scholarly journals Learning Classifier Systems: A Complete Introduction, Review, and Roadmap

2009 ◽  
Vol 2009 ◽  
pp. 1-25 ◽  
Author(s):  
Ryan J. Urbanowicz ◽  
Jason H. Moore
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Knowledge Discovery ◽  
Evolutionary Biology ◽  
Historical Review ◽  
Machine Learning Algorithms ◽  
Learning Classifier Systems ◽  
Classifier Systems ◽  
Rule Based ◽  
Learning Classifier

If complexity is your problem, learning classifier systems (LCSs) may offer a solution. These rule-based, multifaceted, machine learning algorithms originated and have evolved in the cradle of evolutionary biology and artificial intelligence. The LCS concept has inspired a multitude of implementations adapted to manage the different problem domains to which it has been applied (e.g., autonomous robotics, classification, knowledge discovery, and modeling). One field that is taking increasing notice of LCS is epidemiology, where there is a growing demand for powerful tools to facilitate etiological discovery. Unfortunately, implementation optimization is nontrivial, and a cohesive encapsulation of implementation alternatives seems to be lacking. This paper aims to provide an accessible foundation for researchers of different backgrounds interested in selecting or developing their own LCS. Included is a simple yet thorough introduction, a historical review, and a roadmap of algorithmic components, emphasizing differences in alternative LCS implementations.

USING ACCURACY-BASED LEARNING CLASSIFIER SYSTEMS FOR ADAPTABLE STRATEGY GENERATION IN GAMES AND INTERACTIVE VIRTUAL SIMULATIONS

2009 ◽  
Vol 10 (04) ◽  
pp. 365-390
Author(s):  
ALEJANDRO JUAN ◽  
RICHARD W. PAZZI ◽  
AZZEDINE BOUKERCHE
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Training Session ◽  
Learning Classifier Systems ◽  
Machine Learning Techniques ◽  
Knowledge Map ◽  
Classifier Systems ◽  
Learning Classifier System ◽  
Learning Classifier ◽  
Virtual Simulations

Historically, the artificial intelligence (AI) of interactive virtual simulations or games is usually driven by pre-defined static scripts. One of the disadvantages of such scripted opponents is that they can be deciphered and countered by an intelligent user. Thus, the user has the opportunity to find weaknesses and an easy solution against the virtual simulation, which diminishes the efficiency aspect of a training session or entertaining value drastically. While randomization can be used to mask the static behaviour of a scripted AI, it is possible to develop much richer solutions by applying Learning Classifier System (LCS) techniques to create agents with intelligent-like behaviors. Learning Classifier Systems are rule-based machine learning techniques that rely on a Genetic Algorithm to discover a knowledge map used to classify an input space into a set of actions. In this paper, we propose the use of an unsupervised machine learning technique called Accuracy-based Learning Classifier Systems (XCS) for adaptable strategy generation that can be used in virtual simulations or games. XCS use a Genetic Algorithm to evolve a knowledge base in the form of rules. The performance and adaptability of the strategies and tactics developed with the XCS is analyzed by facing these against scripted opponents on a real time strategy game. According to our experiments, the rulesets are able to adapt to a wide array of behaviors from its opponents, as opposed to a linear game script, which is limited in its ability to adapt to its environment.

A Comparison of Learning Classifier Systems’ Rule Compaction Algorithms for Knowledge Visualization

2021 ◽  
Vol 1 (3) ◽  
pp. 1-38
Author(s):  
Yi Liu ◽  
Will N. Browne ◽  
Bing Xue
Keyword(s):  
Large Scale ◽  
Optimal Solution ◽  
Population Level ◽  
Learning Classifier Systems ◽  
Classifier Systems ◽  
Rule Based ◽  
Stochastic Nature ◽  
Learning Classifier ◽  
Large Scale Problems ◽  
First Time

Learning Classifier Systems (LCSs) are a paradigm of rule-based evolutionary computation (EC). LCSs excel in data-mining tasks regarding helping humans to understand the explored problem, often through visualizing the discovered patterns linking features to classes. Due to the stochastic nature of EC, LCSs unavoidably produce and keep redundant rules, which obscure the patterns. Thus, rule compaction methods are invoked to produce a better population by removing problematic rules. Previously, compaction methods have neither been tested on large-scale problems nor been assessed on the performance of capturing patterns. We review and test the most popular compaction algorithms, finding that across multiple LCSs’ populations for the same task, although the redundant rules can be different, the accurate rules are common. Furthermore, the patterns contained consistently refer to the nature of the explored domain, e.g., the data distribution or the importance of features for determining actions. This extends the [ O ] set hypothesis proposed by Butz et al. [1], in which an LCS is expected to evolve a minimal number of non-overlapped rules to represent an addressed domain. Two new compaction algorithms are introduced to search at the rule level and the population level by compacting multiple LCSs’ populations. Two visualization methods are employed for verifying the interpretability of these populations. Successful compaction is demonstrated on complex and real problems with clean datasets, e.g., the 11-bits Majority-On problem that requires 924 different interacting rules in the optimal solution to be uniquely identified to enable correct visualization. For the first time, the patterns contained in learned models for the large-scale 70-bits Multiplexer problem are visualized successfully.

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