Algorithm Synthesis: A Comparative Study

A. Penny Anderson

ISBN 10: 0387969608 / ISBN 13: 9780387969602

Published by Springer New York, Springer US, 1989

About this Item

Druck auf Anfrage Neuware - Printed after ordering - In early 1986, one of us (D.M.S.) was constructing an artificial intelligence system to design algorithms, and the other (A.P.A.) was getting started in program transformations research. We shared an office, and exchanged a few papers on the systematic development of algorithms from specifications. Gradually we realized that we were trying to solve some of the same problems. And so, despite radical differences between ourselves in research approaches, we set out together to see what we could learn from these papers. That's how this book started: a couple of graduate students trying to cope with The Literature. At first, there was just a list of papers. One of us (D.M.S.) tried to cast the papers in a uniform framework by describing the problem spaces searched, an approach used in artificial intelligence for understanding many tasks. The generalized problem space descriptions, though useful, seemed to abstract too much, so we decided to compare papers by different authors dealing with the same algorithm. These comparisons proved crucial: for then we began to see similar key design choices for each algorithm.

Seller Inventory # 9780387969602

Bibliographic Details

• Title: Algorithm Synthesis: A Comparative Study
• Publisher: Springer New York, Springer US
• Publication Date: 1989
• Language: English
• ISBN 10: 0387969608
• ISBN 13: 9780387969602
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 229W x 152H x 8D millimeters 229 millimeters width by 152 millimeters height by 8 millimeters depth
• Weight: 203 grams

About this title

Synopsis

In early 1986, one of us (D.M.S.) was constructing an artificial intelligence system to design algorithms, and the other (A.P.A.) was getting started in program transformations research. We shared an office, and exchanged a few papers on the systematic development of algorithms from specifications. Gradually we realized that we were trying to solve some of the same problems. And so, despite radical differences between ourselves in research approaches, we set out together to see what we could learn from these papers. That's how this book started: a couple of graduate students trying to cope with The Literature. At first, there was just a list of papers. One of us (D.M.S.) tried to cast the papers in a uniform framework by describing the problem spaces searched, an approach used in artificial intelligence for understanding many tasks. The generalized problem space descriptions, though useful, seemed to abstract too much, so we decided to compare papers by different authors dealing with the same algorithm. These comparisons proved crucial: for then we began to see similar key design choices for each algorithm.