3 research outputs found
Aprendizado Autônomo Para Robôs Móveis Baseado em Emoções Artificiais
Especialistas da área de neurofisiologia têm proposto a consideração dos sentimentos como parte dos processos cognitivos, e não de uma alma imaterial: tem sido defendido que as emoções não devem mais ser entendidas como opostas às decisões inteligentes, mas sim como parte e elemento decisivo para estas. Consequentemente se tornaram defensáveis a introdução de emoções artificiais no aprendizado de agentes artificiais, bem como a construção de modelos homeostáticos computacionais para estes. Nesta dissertação são relatados experimentos sobre uma arquitetura de controle baseado em comportamento e fundamentada sobre a simulação de processos hormonais e emocionais. São apresentadas e discutidas a arquitetura e modificações sobre esta, ou seja, a separação, da estrutura de aprendizado baseado em emoções, em diferentes redes neurais artificiais, uma rede para cada emoção. Os resultados mostraram que é razoável considerar modelos computacionais para processos emocionais que possam sustentar seleção de comportamento autônomo inteligente
Oceanographic surveys with autonomous underwater vehicles : performance metrics and survey design
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Ocean Engineering, 1998.Includes bibliographical references (p. 127-134).by Jeffrey Scott Willcox.M.S
Reinforcement Learning in Autonomous Robots: An Empirical Investigation of the Role of Emotions
Institute of Perception, Action and BehaviourThis thesis presents a study of the provision of emotions for artificial agents with the
ultimate aim of enhancing their autonomy, i.e. making them more
exible, robust
and self-sufficient. In recent years, the importance of emotions and their assistance
to cognition has been increasingly acknowledged. Emotions are no longer considered
undesirable or simply useless. Their role in various aspects of human and animal cog-
nition like perception, attention, memory, decision-making and social interaction has
been recognised as essential. The importance of emotions is much more evident in social interaction and therefore much of the emotions research done in artificial systems
focuses on the expression and recognition of emotions. However, recent neurophysiological research suggests that emotions also play a crucial part in cognition itself.
This thesis investigates ways in which artificial emotions can improve autonomous
behaviour in the domain of a simple, but complete, solitary learning agent. For this
purpose, a non-symbolic emotion model was designed and implemented. It takes the
form of a recurrent artificial neural network where emotions influence the perception
of the state of the world, on which they ultimately depend. This is done through
a hormone system that acts as a persistence mechanism. This model is somewhat
more sophisticated than those usually found in equivalent non-symbolic systems, yet
the emotions themselves were restricted to a few simplified emotions that do not try
to mimic the complexity of the human counterparts, but are afforded by the agent's
interaction with the environment.
Several hypotheses were investigated of how the emotion model above could be integrated in a reinforcement learning framework which, by itself, provides the base for the
adaptiveness necessary for autonomy. Experiments were carried out in a realistic robot
simulator that compared the performance of emotional with non-emotional agents in
a survival task that consists of maintaining adequate energy levels in an environment
with obstacles and energy sources. One of the most common roles attributed to emotions is as source of reinforcement and was therefore examined first. In experiments
with a controller that selects between primitive actions, the reinforcement provided by
emotions was found inappropriate because of the time scale discrepancies introduced
by the emotion model. The reinforcement provided by emotions proved to be much
more successful when used by a controller that selects between behaviours rather than
actions, achieving equivalent performance to that of a standard reinforcement function.
One of the crucial issues for efficient and productive learning, highlighted by the latter
experiments, is to determine exactly when the controller should re-evaluate its decision concerning which behaviour to activate. The emotions proved to be particularly
helpful in this role, enabling better performance with substantially less computational
effort than the best suited interruption mechanism using regular time intervals. The
modulation of learning parameters such as learning rate and the exploration vs. exploitation ratio was also explored. Experiments suggested that emotions might also be
useful for this purpose.
This research led to the conclusion that artificial emotions are a useful construct to have
in the domain of behaviour-based autonomous agents, because they provide a unifying
way to tackle different issues of control, analogous to natural systems' emotions