Strength or Accuracy: Credit Assignment in Learning Classifier Systems - Tapa blanda

Sinopsis

1 Introduction.- 1.1 Two Example Machine Learning Tasks.- 1.2 Types of Task.- 1.2.1 Supervised and Reinforcement Learning.- 1.2.2 Sequential and Non-sequential Decision Tasks.- 1.3 Two Challenges for Classifier Systems.- 1.3.1 Problem 1: Learning a Policy from Reinforcement.- 1.3.2 Problem 2: Generalisation.- 1.4 Solution Methods.- 1.4.1 Method 1: Reinforcement Learning Algorithms.- 1.4.2 Method 2: Evolutionary Algorithms.- 1.5 Learning Classifier Systems.- 1.5.1 The Tripartite LCS Structure.- 1.5.2 LCS = Policy Learning + Generalisation.- 1.5.3 Credit Assignment in Classifier Systems.- 1.5.4 Strength and Accuracy-based Classifier Systems.- 1.6 About the Book.- 1.6.1 Why Compare Strength and Accuracy.- 1.6.2 Are LCS EC- or RL-based.- 1.6.3 Moving in Design Space.- 1.7 Structure of the Book.- 2 Learning Classifier Systems.- 2.1 Types of Classifier Systems.- 2.1.1 Michigan and Pittsburgh LCS.- 2.1.2 XCS and Traditional LCS?.- 2.2 Representing Rules.- 2.2.1 The Standard Ternary Language.- 2.2.2 Other Representations.- 2.2.3 Summary of Rule Representation.- 2.2.4 Notation for Rules.- 2.3 XCS.- 2.3.1 Wilson's Motivation for XCS.- 2.3.2 Overview of XCS.- 2.3.3 Wilson's Explore/Exploit Framework.- 2.3.4 The Performance System.- 2.3.4.1 The XCS Performance System Algorithm.- 2.3.4.2 The Match Set and Prediction Array.- 2.3.4.3 Action Selection.- 2.3.4.4 Experience-weighting of System Prediction.- 2.3.5 The Credit Assignment System.- 2.3.5.1 The MAM Technique.- 2.3.5.2 The Credit Assignment Algorithm.- 2.3.5.3 Sequential and Non-sequential Updates.- 2.3.5.4 Parameter Update Order.- 2.3.5.5 XCS Parameter Updates.- 2.3.6 The Rule Discovery System.- 2.3.6.1 Random Initial Populations.- 2.3.6.2 Covering.- 2.3.6.3 The Niche Genetic Algorithm.- 2.3.6.4 Alternative Mutation Schemes.- 2.3.6.5 Triggering the Niche GA.- 2.3.6.6 Deletion of Rules.- 2.3.6.7 Classifier Parameter Initialisation.- 2.3.6.8 Subsumption Deletion.- 2.4 SB-XCS.- 2.4.1 Specification of SB-XCS.- 2.4.2 Comparison of SB-XCS and Other Strength LCS.- 2.5 Initial Tests of XCS and SB-XCS.- 2.5.1 The 6 Multiplexer.- 2.5.2 Woods2.- 2.6 Summary.- 3 How Strength and Accuracy Differ.- 3.1 Thinking about Complex Systems.- 3.2 Holland's Rationale for CS-1 and his Later LCS.- 3.2.1 Schema Theory.- 3.2.2 The Bucket Brigade.- 3.2.3 Schema Theory + Bucket Brigade = Adaptation.- 3.3 Wilson's Rationale for XCS.- 3.3.1 A Bias towards Accurate Rules.- 3.3.2 A Bias towards General Rules.- 3.3.3 Complete Maps.- 3.3.4 Summary.- 3.4 A Rationale for SB-XCS.- 3.5 Analysis of Populations Evolved by XCS and SB-XCS.- 3.5.1 SB-XCS.- 3.5.2 XCS.- 3.5.3 Learning Rate.- 3.6 Different Goals, Different Representations.- 3.6.1 Default Hierarchies.- 3.6.2 Partial and Best Action Maps.- 3.6.3 Complete Maps.- 3.6.4 What do XCS and SB-XCS Really Learn?.- 3.7 Complete and Partial Maps Compared.- 3.7.1 Advantages of Partial Maps.- 3.7.2 Disadvantages of Partial Maps.- 3.7.3 Complete Maps and Strength.- 3.7.4 Contrasting Complete and Partial Maps in RL Terminology.- 3.7.5 Summary of Comparison.- 3.8 Ability to Express Generalisations.- 3.8.1 Mapping Policies and Mapping Value Functions.- 3.8.2 Adapting the Accuracy Criterion.- 3.8.3 XCS-hard and SB-XCS-easy Functions.- 3.8.4 Summary of Generalisation and Efficiency.- 3.9 Summary.- 4 What Should a Classifier System Learn?.- 4.1 Representing Boolean Functions.- 4.1.1 Truth Tables.- 4.1.2 On-sets and Off-sets.- 4.1.3 Sigma Notation.- 4.1.4 Disjunctive Normal Form.- 4.1.5 Representing Functions with Sets of Rules.- 4.1.6 How Should a Classifier System Represent a Solution?.- 4.1.7 The Value of a Single Rule.- 4.1.1 The Value of a Set of Rules.- 4.1.1 Complete and Correct Representations.- 4.1.1 Minimal Representations.- 4.1.1 Non-overlapping Representations.- 4.1.1 Why XCS Prefers Non-overlapping Populations.- 4.1.1 Should we Prefer Non-overlapping Populations?.- 4.1.1 Optimal Rule Sets: [O]s.- 4.1.1 Conflicting Rules.- 4.1.1 Representation in XCS.- 4.3 How Should We

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