Publication Type

Journal Article

Version

publishedVersion

Publication Date

4-2012

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.

Keywords

autonomous systems, intelligent agents, motivated learning, neural networks, reinforcement learning

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems | OS and Networks

Research Areas

Data Science and Engineering

Publication

Cognitive Systems Research

Volume

14

Issue

1

First Page

10

Last Page

25

ISSN

2214-4366

Identifier

10.1016/j.cogsys.2010.12.009

Publisher

Elsevier

Additional URL

https://doi.org/10.1016/j.cogsys.2010.12.009

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