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Unit information: Artificial Intelligence (Teaching Unit) in 2021/22

Please note: you are viewing unit and programme information for a past academic year. Please see the current academic year for up to date information.

Unit name Artificial Intelligence (Teaching Unit)
Unit code COMS30014
Credit points 0
Level of study H/6
Teaching block(s) Teaching Block 1 (weeks 1 - 12)
Unit director Dr. Ray
Open unit status Not open

COMS10016 Imperative and Functional Programming and COMS10017 Object-Oriented Programming and Algorithms I or equivalent

COMS10014 Mathematics for Computer Science A and COMS10013 Mathematics for Computer Science B or equivalent

COMS20011 Data-Driven Computer Science or equivalent

Programming paradigms, mathematics (including statistics, probability and algebra), and also desirable basic ideas of data mining/analysis


EITHER Assessment Unit COMS30013 Artificial Intelligence (10 credit examination assessment)

OR COMS30062 Artificial Intelligence (15 credit coursework assessment).

Please note:

COMS30014 is the Teaching Unit for the Artificial Intelligence option.

Single Honours Computer Science and Mathematics and Computer Science students can choose to be assessed by either examination (10 credits, COMS30013) or coursework (15 credits, COMS30062) by selecting the appropriate co-requisite assessment unit.

Any other students that are permitted to take the Artificial Intelligence option are assessed by examination (10 credits) and should be enrolled on the co-requisite exam assessment unit (COMS30013).

School/department School of Computer Science
Faculty Faculty of Engineering

Description including Unit Aims

Artificial Intelligence (AI) systems and tools are virtually everywhere around us at present, no longer being just ‘science fiction’. Since Alan Turing, considered as the father of AI, postulated the question “can machines think?”, the world has witnessed innumerable advances in the field. “Thinking machines” are continuously developed worldwide to contribute to the societal good, in many aspects and sectors like economy, sustainability, safety, fairness, education, health, manufacturing and entertainment, to name a few. But, what are the foundations behind these “thinking machines” and intelligent tools?

This unit introduces the field of AI and its foundational principles, techniques and algorithms. It firstly covers the basics of knowledge representation and reasoning, followed by AI methods for search and optimisation. These foundations are then used in the second half of the unit, where the paradigm of intelligent agents, multi-agent systems and automated planning techniques are covered.

We will introduce and explore the main paradigms behind AI:

  • Knowledge representation using logics, ontologies and rules. How do AI systems reason and make inferences upon knowledge.
  • Search techniques for solving problems defined in large possible solution domains.
  • Understanding and defining heuristic functions for guiding AI systems in search processes.
  • Overviewing meta-heuristic based techniques to solve problems requiring optimisation.
  • Autonomous agents and multi-agent systems that perceive, reason, coordinate, make decisions and act to achieve goals by themselves.
  • How AI systems (agents) can undertake planning for sequential decision-making and perform multiple consecutive actions in deterministic and uncertain contexts.

We will also apply the above paradigms to define AI agents or teams of them to solve challenging real-world tasks or complex problem-solving games that would normally require capabilities resembling human intelligence.

Intended Learning Outcomes

Successful completion of the unit will enable students to:

  1. Understand fundamental concepts of logical representation and apply knowledge-driven inference and reasoning processes over knowledge typically dealt with by an AI system.
  2. Apply a variety of search and optimisation algorithms to find solutions for problems defined in a complex search space.
  3. Be familiar with the concept of heuristic and apply some well-known meta-heuristics approaches.
  4. Be familiar with the core idea of intelligent agent, environment, perception and action.
  5. Be able to understand and design a simple agent-based and multi-agent based architecture to solve real-world problems requiring AI capabilities such as: reasoning over knowledge, planning, cooperating/coordinating, decision-making and acting.

When assessed by Examination, in addition to the general ILOs above, the student will be also able to:

  1. Critically identify and differentiate between the advantages, disadvantages, and main characteristics of different AI paradigms and techniques learnt in the unit.
  2. Apply reasoning and inference processes on various forms of knowledge representations.


When assessed by Coursework, in addition to the general ILOs above, the student will be also able to:

  1. Follow the basic steps of software engineering to define and implement an agent and/or multi-agent AI system capable of dealing with a real-world challenging problem or game requiring ‘human-like intelligence’ capabilities: searching for solutions and optimising, reasoning on knowledge, planning, coordinating with other AI entities, etc.

Teaching Information

Teaching will be delivered through a combination of synchronous and asynchronous sessions, including lectures, practical activities supported by drop-in sessions, problem sheets and self-directed exercises.

Teaching will take place over Weeks 1-7, with coursework support in weeks 9-11 and for students assessed by examination, consolidation and revision sessions in Weeks 12.

Assessment Information

Examination details:

2 hour exam (100%, 10 credits)


Coursework details:

Coursework (100%, 15 credits) - to be completed during a specific period.


If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.

If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. COMS30014).

How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours of study to complete. Your total learning time is made up of contact time, directed learning tasks, independent learning and assessment activity.

See the Faculty workload statement relating to this unit for more information.

The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit. The Board considers each student's outcomes across all the units which contribute to each year's programme of study. If you have self-certificated your absence from an assessment, you will normally be required to complete it the next time it runs (this is usually in the next assessment period).
The Board of Examiners will take into account any extenuating circumstances and operates within the Regulations and Code of Practice for Taught Programmes.