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Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
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Strength or Accuracy: Credit Assignment in Learning Classifier Systems (Hardcover, 2004 ed.)
Loot Price: R4,414
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Strength or Accuracy: Credit Assignment in Learning Classifier Systems (Hardcover, 2004 ed.)
Series: Distinguished Dissertations
Expected to ship within 10 - 15 working days
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Total price: R4,434
Discovery Miles: 44 340
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The Distinguished Dissertations series is published on behalf of
the Conference of Professors and Heads of Computing and the British
Computer Society, who annually select the best British PhD
dissertations in computer science for publication. The
dissertations are selected on behalf of the CPHC by a panel of
eight academics. Each dissertation chosen makes a noteworthy
contribution to the subject and reaches a high standard of
exposition, placing all results clearly in the context of computer
science as a whole. In this way computer scientists with
significantly different interests are able to grasp the essentials
- or even find a means of entry - to an unfamiliar research topic.
Machine learning promises both to create machine intelligence and
to shed light on natural intelligence. A fundamental issue for
either endevour is that of credit assignment, which we can pose as
follows: how can we credit individual components of a complex
adaptive system for their often subtle effects on the world? For
example, in a game of chess, how did each move (and the reasoning
behind it) contribute to the outcome? This text studies aspects of
credit assignment in learning classifier systems, which combine
evolutionary algorithms with reinforcement learning methods to
address a range of tasks from pattern classification to stochastic
control to simulation of learning in animals. Credit assignment in
classifier systems is complicated by two features: 1) their
components are frequently modified by evolutionary search, and 2)
components tend to interact. Classifier systems are re-examined
from first principles and the result is, primarily, a formalization
of learning in these systems, and a body of theoryrelating types of
classifier systems, learning tasks, and credit assignment
pathologies. Most significantly, it is shown that both of the main
approaches have difficulties with certain tasks, which the other
type does not.
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