scholarly journals A Coordinated Air Defense Learning System Based on Immunized Classifier Systems

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 271
Author(s):  
Sulemana Nantogma ◽  
Yang Xu ◽  
Weizhi Ran
Keyword(s):  
Intelligent Systems ◽  
Intelligent System ◽  
Hybrid Approach ◽  
Learning System ◽  
Artificial Immune ◽  
Learning Classifier System ◽  
Air Defense ◽  
Classifier System ◽  
Defense Systems ◽  
Learning Classifier

Autonomous (unmanned) combat systems will become an integral part of modern defense systems. However, limited operational capabilities, the need for coordination, and dynamic battlefield environments with the requirement of timeless in decision-making are peculiar difficulties to be solved in order to realize intelligent systems control. In this paper, we explore the application of Learning Classifier System and Artificial Immune models for coordinated self-learning air defense systems. In particular, this paper presents a scheme that implements an autonomous cooperative threat evaluation and weapon assignment learning approach. Taking into account uncertainties in a successful interception, target characteristics, weapon type and characteristics, closed-loop coordinated behaviors, we adopt a hierarchical multi-agent approach to coordinate multiple combat platforms to achieve optimal performance. Based on the combined strengths of learning classifier system and artificial immune-based algorithms, the proposed scheme consists of two categories of agents; a strategy generation agent inspired by learning classifier system, and strategy coordination inspired by Artificial Immune System mechanisms. An experiment in a realistic environment shows that the adopted hybrid approach can be used to learn weapon-target assignment for multiple unmanned combat systems to successfully defend against coordinated attacks. The presented results show the potential for hybrid approaches for an intelligent system enabling adaptable and collaborative systems.

Semi-Supervised Learning Classifier System Based on Bayes

2011 ◽  
Vol 48-49 ◽  
pp. 1032-1037
Author(s):  
Guo Qiang Li ◽  
Hua Zou ◽  
Fang Chun Yang
Keyword(s):  
Supervised Learning ◽  
Intelligent System ◽  
Optimal Choice ◽  
Unlabeled Data ◽  
Learning Classifier System ◽  
Classifier System ◽  
Bayes Inference ◽  
Learning Classifier ◽  
Learning Tasks ◽  
The Em Algorithm

The high interpretability and the extraordinary evolvability of learning classifier system make it the optimal choice to build an adaptive intelligent system, and UCS is one of its branches, which is especially designed for the supervised learning tasks. However usually there is a huge amount of unlabeled data that are helpful for the increasing of its accuracy. Hence we use the EM algorithm in Semi-supervised learning as a reference, and proposed a Semi-Supervised Classifier system (SUCS) based on Bayes inference. The experiments we did using the UCI dataset showed that SUCS performed a much better accuracy than UCS by use of only a small number of labeled data and a large number of unlabeled data.

scholarly journals Customer satisfaction prediction with Michigan-style learning classifier system

2019 ◽  
Vol 1 (11) ◽  
Author(s):  
Keivan Borna ◽  
Shokoofeh Hoseini ◽  
Mohammad Ali Mehdi Aghaei
Keyword(s):  
Machine Learning ◽  
Customer Satisfaction ◽  
Adaptive System ◽  
Learning System ◽  
Classification Algorithms ◽  
Learning Classifier System ◽  
Classifier System ◽  
Learning Classifier ◽  
Genetic Base ◽  
Base Machine

Abstract Many different classification algorithms can be use in order to analyze, classify and predict data. Learning classifier system (LCS) which is known as a genetic base machine learning system, combines the machine learning with evolutionary computing and other heuristics to produce an adaptive system that learns to solve a particular problem. This paper uses the Michigan style LCS, in the context of bank customer satisfaction to classify customers into two different groups: unsatisfied/satisfied customers. Three different Rule Compaction strategies are used to compare the rule population’s accuracy and micro/macro population size. The result specifies features that mostly influence prediction.

Multi-Robot Path Planning Based on Learning Classifier System with Policy Gradient Reinforcement Learning and Support Vector Machine (PGRL-SVM)

2013 ◽  
Vol 347-350 ◽  
pp. 3208-3211
Author(s):  
Qiu Li Song ◽  
Jian Bao Zhao
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Search Space ◽  
Learning System ◽  
Learning Ability ◽  
Support Vector ◽  
Learning Classifier System ◽  
Learning Space ◽  
Classifier System ◽  
Learning Classifier

This paper presented a novel approach to solving the problem of robot avoidance collision planning. A Learning Classifier System is a accuracy-based machine learning system using gradient descent that combines covering operator and genetic algorithm. The covering operator is responsible for adjusting precision and large search space according to some reward obtained from the environment. The genetic algorithm acts as an innovation discovery component which is responsible for discovering new better path planning rules. The advantages of this approach are its accuracy-based representation, that can be easily reduce learning space,online learning ability,robustness due to the use of genetic algorithm.

Robot Reinforcement Learning Based on LCS-GDM

2013 ◽  
Vol 347-350 ◽  
pp. 416-420
Author(s):  
Yong Bin Ma
Keyword(s):  
Reinforcement Learning ◽  
Learning System ◽  
Learning Ability ◽  
Learning Classifier System ◽  
Learning Space ◽  
Rule Based ◽  
Classifier System ◽  
System A ◽  
Learning Classifier ◽  
Learning Rules

This paper proposed a robot reinforcement learning method based on learning classifier system. A learning Classifier System is a rule-based machine learning system that combines reinforcement learning and genetic algorithms. The reinforcement learning component is responsible for adjusting the strength of rules in the system according to some reward obtained from the environment. The genetic algorithm acts as an innovation discovery component which is responsible for discovering new better learning rules. The advantages of this approach are its rule-based representation, which can be easily reduce learning space, online learning ability, robustness .

SELF-ADAPTIVE LEARNING CLASSIFIER SYSTEM

2010 ◽  
Vol 19 (01) ◽  
pp. 275-296 ◽  
Author(s):  
OLGIERD UNOLD
Keyword(s):  
Mutation Rate ◽  
Adaptive Learning ◽  
Adaptive Mutation ◽  
Learning Classifier System ◽  
Classifier System ◽  
Learning Classifier ◽  
Adaptive Parameters ◽  
Proposed Model ◽  
Fixed Parameter ◽  
Self Adaptive

This article introduces a new kind of self-adaptation in discovery mechanism of learning classifier system XCS. Unlike the previous approaches, which incorporate self-adaptive parameters in the representation of an individual, proposed model evolves competitive population of the reduced XCSs, which are able to adapt both classifiers and genetic parameters. The experimental comparisons of self-adaptive mutation rate XCS and standard XCS interacting with 11-bit, 20-bit, and 37-bit multiplexer environment were provided. It has been shown that adapting the mutation rate can give an equivalent or better performance to known good fixed parameter settings, especially for computationally complex tasks. Moreover, the self-adaptive XCS is able to solve the problem of inappropriate for a standard XCS parameters.

Sign in / Sign up

Export Citation Format

Share Document