Computational Intelligence for Decision Support

Series:
International Series on Computational Intelligence
Published:
November 24, 1999 by CRC Press - 400 Pages
Author(s):
Zhengxin Chen, University of Nebraska, Omaha, Nebraska, USA

Purchasing Options

Hardback
$189.95
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ISBN 9780849317996
Cat# 1799
 

Features

  • Addresses reasoning as an extension of retrieval, integrating computational intelligence and DBMS
  • Highlights elements of decision support from both science and business
  • Provides necessary background of AI, DBMS, and IR in a concise introduction
  • Supplies many examples and a bibliography of recent research papers

    • Summary

    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
    The Need for Decision Support Agents
    Computerized Decision Support Mechanisms
    Computational Intelligence for Decision Support
    A Remark on Terminology
    Data, Information, and Knowledge
    Issues to be Discussed in This Book
    SEARCH AND REPRESENTATION
    Sample Problems and Applications of Computational Intelligence
    Definition of Computational Intelligence
    Basic Assumptions of Computational Intelligence
    Basic Storage and Search Structures
    Problem Solving Using Search
    Representing Knowledge for Search
    State Space Search
    Remark on Constraint-Based Search
    Planning and Machine Learning as Search
    PREDICATE LOGIC
    First Order Predicate Logic
    Prolog for Computational Intelligence
    Abduction and Induction
    Nonmonotonic Reasoning
    RELATIONS AS PREDICATES
    The Concept of Relation
    Overview of Relational Data Model
    Relational Algebra
    Relational Views and Integrity Constraints
    Functional Dependencies
    Basics of Relational Database Design
    Multivalued Dependencies
    Remark on Object-Oriented Logical Data Modeling
    Basics of Deductive Databases
    Knowledge Representation Meets Databases
    RETRIEVAL SYSTEMS
    Database Management Systems (DBMS)
    Commercial Languages for Database Management Systems
    Basics of Physical Database Design
    An Overview of Query Processing and Transaction Processing.
    Information Retrieval (IR)
    Data Warehousing
    Rule-Based Expert Systems
    Knowledge Management and Ontologies
    CONCEPTUAL DATA AND KNOWLEDGE MODELING
    OVERVIEW
    Entity-Relationship Modeling
    Remark on Legacy Data Models
    Knowledge Modeling for Knowledge Representation
    Structured Knowledge Representation
    Frame Systems
    Conceptual Graphs
    User Modeling and Flexible Inference Control
    REASONING AS EXTENDED RETRIEVAL
    Beyond Exact Retrieval
    Reasoning as Query-Invoked Memory Re-Organization
    COMPUTATIONAL CREATIVITY AND COMPUTER ASSISTED HUMAN INTELLIGENCE
    Computational Aspects of Creativity
    Idea Processors
    Retrospective Analysis for Scientific Discovery and Technical Invention
    Combining Creativity with Expertise
    CONCEPTUAL QUERIES AND INTENSIONAL ANSWERING
    A Review of Question Answering Systems
    Intensional Answering and Conceptual Query
    An Approach for Intensional Conceptual Query Answering
    FROM MACHINE LEARNING TO DATA MINING
    Basics of Machine Learning
    Inductive Learning
    Efficiency and Effectiveness of Inductive Learning
    Other Machine Learning Approaches
    Features of Data Mining
    Categorizing Data Mining Techniques
    Association Rules
    DATA WAREHOUSING, OLAP, AND DATA MINING
    Data Mining in Data Warehouses
    Decision Support Queries, Data Warehouse, and OLAP
    Data Warehouse as Materialized Views and Indexing
    Remarks on Physical Design of Data Warehouses
    Semantic Differences Between Data Mining and OLAP
    Nonmonotonic Reasoning in Data Warehouding Environment
    Combining Data Mining and OLAP
    Conceptual Query Answering Data Warehouses
    Web Mining
    REASONING UNDER UNCERTAINTY
    General Remarks on Uncertain Reasoning
    Uncertainty Based on Probability Theory
    Fuzzy Set Theory
    Fuzzy Rules and Fuzz Expert Systems
    Using Fuzzy CLIPS
    Fuzzy Controllers
    The Nature of Fuzzy Logic
    REDUCTION AND RECONSTRUCTION APPROACHES FOR UNCERTAIN REASONING AND DATA MINING
    The Reduction-Reconstruction Duality
    Some Key Ideas of K-systems Theory and Rough Set Theory
    Rough Sets Approach
    K-Systems Theory
    TOWARD INTEGRATED HEURISTIC DECISION MAKING
    Integrated Problem Solving
    High-Level Heuristics for Problem Solving and Decision Support
    Meta-Issues for Decision Making
    Each section also contains an overview, summary, and self-examination questions.

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