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COMP24011 Introduction to AI syllabus 2021-2022

COMP24011 Introduction to AI

Level 2
Credits: 10
Enrolled students: pending

Course leader: Ian Pratt-Hartmann


Additional staff: view all staff

Requisites

  • Pre-Requisite (Compulsory): COMP11120
  • Pre-Requisite (Compulsory): COMP13212

Additional requirements

  • COMP11120 (not a pre-requisite for CM)

Assessment methods

  • 80% Written exam
  • 20% Coursework
Timetable
SemesterEventLocationDayTimeGroup
Sem 1 w1-5,7-12 ONLINE Lecture Tue 09:00 - 10:00 -
Sem 1 w2,4,7,9,11-12 Lab *SD 1.8+1.10 Mon 09:00 - 11:00 -
Sem 1 w2,4,7,9,11-12 Lab *SD 1.8+1.10 Wed 09:00 - 11:00 -
Sem 1 w2,4,7,9,11-12 Lab *SD 1.8+1.10 Thu 09:00 - 11:00 -
Sem 1 w2,4,7,9,11-12 Lab *SD 1.8+1.10 Fri 11:00 - 13:00 -
Sem 1 w2,4,7,9,11-12 Lab *SD 1.8+1.10 Tue 11:00 - 13:00 -
Sem 1 w2,4,7,9,11-12 Lab *SD 1.8+1.10 Thu 13:00 - 15:00 -
Sem 1 w2,4,7,9,11-12 Lab *SD 1.8+1.10 Tue 13:00 - 15:00 -
Sem 1 w2,4,7,9,11-12 Lab *SD 1.8+1.10 Mon 16:00 - 18:00 -
Sem 1 w2,4,7,9,11-12 Lab *SD 1.8+1.10 Thu 16:00 - 18:00 -

Overview

The Unit constitutes an introduction to the field of Artificial Intelligence, aiming at once to give a broad overview of the subject and to serve as a basis for more detailed third year courses, particularly, COMP34120 (AI and Games), COMP34212 (Cognitive Robotics), COMP34412 (Natural Language Systems) and COMP37212 (Computer Vision).

This course unit detail provides the framework for delivery in 20/21 and may be subject to change due to any additional Covid-19 impact.  Please see Blackboard / course unit related emails for any further updates.

Aims

A student completing this course should:

  • be able to implement basic search- and planning-algorithms from Artificial Intelligence, and apply them to real-world problems;
  • be able to apply first-order logic to model physical situations and reason about the effects of actions,
  • to appreciate the limitations of logic and to select appropriate responses to these limitations;
  • be able to develop formal ontologies to represent knowledge in different domains;
  • be able to select and apply the principal models of uncertainty employed in Artificial Intelligence in concrete problem-solving situations;
  • be able to solve the problem of sensor integration, and to implement simultaneous localization and mapping in robotics;
  • be able to apply techniques for representing (qualitative) temporal and spatial information in Artificial Intelligence;
  • have an appreciation of the central philosophical problems connected with artificial intelligence.

Syllabus

Topic 1. Search and planning:
   problem-solving as search; adversarial games; classical planning.
Topic 2. Logic and reasoning
   review of first-order logic; applications of logic to planning; logic versus reasoning; default `logic'.
Topic 3. AI and probability
   review of probability theory; alternative representations of uncertainly; Bayes' networks.
Topic 4. Knowledge representation
   ontology-driven database access; formal ontologies and knowledge-representation.
Topic 5. The periphery:
   sensors and actuators; sensor integration, simultaneous localization and mapping.
Topic 6. Philosophical issues:
   the Turing test; the meaning of `AI'; the problem of consciousness.

Teaching methods

2 hours lectures per week (22 hours in total), 2 hours of lab per fortnight (8 hours in total)

Feedback methods

Exam and assessments

Coursework:
 
Lab 1: Syllogisms
Lab 2: Games
Lab 3: Fuzzy logic
Lab 4: SLAM

Study hours

  • Lectures (22 hours)
  • Practical classes & workshops (8 hours)

Learning outcomes

On successful completion of this unit, a student will be able to:

On the successful completion of the course, students will be able to: 
  • ILO 1 To be able to implement basic search- and planning-algorithms from Artificial Intelligence, and apply them to real-world problems.
  • ILO 2 To be able to apply first-order logic to model physical situations and to reason about the effects of actions.
  • ILO 3 To appreciate the limitations of logic and to be able to select appropriate responses to these limitations.
  • ILO 4 To be able to develop formal ontologies to represent knowledge in different domains.
  • ILO 5 To be able to select and apply the principal models of uncertainty employed in Artificial Intelligence in concrete problem-solving situations.
  • ILO 6 To be able to solve the problem of sensor integration, and to implement simultaneous localization and mapping in robotics.
  • ILO 7 To be able to apply techniques for representing (qualitative) temporal and spatial information in Artificial Intelligence.
 

Reading list

TitleAuthorISBNPublisherYear
Artificial intelligence : a modern approach Russell, Stuart J.0132071487 (pbk.) :; 9780132071482 (pbk.) :Pearsonc2010.
Knowledge representation and reasoning Brachman, Ronald J.,1558609326; 9781558609327Morgan Kaufmann©2004.
Computer vision : models, learning, and inference Prince, Simon J. D.1107011795 (hbk.) :; 9781107011793 (hbk.) :Cambridge University Press2012.
Computer Vision : Algorithms and Applications Szeliski, Richard.9781848829350Springer London2011.