COMPSCI 767 : Intelligent Software Agents

Science

2025 Semester One (1253) (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.

Course Overview

This course will look at intelligent software agents both in multi-agent settings and with respect to 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. We will also be considering multi-agent systems composed of humans, AI agents and human organisations, and methods for developing effective joint behaviours.

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 explore reducing the size of these search spaces.

The goal of this course is to understand techniques, applications and implications of intelligent agents and multi-agent and mixed multi-agent systems systems; a major technique toward this goal is to improve your ability to quickly read and evaluate complex technical papers, integrating and communicating the results.

Course Requirements

Prerequisite: COMPSCI 367 or 761, or COMPSCI 713 and 714

Capabilities Developed in this Course

Capability 3: Knowledge and Practice
Capability 4: Critical Thinking
Capability 5: Solution Seeking
Capability 6: Communication
Capability 7: Collaboration
Capability 8: Ethics and Professionalism

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 3)
  2. Read and critically evaluate technical papers (Capability 3 and 4)
  3. Explain important challenges of MAS such as coordination and cooperation (Capability 3, 5 and 6)
  4. Apply techniques from deep learning in the context of agent-based systems (Capability 3, 5 and 7)
  5. Paraphrase and summarise MAS scenarios using the language of game theory (Capability 3 and 6)
  6. Understand and critically evaluate current research papers in Agent Systems, Deep Learning and Heuristic Search (Capability 3, 6 and 7)
  7. Understand and critically evaluate the tradeoffs involved in using current agent systems techniques to reduce the problem space sizes (Capability 4, 5 and 8)
  8. Create domain-specific prototypes of agent-based systems (Capability 3, 5 and 7)
  9. Understand and critically evaluate the tradeoffs involved in creating agent, multi-agent,a nd mixed human agent systems (Capability 3, 4, 6 and 8)
  10. Present problems in PDDL, a standard language for describing explict symbolic planning problems (Capability 3 and 6)

Assessments

Assessment Type Percentage Classification
5 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
5 Intelligent Agents Quizes
2 Intelligent Agents Tests
Intelligent Agents Assignment
2 MAS Assignments
MAS Test
5 Weekly Intelligent Agents Discussions

Special Requirements

N/A

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.  These workload expectations are estimates only, and may vary.

Delivery Mode

Campus Experience

Attendance is expected at scheduled activities to receive credit for components of the course.
Lectures will be available as recordings, but will also involve in-class discussion. Other learning activities including  will not be available as recordings.
The course may include live online events including group discussions.
Attendance on campus is required for tests.
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.

Suggested:

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.

This course is being taught by new instructors compared to the previous year, and will include both feedback-driven and research-context-driven changes,

Other Information

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

Academic Integrity

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework, tests and examinations 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. A student's assessed work may be reviewed against electronic source material using computerised detection mechanisms. Upon reasonable request, students may be required to provide an electronic version of their work for computerised review.

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.

Copyright

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 http://disability.auckland.ac.nz

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 https://www.auckland.ac.nz/en/students/academic-information/exams-and-final-results/during-exams/aegrotat-and-compassionate-consideration.html.

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

The recommended text for the FoS DCOs is included below. In the unlikely event of a medical occurrance, this will be used as guidance. 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 https://www.auckland.ac.nz/en/students/forms-policies-and-guidelines/student-policies-and-guidelines/student-charter.html.

Disclaimer

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 08/11/2024 08:48 a.m.