Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1177/105971230200900102 | Adaptive Behaviour |
Keywords | Field | DocType |
recurrent artificial neural network,different role,adaptive value,emotion model,different issue,artificial creature,conventional architecture,artificial emotion,reinforcement-learning architecture,emotion-dependent architecture,reinforcement learning,autonomous agent,emotions,robotics,robot learning | Robot learning,Social robot,Autonomous agent,Computer science,Artificial intelligence,Artificial neural network,Situated learning,Robot,Perception,Machine learning,Reinforcement learning | Journal |
Volume | Issue | ISSN |
9 | 1 | 1059-7123 |
Citations | PageRank | References |
34 | 2.70 | 9 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| sandra clara gadanho | 1 | 103 | 8.59 |
| John Hallam | 2 | 839 | 94.72 |