Anticipatory Learning Classifier Systems is popular PDF and ePub book, written by Martin V. Butz in 2012-12-06, it is a fantastic choice for those who relish reading online the Computers genre. Let's immerse ourselves in this engaging Computers book by exploring the summary and details provided below. Remember, Anticipatory Learning Classifier Systems can be Read Online from any device for your convenience.

Anticipatory Learning Classifier Systems Book PDF Summary

Anticipatory Learning Classifier Systems describes the state of the art of anticipatory learning classifier systems-adaptive rule learning systems that autonomously build anticipatory environmental models. An anticipatory model specifies all possible action-effects in an environment with respect to given situations. It can be used to simulate anticipatory adaptive behavior. Anticipatory Learning Classifier Systems highlights how anticipations influence cognitive systems and illustrates the use of anticipations for (1) faster reactivity, (2) adaptive behavior beyond reinforcement learning, (3) attentional mechanisms, (4) simulation of other agents and (5) the implementation of a motivational module. The book focuses on a particular evolutionary model learning mechanism, a combination of a directed specializing mechanism and a genetic generalizing mechanism. Experiments show that anticipatory adaptive behavior can be simulated by exploiting the evolving anticipatory model for even faster model learning, planning applications, and adaptive behavior beyond reinforcement learning. Anticipatory Learning Classifier Systems gives a detailed algorithmic description as well as a program documentation of a C++ implementation of the system.

Detail Book of Anticipatory Learning Classifier Systems PDF

Anticipatory Learning Classifier Systems
  • Author : Martin V. Butz
  • Release : 06 December 2012
  • Publisher : Springer Science & Business Media
  • ISBN : 9781461508915
  • Genre : Computers
  • Total Page : 197 pages
  • Language : English
  • PDF File Size : 9,9 Mb

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