Next-Generation Game AI

1. Next-Generation Game AI CS196-2: Innovating Game Development Ryan Houlette – Lead AI Software Engineer Stottler Henke Associates houlette@stottlerhenke.com 617-616-1293 or, “We’ve figured out shooting. Now what do we do?”

2. Outline • Who am I? • State of the art • Future directions • Questions • Tools

3. Who am I? • Lead AI Software Engineer at Stottler Henke Associates since 1998 • AI consulting firm • commercial & government clients (NASA & DoD) • Architect of SimBionic game AI middleware • Author & section editor in AI Game Programming Wisdom series • Gamer since about 1984

4. State of the Art • In a word: disappointing. • Only a fraction of today’s games have good AI • Even then AI rarely matches the quality of the graphics and sound (and increasingly, physics) • Many games have merely passable or sub par AI • unrealistic behavior • not challenging or unfairly difficult • boring, predictable, or unresponsive • just plain stupid • Why is this?

5. A Game AI Programmer’s Life is Hard • Developing game AI is hard • one of the most complex parts of gameplay • demands extensive tuning and balancing • requires a mostly-working game world to test • developers often start from scratch • Tension between character design and character autonomy • Currently game AI is written in code or scripting language • game designers are often non-programmers • separates the design of the AI from the implementation • translation barrier • long design-code-test cycle

6. …and AI is Hard • Many human-level capabilities cannot currently be easily replicated by computers • vision • natural language • movement • common sense • learning • AI algorithms tend to be computationally expensive • so are nice graphics and physics • AI ends up getting a small slice of the CPU (about 10%)

7. Current Technology • Code • C++, Lua, Python, UnrealScript, etc…. • Finite state machines • A* for pathfinding • Annotated worlds (“smart environments”) • A few specialized algorithms • influence maps for RTS

8. There is Hope! • A few ambitious developers • e.g. F.E.A.R. (Monolith), Black & White (Lionhead) • More contact between academia & industry • AIIDE conference • lots of game-development curricula • Dawning recognition of AI as “next big thing” after graphics, physics • Faster machines; offloading of graphics, physics chores on specialized hardware

9. Directions for Next-Gen Game AI #0: Eliminate glaring stupidity • instantly breaks the suspension of disbelief and ruins immersion • usual culprits: abysmal pathfinding, unresponsiveness, insensitivity to context, repetitive behavior • Much of this is due to brittleness of current game AI

10. Getting to Zero • Overcoming NIH syndrome • Standard algorithms • AI Game Programming Wisdom series, Gamasutra, Game Developer Magazine, etc. • Middleware • Open-source packages • Recognizing the importance of good AI • requires dedicated developers w/specialized expertise • needs to be included in pre-production and tool-building • More open-ended games are forcing more open-ended AI

11. Direction #1 • Increase behavioral verisimilitude • to complement increasingly realistic character models • more realistic gestures, gaze, facial expressions, movement • can be done manually by animators, but content creation is a huge and growing cost in game development • AAA title cost jumping from $2-10M for Xbox/PS2 to $10-30M for Xbox360/PS3 • “subtle AI” needed to imbue characters with the appearance of emotion, attention, intention • caveat: aim is the illusion of life, not the real thing

12. The Uncanny Valley • Described by Masahiro Mori in 1970 • Humans are more sensitive to subtle behavioral miscues in human-like characters • As characters look more realistic, they run the risk of becoming increasingly unconvincing • Subtle AI needs to model and capture these nuances to avoid the valley GFDL Karl MacDorman, Wikipedia

13. Example: Half-Life 2 • Valve pushed the state of the art in facial animation, gestures • Excellent series of articles on gaze in Game Developer Magazine, Aug-Sep 05 • Based on study of real human behavior • Still lots of scripting behind the scenes

14. Half-Life 2

15. Direction #2 • Engage the player’s mind • games have their eyes, ears, and hands already • goal is not to build “super-AIs” that always win • relatively easy • not much fun for the player • better to build AI that puts up an epic fight and then loses excitingly • want the player to treat characters as thinking adversaries (or allies!) • encourages emotional engagement • allows the player to reason about and outsmart NPCs

16. Tools of Engagement (1) • Goal-based planning • gives AI explicit goals that the player can deduce • enables AI to choose among multiple means of achieving the same end • adds variety, reduces brittleness • used in F.E.A.R. • must be carefully constrained to avoid search combinatorics

17. Tools of Engagement (2) • Team/social intelligence • characters are largely unaware of one another • ability to work as a team to achieve shared goals creates a challenge for the player • both player-visible and –invisible inter-NPC communication are useful tools • next level: characters that have a model of other NPCs’ goals and intentions • difficulties: joint planning, synchronization

18. Tools of Engagement (3) • Emotions and personalities • NPCs who have distinct personalities are easier for the player to anthropomorphize • not “just another guard” • NPCs whose behavior is affected by their emotional state may require more subtlety from the player • Even relatively simple models can be effective • Can be tied to team/social behavior

19. Tools of Engagement (4) • Adaptive behavior • characters who learn and exploit player’s habits, strengths, weaknesses • can be used to dynamically adjust difficulty to keep game constantly engaging • more sophisticated than simply scaling the number or accuracy of enemies • smart advisors/teammates that help out when player is stuck • in-game vs between-game learning • learning can be costly • for some genres (e.g. RTS), between-game learning is appropriate • ranges from simple statistical approaches to more complex induction/pattern recognition

20. Direction #3 • Enable new kinds of gameplay • AI is intimately tied to game design, even more closely than physics • game genres have been limited by the capabilities of game AI • look at all of the movie/book genres yet to be tapped by games! • demands more than incremental improvements to AI

21. Exploring New Frontiers (1) • Dialogue-capable NPCs • basically shunned in games since Infocom • speech recognition is tough • parsing is tough • understanding is really tough • speech generation is not nearly as good as voice actors • the potential rewards are huge • non-combat-based games • richer stories • more natural, open-ended interactions with characters • new modalities for player interaction

22. Exploring New Frontiers (2) • Virtual stagecraft • NPCs who know how to act • games, like movies, already make heavy use of selective omission • want to emphasize the important and interesting stuff • Virtual directors who manage the pacing and intensity of the story development • Virtual storytellers who build customized storylines for the player and adapt them dynamically

23. Exploring New Frontiers (3) • Persistent worlds • current MMOGs have barely scratched the surface here • e.g. player surrogates • take over for the player when he/she is offline (in an RTS-style game) • learn and mimic the player’s playing style • integrating NPCs socially into communities • rumor, history, reputation, memory

24. Direction #4 • Develop better AI tools and standards • content creation is the single biggest challenge facing the industry today • AI tools have traditionally been home-grown (if they exist at all) • better tools are needed if AI is to improve • usable by designers • reusable across projects (incremental improvement) • more data-driven • the analogue of a “higher-level language” for AI is desperately needed • permit designers to work at the conceptual level rather than micromanaging every footstep and eyeblink

25. Summary • Current state of game AI: C- • Plenty of room for innovation: • ransacking academic AI for algorithms • exploring interplay of AI and game design • building better tools • AI will soon be a major selling point for games on a par with graphics & physics • How about an “AI Game Jam”?

26. Questions?

27. Tools for Class Projects • SimBionic • game AI middleware that allows visual authoring of NPC behaviors • integrates with Java/C++ game engine • SimVentive • entire visual platform for developing games and simulations

28. SimBionic Overview

29. SimBionic Demo

30. SimVentive Overview Sim Authoring Tool Sim Execution Engine Player Author Sim Specification • Authoring • Create or modify • Test and debug Standardized GUI Scenario Library Playing • Standard Windows PC • New games can be easily distributed

31. SimVentive Demo