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COSC343 Artificial Intelligence

An introduction to modern AI representation systems and problem-solving techniques.

Paper title Artificial Intelligence
Paper code COSC343
Subject Computer Science
EFTS 0.1500
Points 18 points
Teaching period First Semester
Domestic Tuition Fees (NZD) $1,080.30
International Tuition Fees (NZD) $4,858.95

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COSC 242
Schedule C
Arts and Music, Science

Computer Science Adviser (

Teaching staff
Lecturer: Dr Lech Szymanski
Paper Structure

In this paper we will look at some different definitions of intelligence and at the concept of intelligent agents. As a way of focusing on the hard problems to be solved in AI, we will do some practical work with LEGO robots, concentrating on the issue of how to get information about the world and how to make use of it. We will consider techniques for machine learning and probabilistic reasoning. Almost every human ability results from learning from experience: we will look at how these learning processes can be modelled computationally.

Topics to be considered include:

  • Embodied AI and robots
  • Machine learning algorithms (with a focus on neural networks)
  • Search and optimisation algorithms (including genetic algorithms)
  • Probabilistic reasoning method (including Bayesian methods)
  • Natural language processing


  • Two assignments 40%
  • Final exam 60%
Teaching Arrangements
There are two lectures per week, plus weekly lab or tutorial sessions.
Artificial Intelligence A Modern Approach (Third Edition) , by Stuart Russell and Peter Norvig, Prentice Hall 2010.
Course outline
View the course outline for COSC 343
Graduate Attributes Emphasised
Interdisciplinary perspective, Scholarship, Communication, Ethics, Teamwork.
View more information about Otago's graduate attributes.
Learning Outcomes
  • Students will gain an understanding of some of the core concepts in symbolic AI research: autonomous agents, planning and search, logical knowledge representation formalisms, grammars for representing knowledge of natural language
  • Students will gain an understanding of some of the core concepts in sub-symbolic AI research: sensory perception, motor control, machine learning, decision trees, neural networks
  • In each case, this understanding will be strengthened through practical exercises and experience with implemented systems
  • The paper will also give students an awareness of the increasing influence of these technologies in daily life

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First Semester

Teaching method
This paper is taught On Campus
Learning management system


Stream Days Times Weeks
A1 Monday 09:00-09:50 9-15, 17, 19-22
Wednesday 09:00-09:50 9-12, 14-15, 17-22


Stream Days Times Weeks
Attend one stream from
A1 Wednesday 14:00-15:50 9-12, 14-15, 17-22
A2 Wednesday 16:00-17:50 9-12, 14-15, 17-22
A3 Thursday 16:00-17:50 9-12, 14-15, 17-22