COMPSCI 767 : Intelligent Software Agents


2021 Semester One (1213) (15 POINTS)

Course Prescription

An introduction to the design, implementation and use of intelligent software agents (e.g., knowbots, softbots etc). Reviews standard artificial intelligence problem-solving paradigms (e.g., planning and expert systems) and knowledge representation formalisms (e.g., logic and semantic nets). Surveys agent architectures and multi-agent frameworks. Recommended preparation: COMPSCI 367.

Course Overview

This course will look at intelligent software agents in multi-agent settings and as an individual intelligent software agent. 

A multi-agent system  (MAS) consists of multiple interacting agents who share the same environment. Such systems are useful for multi-party, complex, real-time problems with uncertainty that are often impossible or hard to model or solve using a single agent alone. Applications of multi-agent systems range from negotiation, cooperating robots, market and auction analysis, to security. A main theme in this field involves strategic agents where game theory is an important tool. We will be looking at the algorithmic and game-theoretic foundations of multi-agent systems in this course.

One of the core abilities of an intelligent agent is to be able to solve problems. Search is a general purpose technique for finding solutions to problems. However, these search spaces can be quite large and we need to be able to reduce the size of the search space in order to solve problems in a reasonable amount of time and space. We will be exploring several state of the art techniques for reducing the size of these search spaces.

The main goals of this course is two-fold.  One is to learn advanced material for both intelligent agents and for multi-agent systems.  The second is  to encourage you to quickly read and evaluate technical papers.

Course Requirements

Prerequisite: Approval of the Academic Head or nominee

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

Learning Outcomes

By the end of this course, students will be able to:
  1. Understand what a multi-agent system (MAS) is and when they are useful (Capability 1)
  2. Read and critically evaluate technical papers (Capability 2)
  3. Explain important challenges of MAS such as coordination and cooperation (Capability 3 and 4)
  4. Apply some well-known distributed optimization algorithms (Capability 1 and 3)
  5. Paraphrase and summarise MAS scenarios using the language of game theory (Capability 1, 3 and 4)
  6. Understand and critically evaluate current research papers in Heuristic Search (Capability 1, 2, 4 and 5)
  7. Understand and critically evaluate the tradeoffs involved in using current techniques to reduce the problem space sizes (Capability 1, 2, 3 and 5)
  8. Create domain-specific heuristics (Capability 2 and 3)
  9. Understand and critically evaluate the tradeoffs involved in creating heuristics (Capability 1, 2 and 3)
  10. Present problems in PDDL, the standard language for describing planning problems (Capability 1 and 3)


Assessment Type Percentage Classification
10 Intelligent Agents Quizes 10% Individual Test
2 Intelligent Agents Tests 20% Individual Test
Intelligent Agents Assignment 10% Individual Coursework
2 MAS Assignments 25% Individual Coursework
MAS Test 25% Individual Test
5 Weekly Intelligent Agents Discussions 10% Individual Coursework
Assessment Type Learning Outcome Addressed
1 2 3 4 5 6 7 8 9 10
10 Intelligent Agents Quizes
2 Intelligent Agents Tests
Intelligent Agents Assignment
2 MAS Assignments
MAS Test
5 Weekly Intelligent Agents Discussions

Special Requirements


Workload Expectations

This course is a standard 15 point course and students are expected to spend 10 hours per week.

For this course, you can expect 36 hours of lectures and 72 hours of reading and thinking and discussing the content and 12 hours of work on assignments and/or test preparation.

Delivery Mode

Campus Experience

Attendance is expected at scheduled activities to receive credit for components of the course.
Lectures will be available as recordings. Other learning activities including [seminars/tutorials/labs/studios] will [be available/not be available] as recordings.
The course will include live online events including group discussions.
Attendance on campus is requiredfor the test.
The activities for the course are scheduled as a standard weekly timetable.

Learning Resources


For Intelligent Software Agents: "Heuristic Search: Theory and Applications" by Sefan EdelKamp and Stefan Schroedl

For Multi-Agent Systems: "Reinforcement Learning: An Introduction" (Second Edition) by Richard Sutton and Andrew Barto 

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.

Other Information

To do well in this course will require you to read and to analyse conference papers  and then  discuss them in class.

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.

The recommended text for the FoS DCOs is included below. Please delete the text that does not apply (e.g., for components that are not relevant to your course). Note that permission is required for any on campus teaching at level 2, and there is no obligation to deliver any on-campus activity at level 2.

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, labs, tutorials, office hours, field trips, etc.]
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 26/01/2021 10:35 a.m.