Module Details

The information contained in this module specification was correct at the time of publication but may be subject to change, either during the session because of unforeseen circumstances, or following review of the module at the end of the session. Queries about the module should be directed to the member of staff with responsibility for the module.
Code COMP219
Coordinator Dr FA Oliehoek
Computer Science
Year CATS Level Semester CATS Value
Session 2017-18 Level 5 FHEQ First Semester 15


    To provide an introduction to the topic of Artificial Intelligence (AI) through studying problem-solving, knowledge representation, planning, and learning in intelligent systems.
  • To provide a grounding in the AI programming language Prolog.

  • Learning Outcomes

    At the end of this module, students should be able to:

    identify or describe the characteristics of intelligent agents and the environments that they can inhabit;

    identify, contrast and apply to simple examples the major search techniques that have been developed for problem-solving in AI;

    distinguish the characteristics, and advantages and disadvantages, of the major knowledge representation paradigms that have been used in AI, such as production rules, semantic networks, propositional logic and first-order logic;

    solve simple knowledge-based problems using the AI representations studied;

    identify or describe approaches used to solve planning problems in AI and apply these to simple examples;

    identify or describe the major approaches to learning in AI and apply these to simple examples;

    identify or describe some of the major applications of AI;

    understand and write Prolog code to solve simple knowledge-based problems.


    • Introduction (3 lectures): What is Artificial Intelligence? Characterisation of AI; historical overview; intelligent agents; agents’ environments; applications of AI; current state-of-the-art.
    • Problem-Solving Through Search (7 lectures): Problem formulation; uninformed search strategies; informed search strategies; search in complex environments; adversarial search.
    • Knowledge Representation (4 lectures): Characterisation plus advantages and disadvantages of rule-based systems, semantic networks, ontologies and logics.  Example applications of different knowledge representation schemes.
    • Logic (4 lectures): Reasoning in propositional and first-order logic.
    • Planning (3 lectures): Representing planning problems; classical planning approaches, including search, heuristics and satisfiability; planning in complex environments.
    • Learning (4 lectures): Different forms of learning; logic and learning; reinforcement learning.
    • Basics of Prolog (5 lectures): Facts, rules and queries; recursion; lists; negation as failure.

    Teaching and Learning Strategies

    Lecture -

    3 per week during semester

    Laboratory Work -

    Teaching Schedule

      Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
    Study Hours 30


    Timetable (if known) 3 per week during semester
    Private Study 115


    EXAM Duration Timing
    % of
    Penalty for late
    Unseen Written Exam  2 hours  80  Yes  Standard UoL penalty applies  Final Exam Notes (applying to all assessments) Class test on rest of syllabus (week 12) This work is marked anonymously Class test on Prolog (week 6) This work is marked anonymously. Written examination Resit Exam replaces failed CA components, the Learning Outcomes are covered by the Resit Exam.  
    CONTINUOUS Duration Timing
    % of
    Penalty for late
    Coursework  1 hour  Week 6  10  Yes  Standard UoL penalty applies  Class test on Prolog 
    Coursework  1 hour  Week 12  10  Yes  Standard UoL penalty applies  Class test on rest of syllabus 

    Recommended Texts

    Reading lists are managed at Click here to access the reading lists for this module.
    Explanation of Reading List: