Computational Intelligence for Decision Support  book cover
1st Edition

Computational Intelligence for Decision Support

ISBN 9780367399238
Published September 5, 2019 by CRC Press
400 Pages

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Book Description

Intelligent decision support relies on techniques from a variety of disciplines, including artificial intelligence and database management systems. Most of the existing literature neglects the relationship between these disciplines. By integrating AI and DBMS, Computational Intelligence for Decision Support produces what other texts don't: an explanation of how to use AI and DBMS together to achieve high-level decision making.

Threading relevant disciplines from both science and industry, the author approaches computational intelligence as the science developed for decision support. The use of computational intelligence for reasoning and DBMS for retrieval brings about a more active role for computational intelligence in decision support, and merges computational intelligence and DBMS. The introductory chapter on technical aspects makes the material accessible, with or without a decision support background. The examples illustrate the large number of applications and an annotated bibliography allows you to easily delve into subjects of greater interest.

The integrated perspective creates a book that is, all at once, technical, comprehensible, and usable. Now, more than ever, it is important for science and business workers to creatively combine their knowledge to generate effective, fruitful decision support. Computational Intelligence for Decision Support makes this task manageable.

Table of Contents

Decision Support and Computational Intelligence. Search and Representation. Predicate Logic. Relations as Predicates. Retrieval Systems. Conceptual Data and Knowledge Modeling. Reasoning as Extended Retrieval. Computational Creativity and Computer Assisted Human Intelligence. Conceptual Queries and Intensional Answering. From Machine Learning to Data Mining. Data Warehousing, OLAP, and Data Mining. Reasoning Under Uncertainty. Reduction and Reconstruction Approaches for Uncertain Reasoning and Data Mining. Toward Integrated Heuristic Decision Making.

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Chen, Zhengxin