Elements and Performance Criteria

The range statement relates to the unit of competency as a whole. It allows for different work environments and situations that may affect performance. Bold italicised wording, if used in the performance criteria, is detailed below. Essential operating conditions that may be present with training and assessment (depending on the work situation, needs of the candidate, accessibility of the item, and local industry and regional contexts) may also be included.

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.

The evidence guide provides advice on assessment and must be read in conjunction with the performance criteria, required skills and knowledge, range statement and the Assessment Guidelines for the Training Package.

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.