a large number of scripts and bespoke physical simulations, adapted to fit the needs of the game. This thesis proposes a methodology that can be used to tie together various different physical simulations, both off-the-shelf and bespoke, such as rigid body physics, electrical and magnetic simulations to give something greater than the sum of the individual parts. We present a notation for designing the overall physical simulation and a means for the different parts to interact. Experiments using an implementation of the methodology containing electricity, rigid body simulation, magnetics (including electro-magnetics), buoyancy and sound show that it is possible to model everyday objects such an electric motor or a doorbell. These object work ‘as expected’, without the need for special scripts and new, originally unexpected, interactions are possible without further modification of the experiment setup.

Doctor of Philosophy / Computational models of motivation are software reasoning processes designed to direct, activate or organise the behaviour of artificial agents. Models of motivation inspired by psychological motivation theories permit the design of agents with a key reasoning characteristic of natural systems: experience-based attention focus. The ability to focus attention is critical for agent behaviour in complex or dynamic environments where only small amounts of available information is relevant at a particular time. Furthermore, experience-based attention focus enables adaptive behaviour that focuses on different tasks at different times in response to an agent’s experiences in its environment. This thesis is concerned with the synthesis of motivation and reinforcement learning in artificial agents. This extends reinforcement learning to adaptive, multi-task learning in complex, dynamic environments. Reinforcement learning algorithms are computational approaches to learning characterised by the use of reward or punishment to direct learning. The focus of much existing reinforcement learning research has been on the design of the learning component. In contrast, the focus of this thesis is on the design of computational models of motivation as approaches to the reinforcement component that generates reward or punishment. The primary aim of this thesis is to develop computational models of motivation that extend reinforcement learning with three key aspects of attention focus: rhythmic behavioural cycles, adaptive behaviour and multi-task learning in complex, dynamic environments. This is achieved by representing such environments using context-free grammars, modelling maintenance tasks as observations of these environments and modelling achievement tasks as events in these environments. Motivation is modelled by processes for task selection, the computation of experience-based reward signals for different tasks and arbitration between reward signals to produce a motivation signal. Two specific models of motivation based on the experience-oriented psychological concepts of interest and competence are designed within this framework. The first models motivation as a function of environmental experiences while the second models motivation as an introspective process. This thesis synthesises motivation and reinforcement learning as motivated reinforcement learning agents. Three models of motivated reinforcement learning are presented to explore the combination of motivation with three existing reinforcement learning components. The first model combines motivation with flat reinforcement learning for highly adaptive learning of behaviours for performing multiple tasks. The second model facilitates the recall of learned behaviours by combining motivation with multi-option reinforcement learning. In the third model, motivation is combined with an hierarchical reinforcement learning component to allow both the recall of learned behaviours and the reuse of these behaviours as abstract actions for future learning. Because motivated reinforcement learning agents have capabilities beyond those of existing reinforcement learning approaches, new techniques are required to measure their performance. The secondary aim of this thesis is to develop metrics for measuring the performance of different computational models of motivation with respect to the adaptive, multi-task learning they motivate. This is achieved by analysing the behaviour of motivated reinforcement learning agents incorporating different motivation functions with different learning components. Two new metrics are introduced that evaluate the behaviour learned by motivated reinforcement learning agents in terms of the variety of tasks learned and the complexity of those tasks. Persistent, multi-player computer game worlds are used as the primary example of complex, dynamic environments in this thesis. Motivated reinforcement learning agents are applied to control the non-player characters in games. Simulated game environments are used for evaluating and comparing motivated reinforcement learning agents using different motivation and learning components. The performance and scalability of these agents are analysed in a series of empirical studies in dynamic environments and environments of progressively increasing complexity. Game environments simulating two types of complexity increase are studied: environments with increasing numbers of potential learning tasks and environments with learning tasks that require behavioural cycles comprising more actions. A number of key conclusions can be drawn from the empirical studies, concerning both different computational models of motivation and their combination with different reinforcement learning components. Experimental results confirm that rhythmic behavioural cycles, adaptive behaviour and multi-task learning can be achieved using computational models of motivation as an experience-based reward signal for reinforcement learning. In dynamic environments, motivated reinforcement learning agents incorporating introspective competence motivation adapt more rapidly to change than agents motivated by interest alone. Agents incorporating competence motivation also scale to environments of greater complexity than agents motivated by interest alone. Motivated reinforcement learning agents combining motivation with flat reinforcement learning are the most adaptive in dynamic environments and exhibit scalable behavioural variety and complexity as the number of potential learning tasks is increased. However, when tasks require behavioural cycles comprising more actions, motivated reinforcement learning agents using a multi-option learning component exhibit greater scalability. Motivated multi-option reinforcement learning also provides a more scalable approach to recall than motivated hierarchical reinforcement learning. In summary, this thesis makes contributions in two key areas. Computational models of motivation and motivated reinforcement learning extend reinforcement learning to adaptive, multi-task learning in complex, dynamic environments. Motivated reinforcement learning agents allow the design of non-player characters for computer games that can progressively adapt their behaviour in response to changes in their environment.

Thesis (M.S. in computer science)--Washington State University, December 2007. / Includes bibliographical references (p. 77-79).