@INPROCEEDINGS { Rsds2331,
author = {Ramanna, Sheela and Henry, Christopher J. and Peters, James F.}, 
title = {Reinforcement learning with pattern-based rewards}, 
booktitle = {Proc. Fourth Int. IASTED Conf. Computational Intelligence}, 
conference = {IASTED International Conference - Artificial Intelligence, Expert Systems and Neural Networks}, 
pages = {267-272}, 
publisher = {International Association of Science and Technology for Development}, 
month = {July}, 
year = 2005, 
url = {http://www.iasted.org}, 
abstract = {This paper introduces an approach to deriving pattern-based rewards during reinforcement learning by cooperating agents.  Rough set theory introduced by Zdzisl{}aw Pawlak in 1982 provides a ground for deriving pattern-based rewards in the context of approximation spaces.   The framework provided by an approximation space makes it possible to derive pattern-based reference rewards used to compute action rewards as well as action preferences.   Approximation spaces are used to derive action-based reference rewards at the swarm intelligence level.  Two different forms of reinforcement comparison are considered as a part of a study of learning in real-time by a swarm. In addition, this article introduces an artificial ecosystem testbed that makes it possible to study learning by collections of biologically-inspired bots. The contribution of this article is the introduction of an approach to rewarding swarm behavior in the context of approximation spaces.}, 
keywords = {approximation space, ecosystem, intelligent systems, reinforcement learning, rough sets, swarm, }, 
}