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Learning Classifier Systems (LCSs) are a group of rule-based evolutionary computation techniques, which have been frequently applied to data mining tasks. The LCSs' rules are designed to be human-readable to enable the underlying knowledge to be investigated. However, the models for the majority of domains with high feature interaction contain a large number of rules that cooperatively represent the knowledge. However, the interaction between many rules is too complex to be comprehended by humans. Thus, it is hypothesized that translating the models' underlying patterns into human-discernable visualizations will advance the understanding of the learned patterns and LCSs themselves. Interrogatable artificial Boolean domains with varying numbers of attributes are considered as benchmarks. Three new visualization techniques, termed as Feature Importance Map, Action-based Feature Importance Map, and Action-based Feature's Average value Map, successfully produce interpretable results for all the complex domains tested. This includes both tracing the training progress and analyzing the trained models from LCSs. The visualization techniques' ability to handle complex optimal solutions is observed for the 14-bits Majority-On problem, where the patterns from 6435 different cooperating rules were translated into human-discernable graphs. [ABSTRACT FROM AUTHOR]

A main research direction in the field of evolutionary machine learning is to develop a scalable classifier system to solve high-dimensional problems. Recently work has begun on autonomously reusing learned building blocks of knowledge to scale from low-dimensional problems to high-dimensional ones. An XCS-based classifier system, known as XCSCFC, has been shown to be scalable, through the addition of expression tree--like code fragments, to a limit beyond standard learning classifier systems. XCSCFC is especially beneficial if the target problem can be divided into a hierarchy of subproblems and each of them is solvable in a bottom-up fashion. However, if the hierarchy of subproblems is too deep, then XCSCFC becomes impractical because of the needed computational time and thus eventually hits a limit in problem size. A limitation in this technique is the lack of a cyclic representation, which is inherent in finite state machines (FSMs). However, the evolution of FSMs is a hard task owing to the combinatorially large number of possible states, connections, and interaction. Usually this requires supervised learning to minimize inappropriate FSMs, which for high-dimensional problems necessitates subsampling or incremental testing. To avoid these constraints, this work introduces a state-machine-based encoding scheme into XCS for the first time, termed XCSSMA. The proposed system has been tested on six complex Boolean problem domains: multiplexer, majority-on, carry, even-parity, count ones, and digital design verification problems. The proposed approach outperforms XCSCFA (an XCS that computes actions) and XCSF (an XCS that computes predictions) in three of the six problem domains, while the performance in others is similar. In addition, XCSSMA evolved, for the first time, compact and human readable general classifiers (i.e., solving any n-bit problems) for the even-parity and carry problem domains, demonstrating its ability to produce scalable solutions using a cyclic representation. [ABSTRACT FROM AUTHOR]

Currently when path planning is used in SLAM it is to benefit SLAM only, with no mutual benefit for path planning. Furthermore, SLAM algorithms are generally implemented and modified for individual heterogeneous robotic platforms without autonomous means of sharing navigation information. This limits the ability for robot platforms to share navigation information and can require heterogeneous robot platforms to generate individual maps within the same environment. This paper introduces Learned Action SLAM, which for the first time autonomously combines path-planning with SLAM such that heterogeneous robots can share learnt knowledge through Learning Classifier Systems (LCS). This is in contrast to Active SLAM, where path-planning is used to benefit SLAM only. Results from testing LA-SLAM on robots in the real world have shown; promise for use on teams of robots with various sensor morphologies, implications for scaling to associated domains, and ability to share maps taken from less capable to more advanced robots. [ABSTRACT FROM AUTHOR]

Salient object detection is the task of automatically localizing objects of interests in a scene by suppressing the background information, which facilitates various machine vision applications such as object segmentation, recognition and tracking. Combining features from different feature-modalities has been demonstrated to enhance the performance of saliency prediction algorithms and different feature combinations are often suited to different types of images. However, existing saliency learning techniques attempt to apply a single feature combination across all image types and thus lose generalization in the test phase when considering unseen images. Learning classifier systems (LCSs) are an evolutionary machine learning technique that evolve a set of rules, based on a niched genetic reproduction, which collectively solve the problem. It is hypothesized that the LCS technique has the ability to autonomously learn different feature combinations for different image types. Hence, this paper further investigates the application of LCS for learning image dependent feature fusion strategies for the task of salient object detection. The obtained results show that the proposed method outperforms, through evolving generalized rules to compute saliency maps, the individual feature based methods and seven combinatorial techniques in detecting salient objects from three well known benchmark datasets of various types and difficulty levels. [ABSTRACT FROM AUTHOR]

Image pattern classification is a challenging task due to the large search space of pixel data. Supervised and subsymbolic approaches have proven accurate in learning a problem's classes. However, in the complex image recognition domain, there is a need for investigation of learning techniques that allow humans to interpret the learned rules in order to gain an insight about the problem. Learning classifier systems (LCSs) are a machine learning technique that have been minimally explored for image classification. This work has developed the feature pattern classification system (FPCS) framework by adopting Haar-like features from the image recognition domain for feature extraction. The FPCS integrates Haar-like features with XCS, which is an accuracy-based LCS. A major contribution of this work is that the developed framework is capable of producing human-interpretable rules. The FPCS system achieved 91 ± 1% accuracy on the unseen test set of the MNIST dataset. In addition, the FPCS is capable of autonomously adjusting the rotation angle in unaligned images. This rotation adjustment raised the accuracy of FPCS to 95%. Although the performance is competitive with equivalent approaches, this was not as accurate as subsymbolic approaches on this dataset. However, the benefit of the interpretability of rules produced by FPCS enabled us to identify the distribution of the learned angles--a normal distribution around 0°--which would have been very difficult in subsymbolic approaches. The analyzable nature of FPCS is anticipated to be beneficial in domains such as speed sign recognition, where underlying reasoning and confidence of recognition needs to be human interpretable. [ABSTRACT FROM AUTHOR]