Decision Support System and Expert System(DSS) Syllabus

Course Description: This course is a study uses of artificial intelligence in business decision making. Emphasis will be given in business decision making process, design and development of decision support systems and expert systems.

Course Objectives:

    • Introduce intelligent business decision making

    • Discuss design, development and evaluation of DSS Systems

    • Discuss various models of building DSS systems

    • Explain Concept behind expert systems

1. Decision Making and Computerized Support

teaching hours: 8 hrs

1.1. Management Support Systems:

An OverviewManagers and Decision-Making; Managerial Decision-Making and Information Systems;

 Managers and Computer Support; Computerized Decision Support and the Supporting

Technologies; A Framework for Decision Support; The Concept of Decision Support Systems;

Group Support Systems; Enterprise Information Systems; Knowledge Management Systems;

Expert Systems; Artificial Neural Networks; Advanced Intelligent Decision Support Systems;

Hybrid Support Systems

1.2. Decision-Making Systems, Modeling, and Support Decision-Making:

Introduction and Definitions; Systems; Models; Phases of the Decision-

Making Process; Decision-Making: The Intelligence Phase; Decision-Making: The Design

Phase; Decision-Making: The Choice Phase; Decision-Making: The Implementation Phase; How

Decisions Are Supported; Personality Types, Gender, Human Cognition, and Decision Styles;

The Decision-Makers

2. Decision Support Systems

teaching hours: 14 hrs

2.1. Decision Support Systems: 

An Overview DSS Configurations; What Is a DSS?; Characteristics and Capabilities of DSS; Components of

DSS; The Data Management Subsystem; The Model Management Subsystem; The User

Interface (Dialog) Subsystem; The Knowledge-Based Management Subsystem; The User; DSS

Hardware; DSS Classifications

2.2. Modeling and Analysis

MSS Modeling; Static and Dynamic Models; Certainty, Uncertainty, and Risk; Influence

Diagrams; MSS Modeling with Spreadsheets; Decision Analysis of a Few Alternatives (Decision

Tables and Decision Trees); The Structure of MSS Mathematical Models; Mathematical

Programming Optimization; Multiple Goals, Sensitivity Analysis, What-If, and Goal Seeking;

Problem-Solving Search Methods; Heuristic Programming; Simulation; Visual Interactive

Modeling and Visual Interactive Simulation; Quantitative Software Packages; Model Base

Management

2.3.Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business

Analytics, and Visualization

The Nature and Sources of Data; Data Collection, Problems, and Quality; The Web/Internet and

Commercial Database Services; Database Management Systems in Decision Support Systems/

Business Intelligence; Database Organization and Structures; Data Warehousing; Data Marts;

Business Intelligence/Business Analytics; Online Analytical Processing (OLAP); Data Mining;

Data Visualization, Multidimensionality, and Real-Time Analytics; Geographic Information

Systems; Business Intelligence and the Web: Web Intelligence/Web Analytics

2.4. Decision Support System Development

Introduction to DSS Development; The Traditional System Development Life Cycle; Alternative

Development Methodologies; Prototyping: The DSS Development Methodology; Change

Management; DSS Technology Levels and Tools; DSS Development Platforms; DSS

Development Tool Selection; Team-Developed DSS; End User Developed DSS; Putting The

DSS Together

3. Knowledge Management

teaching hours: 5 hrs

3.1. Knowledge Management

Introduction to Knowledge Management; Organizational Learning and Transformation;

Knowledge Management Initiatives; Approaches to Knowledge Management; Information

Technology in Knowledge Management; Knowledge Management Systems Implementation;

Roles of People in Knowledge Management; Ensuring Success of Knowledge Management

4. Intelligent Decision Support Systems

teaching hours: 18 hrs

4.1. Artificial Intelligence and Expert Systems:

Knowledge-Based Systems Concepts and Definitions of Artificial Intelligence; Evolution of Artificial Intelligence; TheArtificial Intelligence Field; Basic Concepts of Expert Systems; Applications of Expert Systems; Structure of Expert Systems; How Expert Systems Work; Problem Areas Suitable for Expert Systems; Benefits and Capabilities of Expert Systems; Problems and Limitations of Expert Systems; Expert System Success Factors; Types of Expert Systems; Expert Systems on the Web

4.2. Knowledge Acquisition, Representation, and Reasoning

Concepts of Knowledge Engineering; Scope and Types of Knowledge; Methods of Knowledge Acquisition from Experts; Knowledge Acquisition from Multiple Experts; Automated Knowledge Acquisition from Data and Documents; Knowledge Verification and Validation; Representation of Knowledge; Reasoning in Rule-Based Systems; Explanation and Metaknowledge; Inferencing with Uncertainty; Expert Systems Development; Knowledge Acquisition and the Internet

4.3. Advanced Intelligent Systems

Machine-Learning Techniques; Case-Based Reasoning; Basic Concept of Neural Computing;

Learning in Artificial Neural Networks; Developing Neural Network-Based Systems; Genetic

Algorithms Fundamentals; Developing Genetic Algorithm Applications; Fuzzy Logic

Fundamentals; Developing Integrated Advanced Systems

4.4. Intelligent Systems over the Internet

Web-Based Intelligent Systems; Intelligent Agents: An Overview; Characteristics of Agents;

Why Intelligent Agents?; Classification and Types of Agents; Internet-Based Software Agents;

DSS Agents and Multi-Agents; Semantic Web: Representing Knowledge for Intelligent Agents;

Web-Based Recommendation Systems; Managerial Issues of Intelligent Agents

 Student should study some widely used decision support systems and expert systems. Besides, student need to develop decision support systems or expert systems as a miniproject.