Source metadata
- Type: Worked-example game AI textbook
- Author: Mat Buckland
- Published: 2005, Wordware
Key takeaways
- Starts with a practical math and physics primer, then moves into full AI systems rather than disconnected algorithms.
- Gives a strong worked-example treatment of finite state machines, including messaging between agents and reusable machine infrastructure.
- Covers classic steering behaviours, their combinations, spatial partitioning, and group behaviours in a way that is still highly teachable.
- Uses graphs and A* in a practical path-planning context rather than only as abstract search.
- Extends beyond reactive AI into goal-driven behaviour, scripting, and fuzzy logic.
Notable claims
- The book treats FSMs as a sturdy backbone that can later be combined with other techniques such as fuzzy logic and goal arbitration.
- Steering is presented as a distinct layer from locomotion, making behaviour reuse easier across different movement implementations.
- Goal arbitration is framed as desirability-driven, which makes it a useful stepping stone toward more formal utility and planning systems.
Relevance
Directly informs:
- ai-state-machine-pattern
- steering-behaviours
- pathfinding-algorithms
- goal-oriented-action-planning
- fuzzy-logic-for-games
- overview-artificial-intelligence-in-games
Open questions raised
- Which of Buckland’s worked examples are worth recreating in modern Unity/C# as teaching code?
- How should the wiki present the relationship between Buckland’s goal-driven behaviour and more formal GOAP?
Links
ai-state-machine-pattern · steering-behaviours · pathfinding-algorithms · goal-oriented-action-planning · fuzzy-logic-for-games · overview-artificial-intelligence-in-games