In this paper, we present S-ELF, an evolutionary algorithm that we have developed to learn the context of activation of fuzzy logic controllers implementing fuzzy behaviors for autonomous agent. S-ELF learns context metarules that are used to coordinate basic behaviors in order to perform complex tasks in a partially and imprecisely known environment. Context metarules are expressed in terms of positive and negated fuzzy predicates. We also show how S-ELF can learn robust and portable behaviors, thus reducing the time and e ort to design behavior-based agents

Learning to compose fuzzy behaviors for autonomous agents.

BONARINI, ANDREA;
1997

Abstract

In this paper, we present S-ELF, an evolutionary algorithm that we have developed to learn the context of activation of fuzzy logic controllers implementing fuzzy behaviors for autonomous agent. S-ELF learns context metarules that are used to coordinate basic behaviors in order to perform complex tasks in a partially and imprecisely known environment. Context metarules are expressed in terms of positive and negated fuzzy predicates. We also show how S-ELF can learn robust and portable behaviors, thus reducing the time and e ort to design behavior-based agents
Robotics
Fuzzy behaviors
Reinforcement learning
Fuzzy systems
machine learning
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/518619
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