Course Code and Name: SEEM4560 Computational Intelligence for Decision Making

Course Objectives:

- Introduction to knowledge-based system, neural computing, genetic algorithm, and fuzzy logic.

- Inference methods and uncertainty management in design and implementation of expert systems.

- Application of computational intelligence techniques to management decision systems in specific business areas

Course Outcomes:


  1. Competent in understanding the different roles of computational intelligence techniques for solving practical engineering applications
  2. Able to develop a knowledge-base system using rule-based programming on an expert-system shell
  3. Able to understand the basics in uncertainty reasoning, using probabilistic approach such as Bayesian rule, certainty-factor, and fuzzy sets
  4. Able to formulate an expert system for diagnostic reasoning using CLIPS, involving uncertainty handling for a number of typical engineering applications





Programme Outcomes:

(P1)    The ability to apply knowledge of mathematics, science, and engineering appropriate to the degree discipline (K/S)

(P2)    The ability to design and conduct experiments, as well as to analyze and interpret data (K/S)

(P3)    The ability to design a system, component, or process to meet desired needs within realistic constraints, such as economic, environmental, social, political, ethical, health and safety, manufacturability and sustainability (K/S)

(P4)    The ability to function in multi-disciplinary teams (S/V)

(P5)    The ability to identify, formulate, and solve engineering problems (K/S)

(P6)    The understanding of professional and ethical responsibility (V)

(P7)    The ability to communicate effectively (S)

(P8)    The ability to understand the impact of engineering solutions in a global and societal context, especially the importance of health, safety and environmental considerations to both workers and the general public (V)

(P9)    The ability to recognize the need for, and to engage in life-long learning (V)

(P10) The ability to stay abreast of contemporary issues (S/V)

(P11) The ability to use the techniques, skills, and modern engineering tools necessary for engineering practice appropriate to the degree discipline (K/S)

(P12) The ability to use the computer/IT tools relevant to the discipline along with an understanding of their processes and limitations (K/S/V)

(P13) The ability to apply the skills relevant to the discipline of operations research and information technology and their applications in engineering and managerial decision making, especially in financial services, logistics and supply chain management, business information systems, and service engineering and management (K/S)


K = Knowledge outcomes

S = Skills outcomes

V = Values and attitude outcomes

Weights (in %):































Course Outcome(s) is/are measurable or not: Yes / Yes (Partial) / No (Please choose). If Yes, please suggest ways to measure:

This course contributes to

(P1) by teaching probability, fuzzy system, and expert system with uncertainty reasoning. It can be measured by assignments and examination questions

(P2) by using practical examples for financial and engineering problems with practical data. Similar questions in assignments are used to test and measure the analytical skill involved

(P3) by teaching the basic structure of an expert system and explaining the different components and their purposes. Relevant assignment and examination questions are used for measurement

(P5) by using examples to demonstrate how practical engineering problems can be tackled by expert systems. Examination questions are used for measurements

(P7) by providing sample programming solutions in class. Marking of assignments and mid-term are useful for measurements on a comparison metric

(P11) by demonstrating the computer programming skill in using CLIPS needed for formulating an expert system. Measurements by appropriate examination questions in testing programming skill

(P13) by introducing relevant examples in financial and engineering problems, such as oil drilling and diagnostics, and formulating fuzzy rules in motor speed control and financial decision problems