Unit of competency details

Release 

Comments 

Release 1

This Unit first released with ICA11 Information and Communications Technology Training Package version 1.0

Element 

Performance Criteria 

Elements describe the essential outcomes of a unit of competency.

Performance criteria describe the performance needed to demonstrate achievement of the element. Where bold italicised text is used, further information is detailed in the required skills and knowledge section and the range statement. Assessment of performance is to be consistent with the evidence guide.

1. Conduct research on artificial intelligence (AI) strategies

1.1 Describe the terminology  involved in AI as it relates to games and the game industry

1.2 Identify and analyse the range of AI pathfinding  strategies, including appropriate genre and environments  and how they influence the design and development

1.3 Discuss AI strategies and ideas and collaborate as required with relevant personnel to ensure contribution of a range of ideas and creative solutions to fulfil the requirements of the brief

1.4 Organise research and findings for use by relevant personnel throughout the development process, updating as required

2. Design AI strategy for games

2.1 Generate a range of possible goals and actions  and other factors  in the design of an AI non-player character (NPC)

2.2 Select the AI strategies for NPCs for the game design which are technically feasible, respond to the brief and provide creative solutions to all design issues

2.3 Continuously reflect on and assess the AI strategies for implications on budget, time line, technical feasibility and suitability for the brief

3. Implement AI strategy

3.1 Implement a pathfinding algorithm  in a game

3.2 Implement an NPC AI strategy  in a game

4. Evaluate game, based on NPC AI

4.1 Review the game design and AI strategies for its fulfilment of the design brief

4.2 Discuss and confirm additional requirements or modifications to the game design and undertake any necessary amendments

Overview of assessment 

Critical aspects for assessment and evidence required to demonstrate competency in this unit 

Evidence of the ability to:

• demonstrate an understanding of a broad knowledge base of AI strategies
• design and implement AI strategies in a game.

Context of and specific resources for assessment 

Assessment must ensure access to:

• research tools, such as the internet
• pathfinding libraries, algorithms or codes
• development tools to implement AI strategies
• appropriate learning and assessment support when required
• modified equipment for people with special needs.

Method of assessment 

A range of assessment methods should be used to assess practical skills and knowledge. The following examples are appropriate for this unit:

• direct observation of candidate discussing and designing AI strategies
• verbal or written questioning to assess candidate’s knowledge of AI strategies
• development of a game involving AI strategies
• review of candidate’s documented game design.

Guidance information for assessment 

Holistic assessment with other units relevant to the industry sector, workplace and job role is recommended, where appropriate.

Assessment processes and techniques must be culturally appropriate, and suitable to the communication skill level, language, literacy and numeracy capacity of the candidate and the work being performed.

Indigenous people and other people from a non-English speaking background may need additional support.

In cases where practical assessment is used it should be combined with targeted questioning to assess required knowledge.

Terminology  may include:

• agent systems
• AI
• decision trees
• flocking
• fuzzy logic
• obstacle avoidance
• state machines
• steering behaviours.

Pathfinding  may include:

• A*
• Dijkstra
• grid-graph representation
• waypoints
• navigation mesh
• heuristics
• path smoothing.

Genre and environments  may include:

• 2-D or 3-D
• first person shooter
• outdoors or indoors
• strategy game.

Range of possible goals and actions  may include:

• AI states such as hunt, run and search
• decision making
• who to attack based on game data.

Other factors  may include:

• cost versus benefit to implement
• emotional responses
• realism
• suit the design brief, genre and target audience.

Implementing a path-finding algorithm  may include:

• code the algorithm
• using a library to implement pathfinding
• using graphical tools.

NPC AI strategy  may include:

• flock
• game balancing - help the losing player by attacking the leading player to keep everyone in the game
• hunt, chase and flee
• patrol
• search for object or player.