SupRB: A Supervised Rule-based Learning System for Continuous Problems

We propose the SupRB learning system, a new Pittsburgh-style learning classifier system (LCS) for supervised learning on multi-dimensional continuous decision problems. SupRB learns an approximation of a quality function from examples (consisting of situations, choices and associated qualities) and is then able to make an optimal choice as well as predict the quality of a choice in a given situation. One area of application for SupRB is parametrization of industrial machinery. In this field, acceptance of the recommendations of machine learning systems is highly reliant on operators' trust. While an essential and much-researched ingredient for that trust is prediction quality, it seems that this alone is not enough. At least as important is a human-understandable explanation of the reasoning behind a recommendation. While many state-of-the-art methods such as artificial neural networks fall short of this, LCSs such as SupRB provide human-readable rules that can be understood very easily. The prevalent LCSs are not directly applicable to this problem as they lack support for continuous choices. This paper lays the foundations for SupRB and shows its general applicability on a simplified model of an additive manufacturing problem.Comment: Submitted to the Genetic and Evolutionary Computation Conference 2020 (GECCO 2020

Approaches for rule discovery in a learning classifier system

To fill the increasing demand for explanations of decisions made by automated prediction systems, machine learning (ML) techniques that produce inherently transparent models are directly suited. Learning Classifier Systems (LCSs), a family of rule-based learners, produce transparent models by design. However, the usefulness of such models, both for predictions and analyses, heavily depends on the placement and selection of rules (combined constituting the ML task of model selection). In this paper, we investigate a variety of techniques to efficiently place good rules within the search space based on their local prediction errors as well as their generality. This investigation is done within a specific LCS, named SupRB, where the placement of rules and the selection of good subsets of rules are strictly separated in contrast to other LCSs where these tasks sometimes blend. We compare a Random Search, (1,λ)-ES and three Novelty Search variants. We find that there is a definitive need to guide the search based on some sensible criteria, i.e. error and generality, rather than just placing rules randomly and selecting better performing ones but also find that Novelty Search variants do not beat the easier to understand (1,λ)-ES

Discovering rules for rule-based machine learning with the help of novelty search

Automated prediction systems based on machine learning (ML) are employed in practical applications with increasing frequency and stakeholders demand explanations of their decisions. ML algorithms that learn accurate sets of rules, such as learning classifier systems (LCSs), produce transparent and human-readable models by design. However, whether such models can be effectively used, both for predictions and analyses, strongly relies on the optimal placement and selection of rules (in ML this task is known as model selection). In this article, we broaden a previous analysis on a variety of techniques to efficiently place good rules within the search space based on their local prediction errors as well as their generality. This investigation is done within a specific pre-existing LCS, named SupRB, where the placement of rules and the selection of good subsets of rules are strictly separated—in contrast to other LCSs where these tasks sometimes blend. We compare two baselines, random search and -evolution strategy (ES), with six novelty search variants: three novelty-/fitness weighing variants and for each of those two differing approaches on the usage of the archiving mechanism. We find that random search is not sufficient and sensible criteria, i.e., error and generality, are indeed needed. However, we cannot confirm that the more complicated-to-explain novelty search variants would provide better results than -ES which allows a good balance between low error and low complexity in the resulting models

Simultaneous Effect of Ultraviolet Radiation and Surface Modification on the Work Function and Hole Injection Properties of ZnO Thin Films

The combined effect of ultraviolet (UV) light soaking and self-assembled monolayer deposition on the work function (WF) of thin ZnO layers and on the efficiency of hole injection into the prototypical conjugated polymer poly(3-hexylthiophen-2,5-diyl) (P3HT) is systematically investigated. It is shown that the WF and injection efficiency depend strongly on the history of UV light exposure. Proper treatment of the ZnO layer enables ohmic hole injection into P3HT, demonstrating ZnO as a potential anode material for organic optoelectronic devices. The results also suggest that valid conclusions on the energy-level alignment at the ZnO/organic interfaces may only be drawn if the illumination history is precisely known and controlled. This is inherently problematic when comparing electronic data from ultraviolet photoelectron spectroscopy (UPS) measurements carried out under different or ill-defined illumination conditions. © 2020 The Authors. Published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinhei

Simultaneous Effect of Ultraviolet Radiation and Surface Modification on the Work Function and Hole Injection Properties of ZnO Thin Films

The combined effect of ultraviolet (UV) light soaking and self-assembled monolayer deposition on the work function (WF) of thin ZnO layers and on the efficiency of hole injection into the prototypical conjugated polymer poly(3-hexylthiophen-2,5-diyl) (P3HT) is systematically investigated. It is shown that the WF and injection efficiency depend strongly on the history of UV light exposure. Proper treatment of the ZnO layer enables ohmic hole injection into P3HT, demonstrating ZnO as a potential anode material for organic optoelectronic devices. The results also suggest that valid conclusions on the energy-level alignment at the ZnO/organic interfaces may only be drawn if the illumination history is precisely known and controlled. This is inherently problematic when comparing electronic data from ultraviolet photoelectron spectroscopy (UPS) measurements carried out under different or ill-defined illumination conditions.Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659Peer Reviewe

Towards principled synthetic benchmarks for explainable rule set learning algorithms

A very common and powerful step in the design process of a new learning algorithm or extensions and improvements of existing algorithms is the benchmarking of models produced by said algorithm. We propose a paradigm shift in the benchmarking of explainable if-then-rule-based models like the ones generated by Learning Classifier Systems or Fuzzy Rule-based Systems. The principled method we suggest is based on synthetic data sets being sampled from randomly generated but known processes that have the same overall structure as the models that are being benchmarked (i. e. each process consists of a set of components each of which corresponds to one if-then rule) which is up-to-date not available among the many synthetic data generators. This approach has several benefits over other benchmarks and we expect that it will lead to fresh insights into how algorithms producing such explainable models work and can be improved. We demonstrate its usage by benchmarking the effects of different rule representations in the XCSF classifier system