Adaptatividade em aprendizagem de máquina: conceitos e estudo de caso [thesis]

Renata Luiza Stange
AGRADECIMENTOS Deus, pela força espiritual nos momentos de difíceis durante o desenvolvimento desta dissertação. Ao Prof. Dr. João José Neto, pela oportunidade e paciência durante a orientação, pelo constante incentivo durante todo o trabalho de pesquisa, pela compreensão e conselhos nos momentos difíceis, e principalmente pela sua verdadeira amizade. Aos professores Dr. Ricardo Luis De Azevedo Da Rocha e a Drª Angela Hum Tchemra pelas observações e sugestões apresentadas no exame de
more » ... o, que foram de grande valia para o enriquecimento deste trabalho. A Dr. Fabiana Soares Santana, por compartilhar seu conhecimento em pesquisa e sempre estar disposta a sugerir, revisar e ensinar. Também por sua amizade, hospedagens e cafés ao longo desses anos. Ao amigo Luciano Ogiboski pelo incentivo para iniciar a pós-graduação na Universidade de São Paulo. Enfim, a todos os colegas e professores, que influenciaram de alguma forma na realização deste trabalho, em especial aos do ABSTRACT Incremental learning requires a learning mechanism based on the information extracted from dynamically accumulated experiments. Adaptivity-oriented machine-learning combines adaptive techniques with symbolic ones for solving machine-learning problems. The term -adaptivity‖ means the ability of a learning process to change its own set of rules in response to events occurred during the learning process, or, equivalently, self-tuning the set of parameters. The adaptive devices with withhold information ability inside their rules, extracted from input from their own set of rules, can accumulate information to be used whenever they are necessary. The strategies of interest to adopt adaptivity include the use of machine learning techniques and methods, particularly the ones that implement supervised learning and decision-making. This work purposes to investigate the application of adaptive techniques in machine learning process, either exclusively and in cooperation with other techniques. In order to achieve this target, the use of adaptive de vices to represent the knowledge gathered through incremental learning is proposed. Additionally, a case study that combines both machine learning and adaptive techniques to implement a scheme of autonomous learning strategies is also performed with the goal of winning an instance of the simple game. Decision-making is required to learning how to play a game, which is a complex and dynamic process. So as to provide a general framework for the creation, manipulation and analysis of rules in decision-making problems using adaptive decision tables, the Adapt-DT tool was implemented. An illustrative example using adaptive decision tables as a means to represent knowledge is introduced to the tool evaluation. This supports the conclusion that adaptive devices can be used to adequately represent the knowledge, with advantages over other traditional methods.
doi:10.11606/d.3.2011.tde-02072012-175054 fatcat:szhvrokqivbs5geflhrhst5jsq