Access the full text.
Sign up today, get DeepDyve free for 14 days.
B. Knudsen
CC5X C Compiler
A. Barto, R. Sutton, C. Anderson (1983)
Neuronlike adaptive elements that can solve difficult learning control problemsIEEE Transactions on Systems, Man, and Cybernetics, SMC-13
L. Tan, D. Taniar, K. Smith‐Miles (2006)
Maximum-entropy estimated distribution model for classification problemsInt. J. Hybrid Intell. Syst., 3
Z. Pawlak (1981)
Classification of objects by means of attributes
R.S. Sutton, A.G. Barto, C.W. Anderson
Neuronlike adaptive elements that can solve difficult learning problems
L. Kaelbling, M. Littman, A. Moore (1996)
Reinforcement Learning: A SurveyJ. Artif. Intell. Res., 4
(2006)
Line-crawling bots that inspect electric power transmission line equipment
J. Peters (2005)
Rough Ethology: Towards a Biologically-Inspired Study of Collective Behavior in Intelligent Systems with Approximation SpacesTrans. Rough Sets, 3
C. Watkins, P. Dayan (2004)
Technical Note: Q-LearningMachine Learning, 8
N. Tinbergen (2010)
On aims and methods of EthologyEthology, 20
Gavin Rummery, M. Niranjan (1994)
On-line Q-learning using connectionist systems
(2003)
Linux RedHat 9.0 Manuals, available at: www.redhat.com/docs/manuals/linux/ RHL-9-Manual
Microchip Technology Inc.
MPLAB Integrated Development Environment
E. Orlowska
Semantics of Vague Concepts. Applications of Rough Sets
J. Peters, C. Henry, S. Ramanna (2005)
Rough Ethograms: Study of Intelligent System Behavior
Satinder Singh, T. Jaakkola, M. Littman, Csaba Szepesvari (2000)
Convergence Results for Single-Step On-Policy Reinforcement-Learning AlgorithmsMachine Learning, 38
(2007)
Learning with ALiCE II
R.S. Sutton, A.G. Barto
Reinforcement Learning: An Introduction
C. Watkins (1989)
Learning from delayed rewards
Satinder Singh, R. Sutton (2005)
Reinforcement Learning with Replacing Eligibility TracesMachine Learning, 22
O. Selfridge, E. Rissland, M. Arbib (1984)
Adaptive Control of Ill-Defined Systems
M. Seiler, F. Seiler (1989)
Numerical Recipes in C: The Art of Scientific ComputingRisk Analysis, 9
T. Jaakkola, Michael Jordan, Satinder Singh (1993)
On the Convergence of Stochastic Iterative Dynamic Programming AlgorithmsNeural Computation, 6
(2006)
Biologically-inspired approximate adaptive learning control strategies: a rough set approach
Linux
Linux RedHat 9.0 Manuals
O. Selfridge (1984)
Some Themes and Primitives in Ill-Defined Systems
(2010)
Advances in Neural Information Processing Systems pp MIT Press Generalization in Reinforcement Learning Successful Examples Using Sparse Coarse Coding
(1982)
Chandler, AZ, available at: www
R.S. Sutton
Generalization in reinforcement learning: successful examples using sparse coarse coding
E. Orlowska (1985)
Semantics of Vague Concepts
Purpose – The purpose of this paper is to report upon research into developing a biologically inspired target‐tracking system (TTS) capable of acquiring quality images of a known target type for a robotic inspection application. Design/methodology/approach – The approach used in the design of the TTS hearkens back to the work on adaptive learning by Oliver Selfridge and Chris J.C.H. Watkins and the work on the classification of objects by Zdzislaw Pawlak during the 1980s in an approximation space‐based form of feedback during learning. Also, during the 1980s, it was Ewa Orlowska who called attention to the importance of approximation spaces as a formal counterpart of perception. This insight by Orlowska has been important in working toward a new form of adaptive learning useful in controlling the behaviour of machines to accomplish system goals. The adaptive learning algorithms presented in this paper are strictly temporal difference methods, including Q‐learning, sarsa, and the actor‐critic method. Learning itself is considered episodic. During each episode, the equivalent of a Tinbergen‐like ethogram is constructed. Such an ethogram provides a basis for the construction of an approximation space at the end of each episode. The combination of episodic ethograms and approximation spaces provides an extremely effective means of feedback useful in guiding learning during the lifetime of a robotic system such as the TTS reported in this paper. Findings – It was discovered that even though the adaptive learning methods were computationally more expensive than the classical algorithm implementations, they proved to be more effective in a number of cases, especially in noisy environments. Originality/value – The novelty associated with this work is the introduction of an approach to adaptive adaptive learning carried out within the framework of ethology‐based approximation spaces to provide performance feedback during the learning process.
International Journal of Intelligent Computing and Cybernetics – Emerald Publishing
Published: Mar 28, 2008
Keywords: Behaviour; Biology; Robots; Tracking; Intelligent agents
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.