Aprendizado por reforço relacional para o controle de robôs sociáveis

Content Provider	Semantic Scholar
Author	Silva, Renato R. Da
Copyright Year	2009
Abstract	THe Artificial Intelligence search not only understand but to build intelligent entities. The intelligence can be divided into several factors and one of them is known as learning. The area of machine learning aimed at the development techniques for automatic learning of machinery, including computers, robots or any other device. Reinforcement Learning is one of those techniques, main focus of this work. Specifically, the relational reinforcement learning was investigated, which is use relational representation for learning obtained through direct interaction with the environment. The relational reinforcement learning is quite interesting in the field of robotics, because, in general, it does not have the model of environment and economy of resources used are required. The relational reinforcement learning technique was investigated within the context of learning a robotic head. A change in the relational reinforcement learning algorithm was proposed, called TGE, and incorporated into an architecture of control of a robotic head. The architecture was evaluated in the context of a real problem not trivial: the learning of shared attention. The results show that the architecture is capable of displaying appropriate behavior during a social interaction controlled through the use of TGE. A comparative analysis was performed with other methods show that the proposed algorithm has achieved a superior performance in most experiments.
File Format	PDF HTM / HTML
DOI	10.11606/D.55.2009.tde-28052009-100159
Alternate Webpage(s)	https://www.teses.usp.br/teses/disponiveis/55/55134/tde-28052009-100159/publico/Dissertacao_Renato_Ramos.pdf
Alternate Webpage(s)	https://doi.org/10.11606/D.55.2009.tde-28052009-100159
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article