Year
2021Credit points
10Campus offering
No unit offerings are currently available for this unitPrerequisites
ITEC205 Game Fundamentals and Augmented Reality
Unit rationale, description and aim
Simulation models and environments are important for training and decision making such as in medicine, emergency response, manufacturing and defence. This unit aims to develop knowledge and skills in using a game framework for developing AI-driven applications. It integrates the knowledge and skills gained in the ‘Game Fundamentals and Augmented Reality’ unit and other programming units to develop advanced game applications. This unit extends the essential concepts, techniques, software tools and modern frameworks required for designing, creating and testing interactive game-simulated environments introduced in the prerequisite unit. It covers both theoretical and practical knowledge on a wide range of modern technologies used for designing and creating game applications, including programming for: the A* (Dijkstra's) Search Algorithm using the PAC-MAN game (Python); Synthetic Data Processing and Object Recognition using Unity3D for ML (C#, Python); and, Robotic Navigation Simulation in Unity3D (C#, C++, Python). This unit will specially address human dignity where the object detection in images involves people of different races when developing game-simulated environments for training professionals.
Learning outcomes
To successfully complete this unit you will be able to demonstrate you have achieved the learning outcomes (LO) detailed in the below table.
Each outcome is informed by a number of graduate capabilities (GC) to ensure your work in this, and every unit, is part of a larger goal of graduating from ACU with the attributes of insight, empathy, imagination and impact.
Explore the graduate capabilities.
On successful completion of this unit, students should be able to:
LO1 - Demonstrate working knowledge and understanding of how a game-simulated environment can be used to process data, including the associated technologies and concepts, application development frameworks and toolkits (GA4, GA5)
LO2 - Apply the current architectures, frameworks and toolkits to develop game-simulated environments suitable for real-world deployment (GA5, GA10)
LO3 - Critically evaluate design choices and investigate the consequences of key design decisions (GA5, GA8)
LO4 - Communicate effectively in writing, using technical language, with other IT professionals, reflecting on the technical issues of game-simulated environments and their impact on human dignity (GA3, GA5)
Graduate attributes
GA3 - apply ethical perspectives in informed decision making
GA4 - think critically and reflectively
GA5 - demonstrate values, knowledge, skills and attitudes appropriate to the discipline and/or profession
GA8 - locate, organise, analyse, synthesise and evaluate information
GA10 - utilise information and communication and other relevant technologies effectively.
Content
Topics covered:
- A* Search Algorithm using PAC-MAN game (Python)
- Synthetic Data Processing and Object Recognition in Unity3D for ML (C#, Python)
- Robotic Navigation Simulation (C#, C++, Python)
- Impact of game-simulated environments on human diginity
Learning and teaching strategy and rationale
This unit is offered in different modes. These are: “Attendance” mode, “Blended” mode and “Online” mode. This unit is offered in three modes to cater for the learning needs and preferences of a range of participants and maximise effective participation for isolated and/or marginalised groups.
Attendance Mode
In a weekly attendance mode, students will require face-to-face attendance in specific physical location/s. Students will have face-to-face interactions with lecturer(s) to further their achievement of the learning outcomes. This unit is structured with required upfront preparation before workshops, most students report that they spend an average of one hour preparing before the workshop and one or more hours after the workshop practicing and revising what was covered. The online learning platforms used in this unit provide multiple forms of preparatory and practice opportunities for you to prepare and revise.
Blended Mode
In a blended mode, students will require face-to-face attendance in blocks of time determined by the School. Students will have face-to-face interactions with lecturer(s) to further their achievement of the learning outcomes. This unit is structured with required upfront preparation before workshops. The online learning platforms used in this unit provide multiple forms of preparatory and practice opportunities for you to prepare and revise.
Online Mode
This unit uses an active learning approach to support students in the exploration of the essential knowledge associated with working with technology. Students can explore the essential knowledge underpinning technological advances and develop knowledge in a series of online interactive lessons and modules. Students are given the opportunity to attend facilitated synchronous online seminar classes with other students and participate in the construction and synthesis of knowledge, while developing their knowledge of working with technology. Students are required to participate in a series of online interactive workshops which include activities, knowledge checks, discussion and interactive sessions. This approach allows flexibility for students and facilitates learning and participation for students with a preference for virtual learning.
Students should anticipate undertaking 150 hours of study for this unit, including class attendance, readings, online forum participation and assessment preparation.
Assessment strategy and rationale
A range of assessment procedures will be used to meet the unit learning outcomes and develop graduate attributes consistent with University assessment requirements. The assessment strategy for this unit allows students to demonstrate a critical mindset in evaluating the impact of 2D and 3D simulated environment using game engine development and apply this knowledge to a variety of work situations. In order to develop this level of capability, the first assessment students will demonstrate their knowledge on developing a 2D pathfinding application in C# based on the popular pac man game; in the second assessment students will demonstrate their knowledge by applying the 2D pathfinding application to a 3D simulated pac man game in C# using the Unity3D game engine; in the third assessment students will demonstrate their knowledge by applying their knowledge of the Unity3D game engine to the development of synthetic data generation for ML object detection. The final assessment in the unit allows students to demonstrate the depth of their knowledge and understanding of work in a technology enhanced world through the development of a robotic simulated environment with object detection. The assessment tasks for this unit are designed for students to demonstrate their achievement of each learning outcome.
In order to pass this unit, students are required to
1. achieve an overall mark of at least 50%
2. attempt all three assessment tasks
Overview of assessments
| Brief Description of Kind and Purpose of Assessment Tasks | Weighting | Learning Outcomes | Graduate Attributes |
|---|---|---|---|
Assessment Task 1: 2D/3D Simulated Pathfinding Environment This assessment consists of a series of weekly lab exercises in the development of some basic 2D graphics elements in C# and a final 3D simulated environment based on the 2D pracs. This task requires students to demonstrate their theoretical knowledge and practical skills gained through the lab practical exercises. The 3D component consists of building a simulated 3D pathfinding environment based on the 2D practical exercises. Based on the popular pac man game in the Unity3D game engine, students are required to apply core concepts, such as: 3D modelling in the unity game engine editor, player control using C# scripts to access game elements, using collision detection to trigger events, and scene level transition. The feedback from this assessment will help students to apply the database concepts in the next assessment. Submission Type: Individual Assessment Method: Lab Practical Task Artefact: Working Code Examples | 25% | LO1 | GA4, GA5 |
Assessment Task 2: Synthetic Data Generation This assessment task consists of building a simulated 3D synthetic data environment for generating images suitable for ML training for object detection. Student teams are required to apply core concepts, such as: 3D modelling using an external modelling program, image manipulation using masks, creation of unity3D prefab objects, creating random object insertion and their transforms using a C# script, and implementation of the UnityML imageSynthesis library. The individual component of this project is exploring the results using Python’s Juptyer Notebooks. Submission Type: Group/Individual Assessment Method: Practical task Artefact: Working code example and Written report | 25% | LO2, LO3 | GA5, GA8, GA10 |
Assessment Task 3: Robotic navigation using Object Detection This assessment task consists of building a simulated 3D environment for navigating objects using object detection. Student teams are required to apply core concepts, such as: connecting to an external python server, insertion of 3D models and conversion to prefabs. The individual component of this project is a proposal of how incorporate a pathfinding algorithm for robotic navigation. What impact this has and the ethical considerations and human dignity in the context of ML should be discussed in the proposal. Submission Type: Group/Individual Assessment Method: Practical task Artefact: Working code example and Written report | 25% | LO4 | GA3, GA5 |
Representative texts and references
Borromeo, N. Alejandro., 2020, Hands-On Unity 2020 Game Development: Build, customize, and optimize professional games using Unity 2020 and C#, Packt Publishing.
Hocking, Joe., 2018, Unity in Action: Multiplatform game development in C#, Manning Publications.
Borshchev, Andrei., 2013, The Big Book of Simulation Modeling: Multimethod Modeling with AnyLogic, Anylogic North America.
Jiang, Rui., 2018, Game A.I. Made Easy: Designing Agents: With Unity3D Examples, Independently published.
Okita, Alex., 2019, Learning C# Programming with Unity 3D, Routledge.
Diehl, Stephan., 2013, Distributed Virtual Worlds: Foundations and Implementation Techniques Using VRML, Java, and CORBA, Springer.
Ree, Brian., Brusca, Victor., n.d., Video Game UDP Client/Server Design and Implementation: With a cross platform, networked, example game in Unity 3D, Kindle Edition only, Middlemind Games LLC
Linowes, Jonathan., 2015, Unity Virtual Reality Projects: Explore the world of Virtual Reality by building immersive and fun VR projects using Unity 3D, Packt Publishing.
Bourg, David., M., Bywalec, Bryan., 2013, Physics for Game Developers: Science, Math, And Code For Realistic Effects, O'Reilly Media.