DSS and Expert System

DSS and Expert System focuses on how computer-based tools assist in complex decision-making through data analysis and simulated human reasoning. You will explore how Decision Support Systems (DSS) process large datasets to solve unstructured problems, while Expert Systems utilize specialized knowledge bases to provide human-like advice and solutions in specific domains.

DSS and Expert System

Course Title: DSS and Expert System

Course No: BIT405

Nature of the Course: Theory + Lab

Semester: 7

Full Marks: 60 + 20 + 20

Pass Marks: 24 + 8 + 8

Credit Hours: 3

Course Description

This course is a study of 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

To introduce intelligent business decision making, to describe design, development and evaluation of DSS Systems, to know various models of building DSS systems and to explain concept behind expert systems.

Course Contents

1. Introduction to Management Support Systems and Decision Making

14 hrs

1.1. Managers and decision making

Bounded rationality, muddling through

1.2. Factors in decision making

Memory, bias, intuition, experience, models, analytics

1.3. Qualitative vs. quantitative decision making

1.4. Computerized decision support and supporting technologies

1.5. DSS Framework, Building Decision Support Systems

1.6. Gaining Competitive Advantage with Decision Support Systems

Examples of Strategic DSS

DSS Benefits, Limitations, and Risks

2. The Make-up of a Decision Support System

15 hrs

2.1. Types and roles of DSS

2.2. Data component

Databases and DBMS, data warehouses

2.3. Model component

Representation, causality vs. Correlation

2.4. User Interface

ROMC Design Approach

2.5. Designing and Evaluating DSS Systems

Feasibility Study, Development Approach

3. Modeling Decisions

6 hrs

3.1. Introduction to Decision Analysis

3.2. Elements of Decision Problems

Uncertain events, consequences, Structuring Decisions

3.3. Making Choices, Sensitivity Analysis, Value of Information

4. Expert Systems

10 hrs

4.1. Definition and Features of Expert Systems

4.2. Architecture and Components

4.3. Persons Who Interact with Expert Systems

4.4. Advantages and Disadvantages

4.5. Expert Systems Development Life Cycle

Notes:

This syllabus follows the official BIT curriculum of Tribhuvan University. In case of any doubt or revision, the university’s published syllabus shall be considered authoritative.

DSS and Expert System

DSS and Expert System

Select University

Select Program

TabFlux . DSS and Expert System . TU . BIT