Approach E
The motivation for this approach was to produce an agent which uses behaviour trees in order to act and make decisions within the Pacman contest environment.
Behaviour trees are trees containing hierarchical nodes which control the flow of decision making of agents. These trees are defined as directed acyclic graphs with internal nodes corresponding with events/stimuli, and external nodes corresponding with behaviours (in contrast with hierarchical state machines whereby stimuli leads to states, rather than behaviours). The image below illustrates a simplistic behaviour tree for a two-armed robot (By Aliekor at English Wikipedia, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=39804218):
An agent utilising a behaviour tree would be able smart and informed decisions which have been pre-programmed by the agent-designer. Possibilities for evolving behaviour would stem from manual supervision of matches in an effort to assess which stimuli should trigger certain behaviours.
- Behaviour trees can be programmed once and then can be used right away, i.e. behaviour tree implementation requires no pre-match training.
- Decisions can be informed by human intervention, effectively bestowing outsider knowledge/strategies onto the agent.
- Behaviour trees are so extremely effective in controlling agents within gaming environments (i.e. NPCs), that their usage is mainstream within the gaming AI community at large.
- Behaviour trees can be used in conjunction with AI decision making approaches (e.g. if A, then perform Monte Carlo Tree Search)
- Behaviour trees require domain-knowledge to be hard-coded by the agent-designer, i.e. the agent is only as good as the agent-designer.
- Behaviour trees are really an instance of AI decision-making.
This team understood that this is not strictly an AI method as defined within the project specifications, however given its usage within Agent 3, it required mentioning from an approach-perspective.