A survey of formal theoretical advances regarding XCS

Pätzel, David; Stein, Anthony; Hähner, Jörg

doi:10.1145/3319619.3326848

Cited by 20 publications

(5 citation statements)

References 46 publications

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“…Testing environments were created in full compliance with the OpenAI Gym [12], [13] 2 interface. All conducted experiments are reproducible by using tools such as Python scripts and Jupyter Notebooks, which are included in the repository 3 and in Zenodo 4 .…”

Section: Methodsmentioning

confidence: 99%

“…While there is a myriad of various LCS subvariants, one of the most well-known and investigated is the Accuracybased Classifier System (XCS) [3], [4], which was recently extended by Stein et al with a replay buffer called Experience Replay (ER) [5]. ER is an integral part of the Deep Q-Network (DQN) family and is mainly used to stabilize the neural network's training process and increase learning efficiency.…”

Section: Introductionmentioning

confidence: 99%

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Anticipatory Classifier System With Episode-Based Experience Replay

2023

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Deep reinforcement learning with Experience Replay (ER), including Deep Q-Network (DQN), has been used to solve many multi-step learning problems. However, in practice, DQN algorithms need better explainability, which limits their applicability in many scenarios. While we can consider DQN as a black-box model, the Learning Classifier Systems (LCSs), including anticipatory versions, also solve multi-step problems, but their operation is subject to interpretation. It seems promising to combine the properties of these two learning approaches. The paper describes an attempt to design and evaluate modification to the Experience Replay extension of the anticipatory classifier system ACS2. The modification is named Episode-based Experience Replay (EER), and its main premise is to replay entire episodes instead of single experience samples. Promising results affirmed by Bayes estimations are obtained on multi-step problems, albeit limited to deterministic and discrete tasks. The experimental results show that the EER extension significantly improves the ACS2's learning capabilities.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Anticipatory Classifier System With Episode-Based Experience Replay

2023

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“…These rules allow a simple mapping between states and the system's decisions and improve its interpretability. The most widely studied LCS in terms of formal theoretical analysis [22] and empirical evaluation is currently the XCS classifier system (XCS) [39]. It represents an evolutionary rule-based online machine learning technique characterized by an inherent generalization pressure, which is hypothesized to result in accurate and maximally-general rules.…”

Section: Introductionmentioning

confidence: 99%

Mechanisms to Alleviate Over-Generalization in XCS for Continuous-Valued Input Spaces

Wagner

Stein

2022

SN COMPUT. SCI.

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In the field of rule-based approaches to Machine Learning, the XCS classifier system (XCS) is a well-known representative of the learning classifier systems family. By using a genetic algorithm (GA), the XCS aims at forming rules or so-called classifiers which are as general as possible to achieve an optimal performance level. A too high generalization pressure may lead to over-general classifiers degrading the performance of XCS. To date, no method exists for XCS for real-valued input spaces (XCSR) and XCS for function approximation (XCSF) to handle over-general classifiers ensuring an accurate population. The Absumption mechanism and the Specify operator, both developed for XCS with binary inputs, provide a promising basis for over-generality handling in XCSR and XCSF. This paper introduces adapted versions of Absumption and Specify by proposing different identification and specialization strategies for the application in XCSR and XCSF. To determine their potential, the adapted techniques are evaluated in different classification problems, i.e., common benchmarks and real-world data from the agricultural domain, in a multi-step problem as well as different regression tasks. Our experimental results show that the application of these techniques leads to significant improvements of the accuracy of the generated classifier population in the applied benchmarks, data sets, multi-step problems and regression tasks, especially when they tend to form over-general classifiers. Furthermore, considering the working principle of the proposed techniques, the intended decrease in overall classifier generality can be confirmed.

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“…LCSs are a framework of evolutionary rule-based machine learning methods (Urbanowicz and Browne, 2017). Research for LCS goes in multiple directions: mathematical examination as documented by Pätzel et al (2019), reduction of runtime (Lanzi and Loiacono, 2010) or how the structure of LCSs can be adapted to improve learning performance (Stein et al, 2020). One such adaptation of XCS is called XCSF.…”

Section: Introductionmentioning

confidence: 99%

XCSF for Automatic Test Case Prioritization

Rosenbauer

Stein

Pätzel

et al. 2020

Proceedings of the 12th International Joint Conference on Computational Intelligence

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Testing is a crucial part in the development of a new product. Due to the change from manual testing to automated testing, companies can rely on a higher number of tests. There are certain cases such as smoke tests where the execution of all tests is not feasible and a smaller test suite of critical test cases is necessary. This prioritization problem has just gotten into the focus of reinforcement learning. A neural network and an XCS classifier system have been applied to this task. Another evolutionary machine learning approach is the XCSF which produces, unlike XCS, continuous outputs. In this work we show that XCSF is superior to both the neural network and XCS for this problem.

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A survey of formal theoretical advances regarding XCS

Cited by 20 publications

References 46 publications

Anticipatory Classifier System With Episode-Based Experience Replay

Anticipatory Classifier System With Episode-Based Experience Replay

Mechanisms to Alleviate Over-Generalization in XCS for Continuous-Valued Input Spaces

XCSF for Automatic Test Case Prioritization

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