Robot Learning Driven by Emotions  

Oleh:

Gadanho, Sandra Clara ; Hallam, John

Jenis: Article from Journal - e-Journal
Dalam koleksi: Adaptive Behavior vol. 9 no. 1 (Mar. 2001), page 42–64.
Topik: robotics; emotions; reinforcement learning
Fulltext: 42.pdf (401.95KB)

Isi artikel

The adaptive value of emotions in nature indicates that they might also be useful in artificial creatures. Experiments were carried out to investigate this hypothesis in a simulated learning robot. For this purpose, a non-symbolic emotion model was developed that takes the form of a recurrent artificial neural network where emotions both depend on and influence the perception of the state of the world. This emotion model was integrated in a reinforcement-learning architecture with three different roles: influencing perception, providing reinforcement value, and determining when to reevaluate decisions. Experiments to test and compare this emotion-dependent architecture with a more conventional architecture were done in the context of a solitary learning robot performing a survival task. This research led to the conclusion that artificial emotions are a useful construct to have in the domain of behavior-based autonomous agents with multiple goals and faced with an unstructured environment, because they provide a unifying way to tackle different issues of control, analogous to natural systems’ emotions.

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