COMPSCI 761 : Advanced Topics in Artificial Intelligence


2021 Semester Two (1215) (15 POINTS)

Course Prescription

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. Recommended preparation: COMPSCI 220, 225.

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 five 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 un-informed and informed search strategies as well as heuristics, which are "rules of thumbs" 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 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 include developing a representation of the state of the world and making predictions about how decision will affect the world states. In this course, we will examine important planning algorithms in artificial intelligence.

Module 4: 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: Approval of the Academic Head or nominee 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)


Assessment Type Percentage Classification
Assignments 30% Individual Coursework
Test 10% Individual Test
Final Exam 45% Individual Examination
In-Class Quiz 15% Individual Test
Assessment Type Learning Outcome Addressed
1 2 3 4 5 6 7
Final Exam
In-Class Quiz


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

Key Topics

Adversarial search
Constraint satisfaction
Knowledge representation
Natural language processing

Special Requirements

There is an evening test.

You have to pass both the theory [the exam and test] and the practical [i.e., assignments] components to pass this course. The exam is worth 60% of the total marks, mid-term test is worth 10% of the total marks, 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 36 hours of lectures,  48 hours of reading and thinking about the content and 40 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

Recommended textbook:

Artificial Intelligence: A Modern Approach, by Stuart J. Russell and Peter Norvig. Fourth Edition.

Logic Programming with Prolog, by Max Bramer.

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.

Digital 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.

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.


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 your assessment is fair, and not compromised. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator, and if disruption occurs you should refer to the University Website for information about how to proceed.

Level 1:  Delivered normally as specified in delivery mode
Level 2: You will not be required to attend in person.  All teaching and assessment will have a remote option.  The following activities will also have an on campus / in person option: Lectures
Level 3 / 4: All teaching activities and assessments are delivered remotely

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 you may be asked to submit your 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. The final decision on the completion mode for a test or examination, and remote invigilation arrangements where applicable, will be advised to students at least 10 days prior to the scheduled date of the assessment, or in the case of an examination when the examination timetable is published.

Published on 08/07/2021 03:28 p.m.