The paper explores the issue of learning multiple competing tasks in the domain of artificial evolution. In particular, a robot is trained so as to be able to perform two different tasks at the same time, namely a gradient following and rough terrain avoidance behaviours. It is shown that, if the controller is trained to learn two tasks of different difficulty, then the robot performance is higher if the most difficult task is learnt first, before the combined learning of both tasks. An explanation to this superiority is also discussed, in comparison with previous results.

Nicolay, D., Roli, A., Carletti, T. (2014). Learning Multiple Conflicting Tasks with Artificial Evolution. Cham (ZG) : Springer Verlag [10.1007/978-3-319-12745-3_11].

Learning Multiple Conflicting Tasks with Artificial Evolution

ROLI, ANDREA;
2014

Abstract

The paper explores the issue of learning multiple competing tasks in the domain of artificial evolution. In particular, a robot is trained so as to be able to perform two different tasks at the same time, namely a gradient following and rough terrain avoidance behaviours. It is shown that, if the controller is trained to learn two tasks of different difficulty, then the robot performance is higher if the most difficult task is learnt first, before the combined learning of both tasks. An explanation to this superiority is also discussed, in comparison with previous results.
2014
Communications in Computer and Information Science
127
139
Nicolay, D., Roli, A., Carletti, T. (2014). Learning Multiple Conflicting Tasks with Artificial Evolution. Cham (ZG) : Springer Verlag [10.1007/978-3-319-12745-3_11].
Nicolay, Delphine; Roli, Andrea; Carletti, Timoteo
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/520900
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