A Learning Classifier Systems Bibliography

While John Holland has always envisioned learning classifier systems (LCSs) as cognitive systems, most work on LCSs has focused on classification, datamining, and function approximation. In this paper, we show that the XCSF classifier system can be very suitably modified to control a robot system with redundant degrees of freedom, such as a robot arm. Inspired by recent research insights that suggest that sensorimotor codes are nearly ubiquitous in the brain and an essential ingredient for cognition in general, the XCSF system is modified to learn classifiers that encode piecewise linear sensorimotor structures, which are conditioned on prediction-relevant contextual input. In the investigated robot arm problem, we show that XCSF partitions the (contextual) posture space of the arm in such a way that accurate hand movements can be predicted given particular motor commands. Furthermore, we show that the inversion of the sensorimotor predictive structures enables accurate goal-directed closed-loop control of arm reaching movements. Besides the robot arm application, we also investigate performance of the modified XCSF system on a set of artificial functions. All results point out that XCSF is a useful tool to evolve problem space partitions that are maximally effective for the encoding of sensorimotor dependencies. A final discussion elaborates on the relation of the taken approach to actual brain structures and cognitive psychology theories of learning and behavior.

Within identity control theory (ICT), identities control meaning and resources by bringing perceptions of these in the situation into alignment with references levels given in the identity standard. This article seeks to resolve three issues in ICT having to do with the source of the identity standard, the correspondence between identity standards and the identity relevant meanings perceived in the situation or environment, and the activation of identities. Classifier systems, as developed by John Holland, are inductive, flexible, rule-based, message-passing, adaptive systems that are able to learn, to fit in, and to adapt to various and changing environments. Classifier systems are introduced and are extended to incorporate the central components of the model of identity as held in ICT. In this manner, a new identity model is proposed that has inductive and adaptive capacities able to resolve the three issues identified. Unexpectedly, the new model also lays the groundwork for the possible resolution of a long-standing issue within the symbolic interaction framework concerning the origins of shared meanings.

Data mining, and specifically supervised data classification, is a key application area for Learning Classifier Systems (LCS). Scaling to larger classification problems, especially to higher dimensional problems, is a key challenge. Ensemble based approaches can be applied to LCS to address scalability issues. To this end a rough set based ensemble of LCS is proposed, which relies on a pre-processed feature partitioning step to train multiple LCS on feature subspaces. Each base classifier in the ensemble is a Michigan style supervised LCS. The traditional genetic algorithm based rule evolution is replaced by a differential evolution based rule discovery, to improve generalisation capabilities of LCS. A voting mechanism is then used to generate output for test instances. This paper describes the proposed ensemble algorithm in detail, and compares its performance with different versions of base LCS on a number of benchmark classification tasks. Analysis of computational time and model accuracy show the relative merits of the ensemble algorithm and base classifiers on the tested data sets. The rough set based ensemble learning approach and differential evolution based rule searching out-perform the base LCS on classification accuracy over the data sets considered. Results also show that small ensemble size is sufficient to obtain good performance.

A learning classifier system is one of the methods for applying a genetic-based approach to machine learning applications. An enhanced version of the system that employs the Bucket-brigade algorithm to reward individuals in a chain of co-operating rules is implemented and assigned the task of learning rules for control robotics behaviors. Illustrates the approach with the example of kicking a moving Ball into a goal (KMB). The KMB (kick a Moving Ball) System built of two-classifier subsystems work together, each classifier system learns a simple behavior, first classifier system, learn simulated robot chase behavior i.e. learn robot to move single step toward moving ball, second classifier system, learn the simulated robot approach behavior i.e. learn robot to kick the ball toward fixed goal, the system as a whole has as its learning goal the coordinate of behaviors. This work examine the performance of the simple classifier system (SCS) on the (KMB) problem, perform two SCS simulations one without the genetic algorithm enabled (GA) and one with the genetic algorithm enabled (GA) results using a classifier system with genetic algorithm show improvement over one without, and furthermore the level of performance has been high enough to rival human accuracy. Also the results without the genetic algorithm show that the apportionment of credit algorithm adjust the strength values of the rules.

We propose that the behavior of nonlinear media can be controlled automatically through evolutionary learning. By extension, forms of unconventional computing (viz., massively parallel nonlinear computers) can be realized by such an approach. In this initial study a light-sensitive subexcitable Belousov-Zhabotinsky reaction in which a checkerboard image, composed of cells of varying light intensity projected onto the surface of a thin silica gel impregnated with a catalyst and indicator, is controlled using a learning classifier system. Pulses of wave fragments are injected into the checkerboard grid, resulting in rich spatiotemporal behavior, and a learning classifier system is shown to be able to direct the fragments to an arbitrary position through dynamic control of the light intensity within each cell in both simulated and real chemical systems. Similarly, a learning classifier system is shown to be able to control the electrical stimulation of cultured neuronal networks so that they display elementary learning. Results indicate that the learned stimulation protocols identify seemingly fundamental properties of in vitro neuronal networks. Use of another learning scheme presented in the literature confirms that such fundamental behavioral characteristics of a given network must be considered in training experiments.

This issue is dedicated to Stewart Wilson both in recognition of his achievements and in appreciation for his guidance of the field. Not only has his research transformed the field, but his support, encouragement, and advice have been invaluable to many of us individually and to the community as a whole. Learning Classifier Systems (LCS) are rule-based machine learning algorithms introduced by Holland (see [2, 4–6]) that learn a population of IF-THEN rules that specify ‘‘IF x happens THEN do (or predict) y’’. In 1995 Stewart Wilson, already a leading LCS researcher, published a paper entitled ‘‘Classifier Fitness based on Accuracy’’ [16] that introduced the XCS algorithm. This paper proved a turning point for the field as XCS and its derivatives rapidly became the main focus of LCS research, and continue to be so today. We organised this special issue of Evolutionary Intelligence to mark the 20th anniversary of this landmark paper, and to serve as post-proceedings for IWLCS 2014: the Seventeenth International Workshop on Learning Classifier Systems. The annual IWLCS meetings are the yearly highlight of the LCS calendar and have no doubt contributed to the strong sense of community in the LCS field. For 2015 IWLCS has been rebranded as the more descriptive IWERML: the International Workshop on Evolutionary Rule-based Machine Learning. In 1994, the year before introducing XCS, Wilson introduced ZCS, the ‘‘Zeroth-order Classifier System’’ [15]. Wilson felt the growing complexity of the LCS concept was hindering progress and ZCS was an attempt to strip the LCS down to its essentials while retaining a working system. XCS built on the minimalist ZCS with two radical changes: a switch to accuracy-based fitness and, based on work by Booker [1], the addition of a niche Genetic Algorithm (GA) that strongly favours general rules. This combination was a hit: the niche GA favours general rules but accuracy-based fitness insists strictly that they make good predictions. This combination drives XCS to learn rules that are as general as possible while remaining accurate. Bull’s article in this issue provides much more on the history of LCS prior to and following XCS [2]. The famous ‘‘accuracy-based fitness’’ of XCS needs some explanation. The earlier ZCS featured ‘‘strengthbased fitness’’, in which the fitness of a rule in the GA was derived from its strength, a measure of the amount of reward the rule received. Consequently, fit rules in ZCS are those that receive a lot of reward. In contrast, XCS rules are fit if they make consistently accurate predictions about the reward they receive. This has the counterintuitive consequence that XCS rules that consistently take a bad action can be fit, since they are consistent. However, this poses no problem when it comes time for XCS to choose an action, since that can be done using the magnitude of the reward predicted by each rule. Why does XCS retain rules whose action it does not use? This ‘‘complete map’’ of the state/ & Muhammad Iqbal muhammad.iqbal@ecs.vuw.ac.nz

Rough set theory has proved efficient for many applications, including finding hidden patterns in data, data reduction, evaluating significance of data, and generating sets of decision rules from data. Recently, it has shown to be effective approach for constructing ensemble learning systems as well. Learning classifier systems are genetics-based machine learning techniques that have recently shown a high degree of competence on a variety of data mining problems. Attempts to improve its generalization capabilities in the literature using ensemble learning lack a systematic and robust techniques for partitioning the problem at hand. It is well known that ensemble performance depends on the problem decomposition technique being used. Rough set based learning classifier system ensemble is proposed in this paper. In this approach, rough set attribute reduction is used to generate a set of reducts, and then a diverse subset of these reducts is selected to train an ensemble of base classifiers. The experiments show that classification accuracy of reduct-based ensemble systems outperforms a single learning classifier system model. It has also shown better performance than either an ensemble of classifiers with all attributes being used or a single classifier trained by a single reduct. It has also shown competitive performance to the random subspace ensemble strategy on the set of real data sets used in the experiments.

Learning classifier systems (LCSs) have existed for nearly twenty years (Holland & Reitman, 1978). Research efforts in reinforcement learning (RL), evolutionary computation (EC), and neural networks have enhanced the original LCS paradigm. New thoughts from these areas have created a renaissance period for the LCS. This paper highlights some key LCS advancements and the fields that inspired them. One inspiration, from neural networks, is examined for a novel LCS approach to autonomous mobile robots. A simple, LCS-controlled robot simulation is presented. This simulation shows the potential benefits of combined biological paradigms and the hybridization of ideas in the LCS. Future directions for LCS research are discussed.

A byproduct benefit of using probabilistic model-building genetic algorithms is the creation of cheap and accurate surrogate models. Learning classifier systems -- and genetics-based machine learning in general -- can greatly benefit from such surrogates which may replace the costly matching procedure of a rule against large data sets. In this paper we investigate the accuracy of such surrogate fitness functions when coupled with the probabilistic models evolved by the $\chi$-ary extended compact classifier system ($\chi$eCCS). To achieve such a goal, we show the need that the probabilistic models should be able to represent all the accurate basis functions required for creating an accurate surrogate. We also introduce a procedure to transform populations of rules based into dependency structure matrices (DSMs) which allows building accurate models of overlapping building blocks -- a necessary condition to accurately estimate the fitness of the evolved rules.

Learning classifier systems: New models, successful applications

Aim of the paper is the application of a Learning Classifier System (LCS) to learn the inference rules in a Fuzzy Decision Support System (FDSS). The FDSS is used for the Environmental Risk Assessment (ERA) of the deliberate release of genetically modified plants. The evaluation process permits identifying potential impacts that can achieve one or more receptors through a set of migration paths. The risk assessment in the FDSS is obtained by using a Fuzzy Inference System performed using jFuzzyLogic library. For the human experts might be hard developing complex FISs. We propose to use a LCS for automatically learning the appropriate fuzzy rules from the questionnaires produced by notifiers, named Fuzzy Rule Learning System (FRLS). FRLS is based on a special kind of LCS, namely the eXtended Classifier System (XCS). The derived rules have been validated on real world cases by the human experts that are in charge of ERA.

ENKOS – A Smart Home control system basing on learning classifier systems

Learning classifier systems (LCSs) have been successfully adapted to real-world domains with the claim of human-readable rule populations. However, due to the inherent rich characteristic of the employed representation, it is possible to represent the underlying patterns in multiple (polymorphic) ways, which obscures the most informative patterns. A novel rule reduction algorithm is proposed based on ensembles of multiple trained LCSs populations in a hierarchical learning architecture to reduce the local diversity and global polymorphism. The primary aim of this project is to interrogate the hidden patterns in LCSs’ trained population rather than improve the predictive power on test sets. This enables successful visualization of the importance of features in data groups (niches) that can contain heterogeneous patterns, i.e. even if different patterns result in the same class the importance of features can be found.

In dynamic environment, Learning Classifier System (LCS) evolves classifiers to fit the current situation, but may forget classifiers which were useful for previous situations. Our main idea is that, we store the forgotten classifiers as archives and generate new classifiers by recombining them to fit the current situation. Specifically, we propose an archive-based LCS called Arc-XCS, which detects environmental changes and generates classifiers based on the archive. The experimental results on the benchmark problem show that, Arc-XCS successfully stored good classifiers when each environmental changes occurs; compared to the conventional LCS (XCS), Arc-XCS reaches better performances with fewer trainings.

Learning classifier system (LCSs) have the ability to solve many difficult benchmark problems, but they have to be applied individually to each separate problem. Moreover, the solutions produced, although accurate, are not compact such that important knowledge is obscured. Recently a multi-agent system has been introduced that enables multiple, different LCSs to address multiple different problems simultaneously, which reduces the need for human system set-up, recognises existing solutions and assigns a suitable LCS to a new problem. However, the LCSs do not collaborate to solve a problem in a compact or human observable manner. Hence the aim is to extract knowledge from problems by combining solutions from multiple LCSs in a compact manner that enables patterns in the data to be visualised. Results show the successful compaction of multiple solutions to a single, optimum solution, which shows important feature knowledge that would otherwise have been hidden.