Autonomous mental development in high dimensional context and action spaces

Authors:
Ameet Joshi;Juyang Weng
Affiliations:
Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI;Department of Computer Science and Engineering, Michigan State University, East Lansing, MI
Venue:
Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
Year:
2003

Citing 7
Cited 2

From text to speech: the MITalk system

From text to speech: the MITalk system
Hierarchical Discriminant Regression

IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Reinforcement Learning

Introduction to Reinforcement Learning
Learning to Predict by the Methods of Temporal Differences

Machine Learning
An Introduction to the Conjugate Gradient Method Without the Agonizing Pain

An Introduction to the Conjugate Gradient Method Without the Agonizing Pain
Action Chaining by a Developmental Robot with a Value System

ICDL '02 Proceedings of the 2nd International Conference on Development and Learning
Reinforcement learning: a survey

Journal of Artificial Intelligence Research

Language learning for the autonomous mental development of conversational agents

ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Landau theory of meta-learning

SIIS'11 Proceedings of the 2011 international conference on Security and Intelligent Information Systems

Quantified Score

Hi-index	0.02

Visualization

Abstract

Autonomous Mental Development (AMD) of robots opened a new paradigm for developing machine intelligence, using neural network type of techniques and it fundamentally changed the way an intelligent machine is developed from manual to autonomous. The work presented here is a part of SAIL (Self-Organizing Autonomous Incremental Learner) project which deals with autonomous development of humanoid robot with vision, audition, manipulation and locomotion. The major issue addressed here is the challenge of high dimensional action space (5-10) in addition to the high dimensional context space (hundreds to thousands and beyond), typically required by an AMD machine. This is the first work that studies a high dimensional (numeric) action space in conjunction with a high dimensional perception (context state) space, under the AMD mode. Two new learning algorithms, Direct Update on Direction Cosines (DUDC) and High-Dimensional Conjugate Gradient Search (HCGS), are developed, implemented and tested. The convergence properties of both the algorithms and their targeted applications are discussed. Autonomous learning of speech production under reinforcement learning is studied as an example.