This document discusses various techniques for using artificial intelligence in game development to create realistic behaviors and dynamic challenges for players. It outlines 8 tricks or techniques including using graphs and pathfinding to create enemy hordes, decision trees to simulate agents with free will, state machines to model interactions between multiple agents, genetic algorithms and neural networks for procedural content generation, and directors and rubberbanding systems to balance difficulty. The goal is to use formal AI techniques to enhance the player experience within the constraints of real-time games.

1. THE ART OF
ILLUSION
{ Jorge Palacios, Christian Chomiak }
AI tricks for game development

2. AGENDA

3. INTRODUCTION

4. What is AI?
In what contexts?

5. Challenges of video game AI
 Shared physical resources
 Limited execution time (realtime)
 Player experience

6. USING FORMAL AI
TECHNIQUES

7. Trick #1:
ENEMY HOARDS

8. What do we want?
To create obstacles and dynamic challenges.
Have NPCs that move interestingly enough.
 Increase the challenge and the tension.

9. It can be modeled with…
GRAPHS, PATHFINDING & FLOCKING

10. BSP & PATHFINDING
BSP Pathfinding

11. PATHFINDING
Optimisation Smoothening

12. Trick #2:
NPCs with freewill

13. What do we want?
 Expand upon the previously defined
experience.
 Evaluate the possible actions of an actor
according to its current state, needs and the
surrounding..
 Simulate rational beings.

14. It can be modeled using…
DECISIONTREES

15. A tree is…
Energy > 1
Nil
NO
Enemy
near?
YES
WALK
NO
ATTACK
YES

16. Trick #3:
BOXING RING

17. What do we want?
 Have multiple actors interacting with the
player without overwhelming it.
 Balancing according to type and difficulty of
the enemies.

18. Can be modeled using…
STATE MACHINES

19. Trick #4:
CONTENT GENERATION

20. What do we want?
 Create new content dynamically .
 Procedural content generation
 To créate smart and flexible tolos for the
developers.

21. Can be modeled using…
GENETIC ALGORITHMS AND
NEURAL NETWORKS

22. Trick #5:
BALANCE FLOW & DIFFICULTY (part 1)

23. What do we want?
 Balance the difficulty curve according to the
capabilities of the player.
Challenge seasoned players.
 Regulate the general flow of the experience.
 Increase replayability.
 Minimise First-Order Optimal Strategies (FOOS).

24. Can be modeled using…
DIRECTORS

25. Interest curve

26. SMOKE & MIRROS
(*wink* hardcoding *wink*)

27. Trick #6:
BALANCE DIFFICULTY (part 2)

28. What do we want?
The same thing as before but cheaply.
Increase or decrease the difficulty of the
game by giving or limiting (unfair)
advantages to the NPCs.

29. Can be modeled using a…
RUBBERBAND SYSTEM

30. Trick #7:
COMPLEX STRATEGIES

31. What do we want?
 Simulate complex strategies.
 At a low cost (either in time or budget).

32. SEEK PREDICT
AMBUSH WANDER

33. Trick #8:
SPACE-AWARE ENEMIES

34. What do we want?
 A deeper interaction between NPCs and the
world.
 The level itself is key to the gameplay.
 Increased realism

35. Can be modeled by…
SIMULATINGTHE SENSES

36. CONCLUSIONS

37. ANY QUESTIONS?

38. THANKYOU
VERY MUCH!
Jorge Palacios – @pctroll – jorge.palacios.co
Christian Chomiak – @cchomiakm – bonusdisc.com