This thesis investigates the use of machine learning techniques in computer games to create a computer player that adapts to its opponent's game-play. This includes first confirming that machine learning algorithms can be integrated into a modern computer game without have a detrimental effect on game performance, then experimenting with different machine learning techniques to maximize the computer player's performance. Experiments use three machine learning techniques; static prediction models, continuous learning, and reinforcement learning. Static models show the highest initial performance but are not able to beat a simple opponent. Continuous learning is able to improve the performance achieved with static models but the rate of improvement drops over time and the computer player is still unable to beat the opponent. Reinforcement learning methods have the highest rate of improvement but the lowest initial performance. This limits the effectiveness of reinforcement learning because a large number of episodes are required before performance becomes sufficient to match the opponent.

We argue that one of the early goals of color vision is to distinguish one kind of material from another. Accordingly, we show that when a pair of image regions is such that one region has greater intensity at one wavelength than at another wavelength, and the second region has the opposite property, then the two regions are likely to have arisen from distinct materials in the scene. We call this material change circumstance the 'opposite slope sign condition.' With this criterion as a foundation, we construct a representation of spectral information that facilitates the recognition of material changes. Our theory has implications for both psychology and neurophysiology. In particular, Hering's notion of opponent colors and psychologically unique primaries, and Land's results in two-color projection can be interpreted as different aspects of the visual system's goal of categorizing materials. Also, the theory provides two basic interpretations of the function of double-opponent color cells described by neurophysiologists.

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'.

As a bachelor’s degree project, the study examines elements that may affect or distort players’ perception of opponents’ behavior in games. More specifically in stationary character driven games such as Texas Hold ‘em Poker. Elements that were chosen as a focus in the study were visual character design and participants previous experiences, such as knowledge of games in general and familiarity with Texas Hold ‘em Poker. A game prototype was designed as a tool to use when examining the subject. In the game prototype, players were seated at a Texas Hold ‘em Poker table and faced two opponents. One opponent was designed to be visually aggressive and the other visually defensive. Participants of the study were asked to answer a questionnaire and the resulting answers were analyzed in an attempt to find relevant information in regards to the subject of this project. The results of the study suggest that the elements chosen do affect players’ perception of opponents’ behavior. / Som ett kandidatexamensarbete undersöktes vilka element som kan påverka eller förvränga spelares uppfattning om motståndares beteende i spel. Mer specifikt i stationära karaktärsdrivna spel så som Texas Hold ‘em Poker. Element som utvaldes för undersökningen utgjordes av visuell karaktärsdesign och medverkandes tidigare erfarenheter, så som kunskap om spel i allmänhet och kännedom om Texas Hold ‘em Poker. En spelprototyp skapades som ett verktyg för att undersöka ämnet. I spelprototypen sattes spelare vid ett Texas Hold ‘em Poker-bord och mötte två motståndare. En motståndare var utformad att vara visuellt aggressiv och den andra visuellt defensiv. Medverkande i studien blev tillfrågade via ett frågeformulär och de resulterande svaren analyserades i ett försök att hitta relevant information gällande ämnet. Studiens resultat antyder att elementen som valdes verkligen påverkar spelares uppfattning om motståndares beteende i spel.

Commercial video game developers constantly strive to create intelligent humanoid characters that are controlled by computers. To ensure computer opponents are challenging to human players, these characters are often allowed to cheat. Although they appear skillful at playing video games, cheating characters may not behave in a human-like manner and can contribute to a lack of player enjoyment if caught. This work investigates the problem of predicting opponent positions in the video game Counter-Strike: Source without cheating. Prediction models are machine-learned from records of past matches and are informed only by game information available to a human player. Results show that the best models estimate opponent positions with similar or better accuracy than human experts. Moreover, the mistakes these models make are closer to human predictions than actual opponent locations perturbed by a corresponding amount of Gaussian noise.

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The opponent process theory of emotion [Solomon, R.L., and Corbit, J.D. (1974). An opponent-process theory of motivation: I. Temporal dynamics of affect. Psychological Review, 81, 119-143.] predicts a temporary reversal of emotional valence during the recovery from emotional stimulation. We hypothesized that this affective contrast would be apparent in asymmetrical activity patterns in the frontal lobes, and would be more apparent for left frontally active individuals. The present study tested this prediction by examining EEG asymmetries during and after blocked presentations of aversive pictures selected from the International Affective Picture System (IAPS). 12 neutral images, 12 aversive images, and 24 neutral images were presented in blocks. Participants who were right frontally active at baseline did not show changes in EEG asymmetry while viewing aversive slides or after cessation. Participants left frontally active at baseline, however, exhibited greater relative left frontal activity after aversive stimulation than before stimulation. Asymmetrical activity patterns in the frontal lobes may relate to affect regulatory processes, including contrasting opponent after-reactions to aversive stimuli.

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)

Topic: The relationship between the level of technique skills and performance in small- sided games in U12 football players. Objective: To find the diferences in success of defeating an opponent in small-sided games between three groups of players (devided according to level of technical mastery of defeating an opponent in technical exercise without an opponent). To find the relationship between level of technique skill in defeating an opponent and success of defeating an opponent in real condition in small-sided games in U12 football players. Methods: Evaluation was based on our test. Small-sided games were used for evaluation of success of defeating an opponent in situations 1 on 1. Methods of direct and indirect assessment with help of video were used for assessment of the test as well as the small-sided games. Results: Significant effect size differences were found between groups of players in small-sided games in % success of defeating an opponent and % success of keeping the ball in attempt to defeating an opponent. The results suggests that players with higher level of technical skills in defeating an opponent are more successful in defeating an opponent in real game situations. Nevertheless, the statistically significant results weren't found. Key words: Football, motor skills, small-sided...

A Catarata Congênita (CC) é definida como a opacidade do cristalino, que está presente no nascimento ou surge logo após. Pode ser classificada de acordo com sua morfologia, posição, densidade e seu prognóstico varia conforme a morfologia e precocidade do diagnóstico e tratamento. Devido à privação visual na Catarata Congênita, ocorre o impedimento da evolução orgânica e funcional de maneira adequada do sistema visual, levando a alterações que resultam na baixa visão, além afetar componentes da retina e áreas do cérebro relacionados com o processamento de cores. O objetivo deste trabalho foi verificar o impacto da no desenvolvimento da visão de cores e acuidade visual entre crianças tratadas da catarata congênita. Foram avaliados 2 grupos: 16 crianças do grupo controle e 16 crianças do grupo CC. Foi utilizado os Cartões de Acuidade de Teller para avaliar a acuidade visual e uma versão adaptada para avaliação de crianças do Cambridge Colour Test (CCT) para a avaliação dos três eixos de visão de cores (protan, deutan e tritan). Houve redução de Acuidade visual de todas as crianças do grupo CC além de redução nos eixos deutan e protan quando comparados com o grupo controle, principalmente o eixo protan. Foi encontrada correlação entre o eixo protan e acuidade visual, inferindo assim que há uma alteração na retina central e na via parvocelular em crianças com CC / Congenital Cataracts (CC) is defined as lens opacity, present at birth or appearing soon after. This condition can be classified according to morphology, position and density, and the prognosis varies according to morphology and early diagnosis and treatment. Due to visual deprivation in Congenital Cataracts, the organic and functional evolution of the visual system is impaired, leading to changes that result in poor vision, while also affecting retina components and brain areas related to color processing. The aim of this study was to verify the impact on the development of color vision and visual acuity among children who underwent congenital cataracts treatment. Two groups were evaluated: 16 children in the control group and 16 in the CC group. Teller\'s Acuity Cards were used to evaluate visual acuity, and an adapted version of the Cambridge Color Test (CCT) was applied to evaluate the three color vision axes (protan, deutan and tritan). Decreased visual acuity was observed for all children in the CC group, as well as in the deutan and protan axes when compared to the control group, primarily the protan axis. A correlation was detected between the protan axis and visual acuity, thus inferring that changes in the central retina and the parvocellular pathway in children with CC do indeed occur