1 / 14

Learning Classifier Systems

Learning Classifier Systems. Learning Classifier Systems (LCS). The system has three layers: A performance system that interacts with environment, An apportionment of credit algorithm that rates rules as to usefulness,

amity-salinas
Download Presentation

Learning Classifier Systems

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Learning Classifier Systems

  2. Learning Classifier Systems (LCS) • The system has three layers: • A performance system that interacts with environment, • An apportionment of credit algorithm that rates rules as to usefulness, • A rule discovery algorithm that generates plausible new rules to replace less useful rules.

  3. Performance System Cycles • Message is posted in the message list from the input interface. • Each rule is matched against the message list • All matching rules compete to post in the next message list via bidding process; winning rule posts in the new message list • The output interface checks the new message and produces an effector action. • The new message list replaces the previous one. • Repeat.

  4. Overview of LCS

  5. Rule format • Rule • Condition = {0,1,#}k • Action = message to be posted in the message list • Strength = rule’s usefulness to the system

  6. kind ears num. of legs smart scream runaway kiss Example (Wolf or Grandmother?) Encoding teeth Wolf 1 0 1 1 1 # 1 1 0 GrandMa 0 1 0 0 1 # 0 0 1

  7. Matching [N] [M]

  8. Bidding Process • Bid(R,t) = β × specificity(R) × Strength(R,t) • Specificity(R)= number of non # / k [M] β = 0.2 Bid(r1) = 0.2 × ¼ × 100 = 5 Bid(r3) = 0.2 × ½ × 100 = 10 r3 posts its message in the new message list.

  9. Credit assignment: Bucket Brigade Bucket 10 r3 coupled Bucket r5 150 executed Reward Environment 200

  10. Credit assignment: Bucket Brigade Bucket 10 r3 Bucket r5 150 Reward Environment 200

  11. Genetic Algorithms • Fitness = rule strength • Parents: Strong classifiers (best, roulette wheel, etc.) • Mutation: alter parts of parent’s string • Crossover: exchange parts of parents’ strings • Offspring replaces a weak rule.

  12. Genetic Algorithms (cont.) Crossover Crossover point Parent 1 Parent 2 Mutation Parent 1 Parent 2

  13. Maze Environment (Signal smell-ahead bump heading score location) Environment

  14. References • A Mathematical framework for Studying Learning in Classifier Systems, John H. Holland, Phsyca D, Vol 2, No 1-3, 1986, pp. 307-317 • A First Order Logic Classifier System, Drew Mellor Gecco ’05

More Related