COMPSCI 761 : Advanced Topics in Artificial Intelligence


2023 Semester Two (1235) (15 POINTS)

Course Prescription

Examines the cornerstones of AI: representation, utilisation, and acquisition of knowledge. Taking a real-world problem and representing it in a computer so that the computer can do inference. Utilising this knowledge and acquiring new knowledge is done by search which is the main technique behind planning and machine learning. Research frontiers in artificial intelligence.

Course Overview

This course will cover the representation and utilisation of knowledge. These are the cornerstones of AI. You will investigate how to take a real-world problem and represent it in a computer so that the computer can solve that problem. Utilising this knowledge is done by search. The basics of search and its use in planning and in-game playing will be covered.

The course will consist of four modules:
  • Module 1: Search. Many AI problems can be solved in principle by carefully crafted search algorithms. One can even argue that most AI tasks, such as problem-solving, learning, and planning, involve some type of search.  This module covers fundamental search algorithms that include uninformed and informed search strategies as well as heuristics, which are "rules of thumb" for prioritising search options. We will also cover basic adversarial search strategies useful for multi-agent/game contexts.
  • Module 2: Logic. Logic is an important tool to capture the formal reasoning and problem-solving of an artificial agent. Many successful applications of AI are based on logic. The course will examine different logical languages and use Prolog as a tool to apply logic deduction to AI problems. Logic will also be important in knowledge representation as a general language to describe and reason about knowledge, both with and without uncertainty.
  • Module 3: Planning. Planning is a crucial ability of an intelligent agent when it is able to set goals and execute them. This includes developing a representation of the state of the world and making predictions about how decisions will affect the world states. In this course, we will examine important planning algorithms in artificial intelligence.
  • Module 4: Reasoning with Uncertainty: Uncertainty is a key feature of real-world problems. An AI system needs to be able to incorporate uncertainty into its reasoning mechanism. To achieve this, we will describe models such as Bayesian networks and Markov chains that facilitate probabilistic reasoning. We will also introduce utility theory and decision networks that allow an AI system to make optimal decisions.
  • Module 5: Natural language processing. NLP refers to the ability to read and comprehend human language. This is a crucial ability of an intelligent agent in communicating with humans. Applications of NLP include information retrieval, text mining, question answering and machine translation. This course will go over these applications as well as fundamental tools.
The knowledge taught in this course is essential for any student who has an interest in artificial intelligence. The skills developed in this course are particularly useful for those wishing to have a career involving artificial intelligence.

Course Requirements

Prerequisite: COMPSCI 220, and COMPSCI 225 or MATHS 254 Restriction: COMPSCI 367

Capabilities Developed in this Course

Capability 1: Disciplinary Knowledge and Practice
Capability 2: Critical Thinking
Capability 3: Solution Seeking
Capability 4: Communication and Engagement
Capability 5: Independence and Integrity
Capability 6: Social and Environmental Responsibilities

Learning Outcomes

By the end of this course, students will be able to:
  1. Demonstrate the ability of representing, in a declarative way, what it means for something to be a solution to a given problem. (Capability 1, 2, 3 and 4)
  2. Explain the main heuristic-search-based approaches to problem solving and their pro's and con's. (Capability 1, 2 and 3)
  3. Develop heuristic-based search strategies to solve AI problems (Capability 1, 2, 3, 4 and 5)
  4. Explain and communicate knowledge representation and intermediate knowledge representations. (Capability 2, 3, 4, 5 and 6)
  5. Describe data driven and goal driven inference and can program a declarative rule-based system. (Capability 2, 3, 4, 5 and 6)
  6. Develop and demonstrate the ability to use predicate logic calculus and apply logical inference on logic programs. (Capability 1, 2, 3, 4 and 5)
  7. Explain and apply fundamental AI tools and concepts in processing natural languages. (Capability 1, 2, 4, 5 and 6)
  8. Model and solve real-world problems using Bayesian networks, Markov chains and decision networks. (Capability 1, 2, 3 and 5)


Assessment Type Percentage Classification
Assignments 40% Individual Coursework
Test 30% Individual Test
Final Exam 30% Individual Examination
Assessment Type Learning Outcome Addressed
1 2 3 4 5 6 7 8
Final Exam


Tuākana Science is a multi-faceted programme for Māori and Pacific students providing topic specific tutorials, one-on-one sessions, test and exam preparation and more. Explore your options at

For more information and to find contact details for the Computer Science Tuākana coordinator, please see

Key Topics

  • Logic
  • Prolog
  • Search
  • Adversarial search
  • Constraint satisfaction
  • Knowledge representation
  • Inference
  • Planning
  • Reasoning with uncertainty
  • Natural language processing

Special Requirements

The exam is worth 45% of the total marks, the mid-term test is worth 10% of the total marks, the quizzes are worth 15%, and the assignments are worth 30%.

Workload Expectations

This course is a standard 15-point course and students are expected to spend 10 hours per week involved in each 15-point course that they are enrolled in.

For this course, you can expect:

  • 3 hours of lectures
  • 4 hours of reading and thinking about the content
  • 3-4 hours of work on assignments and/or test preparation

Delivery Mode

Campus Experience

This course is available for students who are remote.

Campus Experience
Lectures will be available as recordings. Other learning activities including possible Q&A sessions (tutorials) will not be available as recordings.
The course will not include live online events including group discussions/tutorials.
Attendance on campus is required for the test and the exam.
The activities for the course are scheduled as a standard weekly timetable.

Learning Resources

Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).

Please remember that the recording of any class on a personal device requires the permission of the instructor.

Recommended Readings:
  • Artificial Intelligence: A Modern Approach, by Stuart J. Russell and Peter Norvig. Fourth Edition.
  • Logic Programming with Prolog, by Max Bramer.
  • Prolog Programming for Artificial Intelligence, 4th edition, by Ivan Bratko

Student Feedback

During the course Class Representatives in each class can take feedback to the staff responsible for the course and staff-student consultative committees.

At the end of the course students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback.

Your feedback helps to improve the course and its delivery for all students.

No change is needed

Academic Integrity

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the internet. A student's assessed work may be reviewed against online source material using computerised detection mechanisms.

Class Representatives

Class representatives are students tasked with representing student issues to departments, faculties, and the wider university. If you have a complaint about this course, please contact your class rep who will know how to raise it in the right channels. See your departmental noticeboard for contact details for your class reps.


The content and delivery of content in this course are protected by copyright. Material belonging to others may have been used in this course and copied by and solely for the educational purposes of the University under license.

You may copy the course content for the purposes of private study or research, but you may not upload onto any third party site, make a further copy or sell, alter or further reproduce or distribute any part of the course content to another person.

Inclusive Learning

All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.

Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website

Special Circumstances

If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.

If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page

This should be done as soon as possible and no later than seven days after the affected test or exam date.

Learning Continuity

In the event of an unexpected disruption, we undertake to maintain the continuity and standard of teaching and learning in all your courses throughout the year. If there are unexpected disruptions the University has contingency plans to ensure that access to your course continues and course assessment continues to meet the principles of the University’s assessment policy. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator/director, and if disruption occurs you should refer to the university website for information about how to proceed.

The delivery mode may change depending on COVID restrictions. Any changes will be communicated through Canvas.

Student Charter and Responsibilities

The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter


Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.

In this course students may be asked to submit coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. In exceptional circumstances changes to elements of this course may be necessary at short notice. Students enrolled in this course will be informed of any such changes and the reasons for them, as soon as possible, through Canvas.

Published on 31/10/2022 09:29 a.m.