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http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/139| Automatic discovery of concepts for unknown environments | |
| ANA CECILIA TENORIO GONZALEZ | |
| EDUARDO FRANCISCO MORALES MANZANARES | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| Concept learning Reinforcement learning Predicate invention Inductive logic programming | |
| This thesis explores how an agent can autonomously learn about its environment just by interacting with it. This is not an easy task, since traditional machine learning algorithms strongly depend on the user's intervention to define the data to use and the experimental conditions under which the learning process takes place. Designing an agent that autonomously drives its own learning process poses several interesting challenges. How to explore the environment, how to gather and represent the information obtained from the environment (what to learn, when to learn, and how to organize the new knowledge) and how to evaluate the knowledge acquired. In this thesis, an algorithm called ADC which combines different machine learning techniques in novel ways, is proposed to answer these questions. In particular, a novel exploration strategy is proposed based on an asymmetric Wundt's curve and biased actions to guide an agent through the environment and the learning process. ADC incrementally builds, during exploration, a graph-based representation of the environment using some initial background knowledge. Frequent sub-graphs are automatically identified as instances of potentially useful concepts from which relational concepts are induced. These concepts are organized in a lattice and incorporated into its background knowledge so that they can be used for learning new concepts. ADC also learns how to perform new tasks by reinforcement learning with intrinsic motivation, relational concepts are used to define states where actions are learned. The learned behavior policies are stored for solving future tasks. ADC was tested on simulated environments (floors, polygons, furniture, mobility and stability of objects) and the concepts learned by the system were validated by independent users (different to the author of this thesis) with encouraging results. Among the learned concepts are basic structures (e.g., room), polygons (e.g., pentagon, triangle), furniture (e.g., table, chair), movable objects, and examples of simple stable structures. | |
| Instituto, Nacional de Astrofísica, Óptica y Electrónica | |
| 2016-06 | |
| Tesis de doctorado | |
| Inglés | |
| Público en general | |
| Tenorio-Gonzalez A.C. | |
| CIENCIA DE LOS ORDENADORES | |
| Aparece en las colecciones: | Doctorado en Ciencias Computacionales |
Cargar archivos:
| Fichero | Descripción | Tamaño | Formato | |
|---|---|---|---|---|
| TenorioGoAC.pdf | 11.4 MB | Adobe PDF | Visualizar/Abrir |