Module Code: H9AIDM
Long Title AI Driven Decision Making
Title AI Driven Decision Making
Module Level: LEVEL 9
EQF Level: 7
EHEA Level: Second Cycle
Credits: 5
Module Coordinator: Ade Fajemisin
Module Author: Margarete Silva
Departments: School of Computing
Specifications of the qualifications and experience required of staff

MSc and/or PhD degree in computer science, mathematics or cognate discipline. May have industry experience also.

Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Critically assess theories and models of decision making to contextualise artificial intelligence driven approaches for decision making.
LO2 Model and solve a variety of real-world problems as constraint satisfaction and optimisation problems.
LO3 Identify and apply appropriate artificial intelligence driven decision-making approaches (e.g., Bayesian Networks, Fuzzy Systems, Evolutionary Systems) to solve problems across various application domains.
LO4 Implement, compare, contrast, and critically evaluate alternative artificial intelligence algorithmic approaches to determine their suitability for decision making with sample optimisation problems.
Module Recommendations

This is prior learning (or a practical skill) that is required before enrolment on this module. While the prior learning is expressed as named NCI module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).

No recommendations listed
Co-requisite Modules
No Co-requisite modules listed
Entry requirements

Learners are required to hold a minimum of a level 8 honours qualification, or equivalent on the National Qualifications Framework, and must be from a cognate background.


Module Content & Assessment

Indicative Content
Module Introduction
Theory & Models of Decision Making (e.g., Simon / Boyd) Structured, semi-structured, unstructured decision making Decision Support Systems Intelligent Decision Support Systems AI/Human Decision Making context (Support, Augment, Replace, Automate) Explainable AI Expert Systems, Case-based & Rule-based systems / Historical Perspective AI approaches to decision making covered in the module Ethical questions and implications
Introduction to Optimization
Components of an optimisation problem Maximization and Minimization problems Graph optimization Application to real-world problems such as cutting stock problems, vehicle routing problems, scheduling problems, etc.
Linear and Integer Programming
Basic properties of Linear Programming problems Linear Programming formulation Mixed Integer Programming Algorithms for solving optimisation problems: Branch-and-Bound, Branch-and-Price
Constraint Programming
Modelling problems using constraint programming Constraint propagation using arc-consistency, node-consistency and path consistency Backtracking search algorithms Local search methods Applications of constraint programming
Bayesian Networks
Random variables Bayes's Theorem and Conditional Probability Bayesian Networks Real-world applications of Bayesian Networks, e.g. image processing, information retrieval, troubleshooting, etc.
Fuzzy Systems
Fuzzy Sets Fuzzy Logic Membership Functions Fuzzy Reasoning Fuzzy Decision Making
Evolutionary Systems
Genetic Algorithms Operators in Genetic Algorithms Stopping Conditions and Constraints Classification of GAs
Evolutionary Systems
Swarm Intelligence Memetic Algorithms
Fitness landscapes Local search Simulated annealing
Tabu Search Variable neighbourhood search
Hybrid Systems
Combining metaheuristics with mathematical programming, constraint programming, machine learning and data mining
Survey and analysis of AI decision making across a number of application domains.
Assessment Breakdown%


Full Time

Assessment Type: Formative Assessment % of total: Non-Marked
Assessment Date: n/a Outcome addressed: 1,2,3,4
Non-Marked: Yes
Assessment Description:
Formative assessment will be provided on the in-class individual or group activities. Feedback will be provided in written or oral format, or on-line through Moodle. In addition, in class discussions will be undertaken as part of the practical approach to learning.
Assessment Type: Continuous Assessment % of total: 40
Assessment Date: n/a Outcome addressed: 2
Non-Marked: No
Assessment Description:
The learner will be required to model and solve a series of problems using integer programming
Assessment Type: Project % of total: 60
Assessment Date: n/a Outcome addressed: 1,2,3,4
Non-Marked: No
Assessment Description:
Long-form project which the student produces over the course of the entire semester. Student is required to research and utilise a suite of AI based algorithms and approaches to provide decision making capabilities in a chosen application problem domain. The results of applying these techniques should then be critically evaluated. It is required to submit a project report including: (a) the background research that has been conducted, (b) the methodology applied to complete the project, (c) implementation details, (d) experimentation details, (e) evaluation of results, and (f) conclusion.
No End of Module Assessment
No Workplace Assessment
Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.

NCIRL reserves the right to alter the nature and timings of assessment


Module Workload

Module Target Workload Hours 0 Hours
Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture In-class lectures 24 Per Semester 2.00
Lab Application of concepts presented in lectures 24 Per Semester 2.00
Independent Learning Independent learning 77 Per Semester 6.42
Total Weekly Contact Hours 4.00

Module Resources

Recommended Book Resources
  • Frederick S. Hillier,Gerald J. Lieberman. Introduction to Operations Research, [ISBN: 9781259253188].
  • William Kocay,Donald L. Kreher. (2016), Graphs, Algorithms, and Optimization, Second Edition, CRC Press, p.546, [ISBN: 9781482251166].
  • Francesca Rossi,Peter Van Beek,Toby Walsh. (2006), Handbook of Constraint Programming, Elsevier Science Limited, p.955, [ISBN: 9780444527264].
  • Norman Fenton,Martin Neil. (2012), Risk Assessment and Decision Analysis with Bayesian Networks, CRC Press, p.524, [ISBN: 9781439809112].
Supplementary Book Resources
  • El-Ghazali Talbi. (2009), Metaheuristics: From Design to Implementation.
  • Kartik Hosanagar. (2019), A Human's Guide to Machine Intelligence, Penguin, p.272, [ISBN: 9780525560890].
Supplementary Article/Paper Resources
This module does not have any other resources
Discussion Note: