IGB383 AI for Games


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Unit Outline: Semester 2 2025, Gardens Point, Internal

Unit code:IGB383
Credit points:12
Pre-requisite:CAB301 and IGB283
Equivalent:INB383
Coordinator:David Conroy | david.conroy@qut.edu.au
Disclaimer - Offer of some units is subject to viability, and information in these Unit Outlines is subject to change prior to commencement of the teaching period.

Overview

The core concepts, principles and practices of designing and implementing Artificial Intelligence (AI) within computer games are explored and implemented within this unit. The introduction of modern theoretical models as well as commercial examples provides a foundational understanding of both the history and future of Game AI. This is particularly important when designing natural and/or humanistic behavioural effects of Non-Player Characters (NPC). Knowledge and skills developed during this unit adhere directly to modern Game and AI development and are required of industry practitioners today. You will develop an understanding of the field and develop expertise in addressing modern Game AI algorithms and problems.

Learning Outcomes

On successful completion of this unit you will be able to:

  1. Identify the features of different computer games that constitute game AI
  2. Communicate about a set of algorithms in game AI using a formal pseudo code language
  3. Identify well-structured solutions for a wide range of AI problems in computer games
  4. Manipulate the behaviours of NPC in a 3D scripting environment

Content

This unit will cover: types of game play, game theory, scripted and automated NPC behaviours, graph searching and path-finding, simulated reality (flocking, flight, hunting), finite state automata, decision trees, logic and various optimization techniques.

Learning Approaches

In this unit, students will combine theory with practical experience to develop their knowledge of different kinds of NPC agents and their applications in games. Contact hours each week will consist of:

  • A two-hour lecture that will explore different AI problems in computer games and will show solutions through illustrated and worked examples.
  • A one-hour workshop where students can work through exercises and get hands-on experience with scripting.

Feedback on Learning and Assessment

You can obtain feedback on your progress throughout the unit through the following mechanisms:

  • Teaching staff will provide feedback during the workshop/lab sessions.
  • Solutions to workshop exercises will be released on the Canvas site.
  • Before the final examinations, sample questions will be made available to help you prepare and to provide you with feedback on you progress.
  • Teaching staff and the unit coordinator will be available during their consultation times or via email to provide individual assistance and feedback on your progress.

Assessment

Overview

All assessment contributes to your grade.

Unit Grading Scheme

7- point scale

Assessment Tasks

Assessment: Project (applied)

This assignment explores the application of reactive agents based on finite state machines to NPC in interactive environments such as games and applications of a particular kind of path-finding solution.

This assignment is eligible for the 48-hour late submission period and assignment extensions. 

Weight: 30
Individual/Group: Individual
Due (indicative): Week 5-6
Related Unit learning outcomes: 1, 2

Assessment: Project (applied)

This assignment explores the application of knowledge-based agents that can solve complex real-world resource collection and planning problems alongside suitable boid and flocking solutions.

This assignment is eligible for the 48-hour late submission period and assignment extensions. 

Weight: 40
Individual/Group: Individual
Due (indicative): Week 10-11
Related Unit learning outcomes: 2, 3, 4

Assessment: Project (applied)

This assignment explores the application of decision tree theory in a working game context to create complex, player-like agents capable of realistic and competitive gameplay. Students will be able to test their developed models against other human players and AI agents.

This assignment is eligible for the 48-hour late submission period and assignment extensions. 

Weight: 30
Individual/Group: Individual
Due (indicative): Week 13-14
Related Unit learning outcomes: 1, 2, 3, 4

Academic Integrity

Academic integrity is a commitment to undertaking academic work and assessment in a manner that is ethical, fair, honest, respectful and accountable.

The Academic Integrity Policy sets out the range of conduct that can be a failure to maintain the standards of academic integrity. This includes, cheating in exams, plagiarism, self-plagiarism, collusion and contract cheating. It also includes providing fraudulent or altered documentation in support of an academic concession application, for example an assignment extension or a deferred exam.

You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.

Breaching QUT’s Academic Integrity Policy or engaging in conduct that may defeat or compromise the purpose of assessment can lead to a finding of student misconduct (Code of Conduct – Student) and result in the imposition of penalties under the Management of Student Misconduct Policy, ranging from a grade reduction to exclusion from QUT.

Resources

No additional costs are associated with the requirements for this unit.

The Canvas site will provide:
  • Lecture notes
  • Workshop documents and resources.
  • Assessment details, specifications and marking criteria.
  • Supporting documentation and references.

    There is no prescribed textbook for this unit, but the following books are recommended for general reading and reference:

    Russell, Stuart J.; Norvig, Peter (2003), Artificial Intelligence: A Modern Approach (2nd ed.), Upper Saddle River, NJ: Prentice Hall

    Rabin, S. 2002 AI Game Programming Wisdom. Charles River Media, Inc.
    Rabin, S. 2004 AI Game Programming Wisdom 2 (Game Development Series). Charles River Media, Inc.
    Rabin, S. 2006 AI Game Programming Wisdom 3 (Game Development Series). Charles River Media, Inc.
    Rabin, S. 2008 AI Game Programming Wisdom 4 (Game Development Series). Charles River Media, Inc.

Risk Assessment Statement

There are no foreseeable health or safety risks associated with this unit.

Course Learning Outcomes

This unit is designed to support your development of the following course/study area learning outcomes.

IN05 Bachelor of Games and Interactive Environments

  1. Demonstrate broad knowledge of games and interactive environments principles and theory, with an in-depth knowledge of one games-related discipline.
    Relates to: ULO1, Project (applied), Project (applied), Project (applied)
  2. Apply creativity, critical thinking and problem-solving skills to generate solutions to design challenges.
    Relates to: ULO2, ULO3, Project (applied), Project (applied), Project (applied)
  3. Create engaging and meaningful games experiences for specific target audiences in partnership with diverse industry and community stakeholders using industry-relevant software and technologies..
    Relates to: ULO4, Project (applied), Project (applied)