Motivated learning for the development of autonomous systems
A new machine learning approach known as motivated learning (ML) is presented in this work. Motivated learning drives a machine to develop abstract motivations and choose its own goals. ML also provides a self-organizing system that controls a machine’s behavior based on competition between dynami...
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| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/96713 http://hdl.handle.net/10220/13056 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | A new machine learning approach known as motivated learning (ML) is presented in this work. Motivated learning drives a machine
to develop abstract motivations and choose its own goals. ML also provides a self-organizing system that controls a machine’s behavior
based on competition between dynamically-changing pain signals. This provides an interplay of externally driven and internally
generated control signals. It is demonstrated that ML not only yields a more sophisticated learning mechanism and system of values
than reinforcement learning (RL), but is also more efficient in learning complex relations and delivers better performance than RL in
dynamically-changing environments. In addition, this paper shows the basic neural network structures used to create abstract motivations,
higher level goals, and subgoals. Finally, simulation results show comparisons between ML and RL in environments of gradually
increasing sophistication and levels of difficulty. |
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