Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

Clãs e Guildas – Dinâmicas, expectativas e motivações do jogar em equipa

Este artigo, tem como principal objectivo analisar e compreender a forma como os clãs ou guildas de jogos on-line funcionam e interagem enquanto sistema, no qual os seus elementos partilham motivações, expectativas e modos de jogar nos jogos multiplayer, Massive Multiplayer On-line Role Playing Game e Massive Multiplayer On-line First Person Shooter. Estes jogos são jogados por várias pessoas ao mesmo tempo ligadas on-line que começam a associar-se em grupos, formando equipas (clãs/guildas) que têm a sua própria identidade, estrutura e hierarquia. Os singleplayers (os jogadores que não estão associados a uma guilda) sentem, normalmente, a necessidade de jogar em grupo, pois consideram que não possuem uma identidade própria dentro do jogo. Assim, o artigo também se debruça sobre os motivos que levam os singleplayers a aderir a uma guilda, assim como as vantagens e as desvantagens que daí advém

Emotional Agents for Shooter Games: Understanding How Players' Emotional Profiles Influence Game Playouts

This project attempts to improve on game testing practices by using human behaviour modelling techniques applied on artificial intelligence agents. By mapping responses to events to variations in Arousal and Valence values, we can cluster those responses and develop a regression model that emulates a cluster members' response to those events, which we can map back to certain types of behaviours , such as evading the enemy. To prove this, we will conduct both a simplified Turing Test, based on captured footage of playthroughs by Humans and AI Agents, as well as comparing simulation parameters, such as accuracy and time taken, to see how closely the AI Agent can emulate human behaviour. To facilitate this, a simulator representative of the games is also developed.Gameplay testing still suffers from inefficient feedback assimilation, not only due to the subjective nature of the retrieved information but also due to the amount of time required to retrieve it. This work attempts to ameliorate that issue by automating the testing process without losing all emotional data. We aim to achieve this by modeling an agent to replicate expected human behaviors. The models are created based on previously collected data from actual gameplay sessions, translated into Arousal and Valence values, through fuzzy clustering. With this proof of concept, we expect to emulate human behaviors in a satisfactory manner and evaluate the usefulness of this method as a quality assurance tool

COMBINED ARTIFICIAL INTELLIGENCE BEHAVIOUR SYSTEMS IN SERIOUS GAMING

This thesis proposes a novel methodology for creating Artificial Agents with semi-realistic behaviour, with such behaviour defined as overcoming common limitations of mainstream behaviour systems; rapidly switching between actions, ignoring “obvious” event priorities, etc. Behaviour in these Agents is not fully realistic as some limitations remain; Agents have a “perfect” knowledge about the surrounding environment, and an inability to transfer knowledge to other Agents (no communication). The novel methodology is achieved by hybridising existing Artificial Intelligence (AI) behaviour systems. In most artificial agents (Agents) behaviour is created using a single behaviour system, whereas this work combines several systems in a novel way to overcome the limitations of each. A further proposal is the separation of behavioural concerns into behaviour systems that are best suited to their needs, as well as describing a biologically inspired memory system that further aids in the production of semi-realistic behaviour. Current behaviour systems are often inherently limited, and in this work it is shown that by combining systems that are complementary to each other, these limitations can be overcome without the need for a workaround. This work examines in detail Belief Desire Intention systems, as well as Finite State Machines and explores how these methodologies can complement each other when combined appropriately. By combining these systems together a hybrid system is proposed that is both fast to react and simple to maintain by separating behaviours into fast-reaction (instinctual) and slow-reaction (behavioural) behaviours, and assigning these to the most appropriate system. Computational intelligence learning techniques such as Artificial Neural Networks have been intentionally avoided, as these techniques commonly present their data in a “black box” system, whereas this work aims to make knowledge explicitly available to the user. A biologically inspired memory system has further been proposed in order to generate additional behaviours in Artificial Agents, such as behaviour related to forgetfulness. This work explores how humans can quickly recall information while still being able to store millions of pieces of information, and how this can be achieved in an artificial system

Enhancing player experience in computer games: A computational Intelligence approach.

Ph.DDOCTOR OF PHILOSOPH

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

E-AI : an emotion architecture for agents in games & virtual worlds

Characters in games and virtual worlds continue to gain improvements in both their visual appearance and more human-like behaviours with each successive generation of hardware. One area that seemingly would need to be addressed if this evolution in human-like characters is to continue is in the area of characters with emotions. To begin addressing this, the thesis focuses on answering the question “Can an emotional architecture be developed for characters in games and virtual worlds, that is built upon a foundation of formal psychology? Therefore a primary goal of the research was to both review and consolidate a range of background material based on the psychology of emotions to provide a cohesive foundation on which to base any subsequent work. Once this review was completed, a range of supplemental material was investigated including computational models of emotions, current implementations of emotions in games and virtual worlds, machine learning techniques suitable for implementing aspects of emotions in characters in virtual world, believability and the role of emotions, and finally a discussion of interactive characters in the form of chat bots and non-player characters. With these reviews completed, a synthesis of the research resulted in the defining of an emotion architecture for use with pre-existing agent behaviour systems, and a range of evaluation techniques applicable to agents with emotions. To support validation of the proposed architecture three case studies were conducted that involved applying the architecture to three very different software platforms featuring agents. The first was applying the architecture to combat bots in Quake 3, the second to a chat bot in the virtual world Second Life, and the third was to a web chat bot used for e-commerce, specifically dealing with question and answers about the companies services. The three case studies were supported with several small pilot evaluations that were intended to look at different aspects of the implemented architecture including; (1) Whether or not users noticed the emotional enhancements. Which in the two small pilot studies conducted, highlighted that the addition of emotions to characters seemed to affect the user experience when the encounter was more interactive such as in the Second Life implementation. Where the interaction occurred in a combat situation with enemies with short life spans, the user experience seemed to be greatly reduced. (2) An evaluation was conducted on how the combat effectiveness of combat bots was affected by the addition of emotions, and in this pilot study it was found that the combat effectiveness was not quite statistically reduced, even when the bots were running away when afraid, or attacking when angry even if close to death. In summary, an architecture grounded in formal psychology is presented that is suitable for interactive characters in games and virtual worlds, but not perhaps ideal for applications where user interaction is brief such as in fast paced combat situations. This architecture has been partially validated through three case studies and includes suggestions for further work especially in the mapping of secondary emotions, the emotional significance of conversations, and the need to conduct further evaluations based on the pilot studies.EThOS - Electronic Theses Online ServiceGBUnited Kingdo