Automatic Discovery of Heuristics for Nondeterministic Programs from Sample Execution Traces (Classic Reprint) - Couverture souple

Stolfo, Salvatore J.

9781334237386: Automatic Discovery of Heuristics for Nondeterministic Programs from Sample Execution Traces (Classic Reprint)

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ISBN 10 : 1334237387 ISBN 13 : 9781334237386

Editeur : Forgotten Books, 2018

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�diteur: Forgotten Books
Date d'�dition: 2018
Langue: anglais
ISBN 10: 1334237387
ISBN 13: 9781334237386
Reliure: Broch�
Nombre de pages: 182
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9780656317059: Automatic Discovery of Heuristics for Nondeterministic Programs From Sample Execution Traces (Classic Reprint)

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ISBN 10 : 0656317051 ISBN 13 : 9780656317059
Editeur : Forgotten Books, 2018
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Automatic Discovery of Heuristics for Nondeterministic Programs From Sample

Salvatore J. Stolfo

Edit� par Forgotten Books, 2024

ISBN 10 : 1334237387 ISBN 13 : 9781334237386

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Paperback. Etat : New. Print on Demand. This book delves into the fascinating field of artificial intelligence, specifically focusing on the challenge of encoding knowledge into computer programs. The author examines how to represent information in a way that allows for both flexibility and efficiency, a problem that has been central to AI research for decades. The book draws upon the work of pioneers in the field like Winograd, Sickel, and Davis, who have explored various approaches to incorporating procedural knowledge into primarily declarative frameworks. The author argues that automatic inference of control information from declarative programs is a promising avenue for improving the performance of AI systems. This approach involves analyzing the behavior of programs on sample inputs and then using this analysis to generate rules that guide future decisions. The book focuses on a technique for enhancing the efficiency of nondeterministic programs by analyzing their behavior on sample inputs. The author presents a sample problem, a jigsaw puzzle, and explains how this problem can be represented using a production system. Through this example, the author demonstrates how to improve the performance of a nondeterministic program by automatically inferring simple heuristics that guide its actions. The book explores a language called CRAPS, designed for describing and generating such heuristics. The author discusses the development of meta-rules, which serve as a "fine-tuning" mechanism to correct any inaccuracies in the automatically generated heuristics. This book offers valuable insights into the intricate problem of building AI systems that can effectively learn and adapt from experience, a crucial step towards creating more intelligent and capable machines. This book is a reproduction of an important historical work, digitally reconstructed using state-of-the-art technology to preserve the original format. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in the book. print-on-demand item. N� de r�f. du vendeur 9781334237386_0

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