The Art of illusion
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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.
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Rainbow 6 Siege is a tactical FPS released in 2015 that focuses on close quarter combat between attackers and defenders. It uses destructible environments to change sight lines and strategies during matches. Operators have unique abilities to aid their team on attack or defense. The game emphasizes audio cues and communication between players. Graphics were improved in 2017 to enhance character models and lighting. It requires a mid-range gaming PC or current gen consoles to run well.
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The Art of illusion
- 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
- 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
- 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
- 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
- 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
- 31. What do we want? Simulate complex strategies. At a low cost (either in time or budget).
- 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