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LEARNING CLASSIFIER SYSTEM 楊皓鈞 陳思誠
OUTLINE ,[object Object],[object Object],[object Object],[object Object],[object Object]
INTRODUCTION ,[object Object],[object Object],[object Object]
PREREQUISITES ,[object Object],[object Object],[object Object]
PROBLEM TYPES ,[object Object],[object Object]
CLASSIFICATION PROBLEM ,[object Object]
REINFORCEMENT LEARNING PROBLEMS ,[object Object]
MARKOV DECISION PROCESSES ,[object Object],[object Object],[object Object],[object Object],[object Object]
PARTIALLY OBSERVABLE MARKOV DECISION PROCESSES ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
REINFORCEMENT LEARNING ,[object Object],[object Object],[object Object]
MODEL-FREE REINFORCEMENT LEARNING ,[object Object],[object Object]
GENETIC ALGORITHM ,[object Object],[object Object],[object Object]
ARCHITECTURE OF LCS [N]: Rule-base, Population of GA {0,1,#} (IF [condition] THEN [action]) [M]: Match List
EXECUTION CYCLE OF LCS ,[object Object]
EXECUTION CYCLE OF LCS ,[object Object]
EXECUTION CYCLE OF LCS ,[object Object]
BUCKET BRIGADE ALGORITHM ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
EXECUTION CYCLE OF LCS ,[object Object]
EXECUTION CYCLE OF LCS ,[object Object]
ARCHITECTURE OF XCS Reward not based on payoff received by rules, but on the accuracy of predictions of payoff.

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ppt.gif

  • 1. LEARNING CLASSIFIER SYSTEM 楊皓鈞 陳思誠
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9.
  • 10.
  • 11.
  • 12.
  • 13. ARCHITECTURE OF LCS [N]: Rule-base, Population of GA {0,1,#} (IF [condition] THEN [action]) [M]: Match List
  • 14.
  • 15.
  • 16.
  • 17.
  • 18.
  • 19.
  • 20. ARCHITECTURE OF XCS Reward not based on payoff received by rules, but on the accuracy of predictions of payoff.

Editor's Notes

  1. ZCS- keeps much of Holland ’ s original framework but simplifies it to increase understandability and performance. XCS-altered the way in which rule fitness is calcuated
  2. LCSs are designed to solve classification as well as more general reinforcement learning (RL) problems.