Please use this identifier to cite or link to this item:
|Title:||Motivated learning for the development of autonomous systems||Authors:||Starzyk, Janusz A.
Graham, James T.
|Keywords:||DRNTU::Engineering::Computer science and engineering||Issue Date:||2011||Source:||Starzyk, J. A., Graham, J. T., Raif, P.,& Tan, A. H. (2012). Motivated learning for the development of autonomous systems. Cognitive Systems Research, 14(1), 10-25.||Series/Report no.:||Cognitive systems research||Abstract:||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.||URI:||https://hdl.handle.net/10356/96713
|ISSN:||1389-0417||DOI:||10.1016/j.cogsys.2010.12.009||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||SCSE Journal Articles|
Updated on Jul 16, 2020
Updated on Mar 6, 2021
Page view(s) 50451
Updated on Aug 9, 2022
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.