Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551039
Pertenecen a esta colección Tesis y Trabajos de grado de las Maestrías correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- Use of reinforcement learning to help players improve their skills in Super Smash Bros. Melee(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-11-22) Estrada Valles, Jorge Alberto; Ramírez Uresti, Jorge Adolfo; puelquio/tolmquevedo; Morales Manzanares, Eduardo; Sosa Hernández, Víctor Adrián; Medina Pérez, Miguel Ángel; Ingenieria y Ciencias; Campus MonterreyeSports have become a huge industry in recent years which has led to more and more people being interested in competing as professional players, however not all players have the same opportunities as things like the current residence of the player are a huge factor. This is especially true for fighting games as people who live in small cities or countries usually have the problem of finding people with whom to practice and even then it may not be the best practice, so people opt to play against in-game AI which is also not good practice. Due to this problem new and more accessible ways for players to train must be created which is why a reinforcement learning solution is proposed. In this thesis, we present a solution using Proximal Policy Optimization to help people train when their best option is against the in-game AI. Furthermore, several additions, namely multiple time step actions, reward shaping, and specialized training; are suggested to optimize the created model to be used as a training partner by a human. To evaluate the effectiveness of the resulting model the game named Super Smash Bros. Melee was used to compare the improvement achieved by training against our bot and against the in-game AI. The results show that people that trained against the bot improved more than the people that trained against the AI, proving that it is a good way to help players train for eSport competitions.
- Detecting empathy on textual communication(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-11) Montiel Vázquez, Edwin Carlos; RAMIREZ URESTI, JORGE ADOLFO; 21998; Ramírez Uresti, Jorge Adolfo; emijzarate/puemcuervo; Monroy Borja, Raúl; González Mendoza, Miguel; Montes y Gómez, Manuel; School of Engineering and Sciences; Campus Estado de México; Loyola González, OctavioEmpathy is a necessary component of human communication. The ability to understand and relate to others provides depth to any conversation between people, and is the basis for any exchange that deals with highly emotional topics. Current technological developments have raised interest in human-like behavior from computer systems regarding communication. This has led to the development of the area known as Affective computing, which is based on the study and processing of concepts related to emotions through artificial intelligence. However, in this area, empathy has been largely ignored in favor of other concepts such as emotion and feeling. This can be attributed to the complexity inherent of the concept. Nevertheless, there are now several methods that can be used to finally study and take advantage of empathy in computer applications. We provide a comprehensive study on the nature of empathy and a method for detecting it in textual communication. Thanks to this research, we present a database of conversations with their respective measurement of empathy. This metric, the Empathy score, is the first method for measuring empathy on texts based on psychological research. In order to detect the value of empathy on conversations, we apply machine learning classification. A pattern-based classification approach was taken in order to predict the Empathy score of utterances in our database, which allowed us to explore the advantages presented by these algorithms in psychologically-adjacent computing research. We were able to use methods found in computer science for the study and detection of empathy, and prove the viability of contrast pattern-based classification for measuring empathy levels on textual conversations.
- Hybrid Recommender System for a Context Aware Recommendation in the Film Domain(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2020-06) Hernández López, Nora Patricia; RAMIREZ URESTI, JORGE ADOLFO; 21998; OLIART ROS, ALBERTO; 204266; GONZALEZ MENDOZA, MIGUEL; 123361; Ramírez Uresti, Jorge Adolfo; RR; González, Juan Gabriel; Oliart Ros, Alberto; González Mendoza, Miguel; Escuela de Ingeniería y Ciencias; Campus Estado de MéxicoRecommendation systems aim to offer personalized help in discovering relevant content. Several approaches have been designed for providing better recommendations that satisfy users’ needs. Based on ratings, on content, or on knowledge, isolated recommendation techniques often lack some good properties of other methods. Hence, hybrid combinations are able to compensate for those differences. Furthermore, the information to include in the recommendation is most of the time limited to the set of ratings users assigned to the items. By including additional information on where and when the recommendation is taking place, can improve the overall performance. Nevertheless, combining all these features into one single model is rather a daunting task due to its complexity, and often is disregarded as it might require some degree of domain knowledge. We propose a recommender system based on a model that captures the human understanding of how to produce a personalized recommendation. Moreover, by including context information, we try to enhance the overall user’s experience. This system is able to produce recommendations even under uncertainty. Hence, we used an explicit model which is in fact a Bayesian network, that directly encodes the relationships between users’ preferences, item attributes, and context information. The final recommendation is obtained by a two stage process, a combination of two recommendation strategies that complement each other. Such model is the Contextual Hybrid Bayesian Model.
- Definición de una arquitectura para la generación y selección de estrategias en equipos de fútbol robótico(Tecnologico de Monterrey) Arias Ruiz, Myriam Zoraya; Myriam Zoraya Arias Ruiz; Ramírez Uresti, Jorge Adolfo; Jorge Adolfo Ramírez Uresti