Some studies in machine learning using the game of checkers. II—Recent progress

Samuel, Arthur L.

doi:10.1016/0066-4138(69)90004-4

Cited by 80 publications

(74 citation statements)

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“…They are also repeatable, which means that the agent can play as many games as needed and accumulate the knowledge obtained based on the outcome of each game. After a computer successfully learned to play checkers (Samuel, 1959(Samuel, , 1967, other board games followed afterwards, such as Chess, Go and Othello.…”

Section: Reinforcement Learningmentioning

confidence: 99%

Reinforcement Learning as a tool to make people move to a specific location in Immersive Virtual Reality

Rovira

Slater

2017

International Journal of Human-Computer Studies

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This paper describes the use of Reinforcement Learning in Immersive Virtual Reality to make a person move to a specific location in a virtual environment. Reinforcement Learning is a sub-area in Machine Learning in which an active entity called agent interacts with its environment and learns how to act in order to achieve a predetermined goal. The Reinforcement Learning had no prior model of behaviour and the participants no prior knowledge that their task was to move to and stay in a specific place. The participants were placed in a virtual environment where they had to avoid collisions with virtual projectiles. Following each projectile the agent analysed the movement made by the participant to determine paths of future projectiles in order to increase the chance of driving participants to the goal position and make them stay there as long as possible. The experiment was carried out with 30 participants, 10 were guided towards the leftmost part of the environment, 10 to the rightmost area, and 10 were used as control group where the projectiles were shot randomly throughout the game. Our results show that people tended

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Section: Reinforcement Learningmentioning

confidence: 99%

Reinforcement Learning as a tool to make people move to a specific location in Immersive Virtual Reality

Rovira

Slater

2017

International Journal of Human-Computer Studies

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“…A popular way of constructing an evaluation function is to make it a (linear) combination of evaluation primitives calledfeatures, and adjust the parameters ofthe combination (Samuel, 1967;Tesauro, 1992;Buro, 2002). Generally, the construction of evaluation functions requires the acquisition of features, and the training of a prediction model (e.g., linear combination).…”

Section: Learning Of Evaluation Functionsmentioning

confidence: 99%

Automated Identification of Patterns in Evaluation Functions

Kaneko

Yamaguchi

Kawai

2004

Advances in Computer Games

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This pa per proposes a general and automated method that generates accurate evaluation functions, without expert players' knowledge of a target game. Patterns (which are partial descriptions of a game state) are widely used as primitives of evaluation functions in game programming. They ha veto be carefully selected in order to generate accurate evaluation functions. Our approach consists of three steps: (1) generation of logic formulae by using the specifications of a target game, (2) translation of the formulae into patterns, and (3) selection of a set of suitable patterns from those generated. The problem, in the automated identification of suitable patterns, is that it is difficult either to generate only useful patterns or to examine ali possible patterns. The latter obstacle is due to the prohibi ti ve numbers involved. We solved this dilemma by a combination of two methods, where one method generates patterns of good quality, and the other method entails a lightweight selection based on statistics that could handle a large number of candidates. Experiments in Othello revealed that about 100,000 patterns from more than eight million automatically generated patterns could be successfully selected with our method, and that accurate evaluation functions were constructed. This accuracy is comparable to that of specialized Othello programs and is much better than that of the evaluation functions generated by existing general methods.

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“…Otra aportación que ha enriquecido al AA, es el estudio de los modelos del cerebro para aproximar estas redes artificiales al aprendizaje de los seres vivos (Gluck and Rumelhart, 2013), (Dayan and Abbott, 2001). Los modelos psicológicos para estudiar el desempeño de los humanos en tareas de aprendizaje (Feigenbaum, 1961), la inteligencia artificial para aprender parámetros de una función (Samuel, 1988) y los modelos evolutivos como los algoritmos genéticos (Goldberg and Holland, 1988), también han coadyuvado al desarrollo de estos sistemas.…”

Section: Introductionunclassified

Aprendizaje Automático en Aplicaciones Fisioterapéuticas

González-Islas

2019

ICBI

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El aprendizaje automático (AA) o aprendizaje máquina se centra en el desarrollo de sistemas de análisis que aprenden de datos existentes para predecir o agrupar datos futuros. El AA se ha desarrollado sustancialmente en años recientes en muchas áreas de la ciencia y de la ingeniería, siendo una de las de mayor interés el área de la medicina. En los ambientes médicos reales existe una gran cantidad de datos de naturaleza multivariable y multidimensional, que requieren analizarse para diagnóstico y tratamiento médico, lo que representa un área de oportunidad importante para el aprendizaje máquina. Este trabajo presenta una revisión de algunas de las aplicaciones más relevantes del aprendizaje automático, así como las tendencias y expectativas emergentes del mismo. Posteriormente, se hace una descripción de los trabajos más significativos del aprendizaje máquina aplicados a la medicina, haciendo especial énfasis en aplicaciones fisioterapéuticas. Finalmente, un tema de particular interés en el contexto de la medicina física es el análisis de la marcha, por lo que se hace una revisión de los trabajos realizados para dicho propósito.

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Some studies in machine learning using the game of checkers. II—Recent progress

Cited by 80 publications

References 0 publications

Reinforcement Learning as a tool to make people move to a specific location in Immersive Virtual Reality

Reinforcement Learning as a tool to make people move to a specific location in Immersive Virtual Reality

Automated Identification of Patterns in Evaluation Functions

Aprendizaje Automático en Aplicaciones Fisioterapéuticas

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