Strength or Accuracy: Credit Assignment in Learning Classifier Systems

Name: Strength or Accuracy: Credit Assignment in Learning Classifier Systems
Brand: Springer London Ltd
SKU: 9781852337704
Price: 636.01 PLN
Availability: InStock

Credit Assignment in Learning Classifier Systems. Diss.

Autor: Tim Kovacs

Język:

Oprawa: Twarda

Wydawca: Springer London Ltd

Dostępność: Dostępna u dostawcy

Wysyłamy za 10-13 dni

636.01 zł

The Distinguished Dissertations series is published on behalf of the Conference of Professors and He...

Informacje o książce

Autor

Tim Kovacs

Język

Angielski

Oprawa

Książka - Twarda

Data wydania

2003

strony

307

EAN

9781852337704

ISBN

1852337702

Enbook ID

01435339

Wydawca

Springer London Ltd

Waga

1400

Wymiary

155 x 235 x 22

Pełny opis

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 incomputer 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 theory relating 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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Strength or Accuracy: Credit Assignment in Learning Classifier Systems

Credit Assignment in Learning Classifier Systems. Diss.

Informacje o książce

Pełny opis

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