An Algorithmic Description of XCS

Butz, Martin V.; Stolzmann, Wolfgang

doi:10.1007/3-540-44640-0_15

Cited by 191 publications

(127 citation statements)

References 5 publications

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“…There were also other parameters that either we did not take into account or we did not change the values from what is stated in [6].…”

Section: Methodsmentioning

confidence: 99%

“…XCS, developed by Wilson [12], is a learning classifier system [6]. It uses reinforcement learning along with genetic algorithms in order to search for the best rule-based.…”

Section: An Overview Of Xcsmentioning

confidence: 99%

“…XCS is designed for both single-step and multiple-step tasks, but the discussion here is restricted to only the version that works for single-step tasks in which the system, by receiving an input data, makes a decision and selects an action. System does not receive any input before applying the selected action to the environment and, later, receiving the reward of the action back from the environment [6].…”

Section: An Overview Of Xcsmentioning

confidence: 99%

“…XCS is a collection of classifiers [6]. Each classifier has a pair of <CONDITION, ACTION> and a set of parameters.…”

Section: An Overview Of Xcsmentioning

confidence: 99%

“…This feature makes XCS a very effective method of making prediction engines as rule-based systems are one of the most widely used frameworks to develop predicting systems [16], [17]. Furthermore, XCS' population based nature provides a competitive environment which, by itself, results in generating sets of properly pruned classification rules [6]. Further, unlike other population based algorithms, which need an explicit fitness function to operate, XCS learns using rewards.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Using XCS as a Prediction Engine in Data Compression

Sharifi¹,

Aavani²,

Tasharrofi³

2007

Proceedings on Intelligent Systems and Knowledge Engineering (ISKE2007)

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XCS has been used in several fields as a prediction engine before; its population based nature enables XCS to generate sets of properly pruned classification rules. Further, unlike other population based algorithms, it learns using rewards. These properties encouraged us to use it as a predictor engine for lossless data compression. In the compression context, XCS can be used to find the hidden relations in files of the same type. So, we used it as a preprocessor before an entropy encoder, to remove the existing correlations between file's bits or symbols. Removing correlations causes entropy encoders to achieve higher rates of compression. The results support this conclusion.

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“…There were also other parameters that either we did not take into account or we did not change the values from what is stated in [6].…”

Section: Methodsmentioning

confidence: 99%

“…XCS, developed by Wilson [12], is a learning classifier system [6]. It uses reinforcement learning along with genetic algorithms in order to search for the best rule-based.…”

Section: An Overview Of Xcsmentioning

confidence: 99%

Section: An Overview Of Xcsmentioning

confidence: 99%

“…XCS is a collection of classifiers [6]. Each classifier has a pair of <CONDITION, ACTION> and a set of parameters.…”

Section: An Overview Of Xcsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Using XCS as a Prediction Engine in Data Compression

Sharifi¹,

Aavani²,

Tasharrofi³

2007

Proceedings on Intelligent Systems and Knowledge Engineering (ISKE2007)

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Continuous‐Action XCSR with Dynamic Reward Assignment Dedicated to Control of Black‐Box Mechanical Systems

Hashemnia

Panahi

Mahjoob

2017

Asian Journal of Control

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A real-value classifier system (CSR) is improved by the introduction of a continuous domain of actions to be employed for control of mechanical systems where there is no information concerning the system's mathematical model. To enable the classifier system to handle real-world control problems where continuous (non-discrete) actions are required, the exploitation of fuzzy membership functions is proposed. To cope with the dynamic system's delayed response due to its mass inertia, a dynamic reward assignment mechanism is incorporated into the proposed CSR. This allows the rapid calculation of the reward and hence enables the controller to be used in such real time applications. To demonstrate the efficiency of the developed enhanced CSR, it is employed as the controller to balance an unmanned bicycle, without using bicycle properties for the design process of the enhanced CSR. Simulation results show that in terms of overshoot and settling time, the proposed classifier system outperforms traditional XCSR as well as some of the more common balance-control strategies reported in the literature, as verified using ADAMS software.

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Development of a classifier system for a continuous environment

Hayashida

Nishizaki

Sekizaki

et al. 2019

Elect Comm in Japan

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A learning classifier system is an adaptive system that obtains a set of appropriate action rules that adapts to multistep problems by training action rules defined in if‐then form by trial and error process, in a similar framework as reinforcement learning. Because of that the input signals of the classifier system are encoded into binary values, bit strings are often lengthened when dealing with such a problem that the state of the environment continuously changes. A neural network can treat with real values as input signal; however, it cannot be applied to multistep problems. This paper proposes a system that responds to problems such that the state of the environment continuously changes by combining a neural network and a classifier system, and actions are selected from multiple options, so that output can be defined as discrete values. In order to verify the effectiveness of the proposed system, this paper conducts several numerical experiments using benchmarks corresponding to multistep problems defined by continuous values.

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An Algorithmic Description of XCS

Cited by 191 publications

References 5 publications

Using XCS as a Prediction Engine in Data Compression

Using XCS as a Prediction Engine in Data Compression

Continuous‐Action XCSR with Dynamic Reward Assignment Dedicated to Control of Black‐Box Mechanical Systems

Development of a classifier system for a continuous environment

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