On the Effects of Absumption for XCS with Continuous-Valued Inputs

Wagner, Alexander R. M.; Stein, Anthony

doi:10.1007/978-3-030-72699-7_44

Cited by 6 publications

(2 citation statements)

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“…This article is an extension of an original conference paper published at the Evostar 2021 conference [34]. It additionally includes: new experiments on the application of OGH to XCSF for regression tasks; a more thorough discussion of both the new results and the results of the original conference paper; more elaborate descriptions of the algorithms of the proposed techniques using pseudocode.…”

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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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 Chameleon, the focus lies on LCS because those and variants -such as the Extended Classifier System (XCS) [ST21] by Wilson [Wil95] or XCS for real-valued input spaces (XCSR) [WS21] -are well-suitable for real-time systems, have a low computational complexity and have been widely used for implementing adaptive behavior with runtime learning capabilities in various domains. For instance, they have been applied in typical CPS use cases such as self-adaptive traffic management [Ste+17a;STH16], autonomous parameter adjustment of data communication protocols [TH11], or Industry 4.0 [HPH20].…”

Section: Learning -Lcs and Adaptation Languagesmentioning

confidence: 99%

Comprehensive adaptive middleware for mixed-critical cyber-physical networks

Feist

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Cyber Physical Systems (CPS) are growing more and more complex due to the availability of cheap hardware, sensors, actuators and communication links. A network of cooperating CPSs (CPN) additionally increases the complexity. This poses challenges as well as it offers chances: the increasing complexity makes it harder to design, operate, optimize and maintain such CPNs. However, on the other side an appropriate use of the increasing resources in computational nodes, sensors, actuators can significantly improve the system performance, reliability and flexibility. Therefore, self-X features like self-organization, self-adaptation and self-healing are key principles for such systems. Additionally, CPNs are often deployed in dynamic, unpredictable environments and safety-critical domains, such as transportation, energy, and healthcare. In such domains, usually applications of different criticality level exist. In an automotive environment for example, the brake has a higher criticality level regarding safety as the infotainment. As a result of mixed-criticality, applications requiring hard real-time guarantees compete with those requiring soft real-time guarantees and best-effort application for the given resources within the overall system. This leads to the need to accommodate multiple levels of criticality while ensuring safety and reliability, which increases the already high complexity even more. This thesis deals with the question on how to conveniently, effectively and efficiently handle the management and complexity of mixed-critical CPNs (MC-CPNs). Since this cannot be done by the system developer without the assistance of the system itself any longer, it is essential to develop new approaches and techniques to ensure that such systems can operate under a range of conditions while meeting stringent requirements. Based on five research hypothesis, this thesis introduces a comprehensive adaptive mixed-criticality supporting middleware for Cyber-Physical Networks (Chameleon), which efficiently and autonomously takes care of the management and complexity of CPNs with regard to the mixed-criticality aspect. Chameleon contributes to the state-of-art by introducing and combining the following concepts: - A comprehensive self-adaption mechanism on all levels of the system model is provided. - This mechanism allows a flexible combination of parametric and structural adaptation actions (relocation, scheduling, tuning, ...) to modify the behavior of the system. - Real-time constraints of mixed-critical applications (hard real-time, soft real-time, best-effort) are considered in all possible adaptation conditions and actions by the use of the importance parameter. - CPNs are supported by the introduction of different scopes (local, system, global) for the adaptation conditions and actions. This also enables the combination of different scopes for conditions and actions. - The realization of the adaptation with a MAPE-K loop instantiated by a distributed LCS allows for real-time capable reasoning of adaptation actions which also works on resource-spare systems. - The developed rule language Rango offers an intuitive way to specify an initial rule set for LCS in the context of CPS/CPNs and supports the system administrators in the process of rule set generation.

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