CSCE 552 Fall 2012

1. CSCE 552 Fall 2012 AI By Jijun Tang

2. Homework 3 • List of AI techniques in games you have played; • Select one game and discuss how AI enhances its game play or how its AI can be improved • Due Nov 28th

3. Command Hierarchy • Strategy for dealing with decisions at different levels • From the general down to the foot soldier • Modeled after military hierarchies • General directs high-level strategy • Foot soldier concentrates on combat

4. Dead Reckoning • Method for predicting object’s future position based on current position, velocity and acceleration • Works well since movement is generally close to a straight line over short time periods • Can also give guidance to how far object could have moved • Example: shooting game to estimate the leading distance

5. Emergent Behavior • Behavior that wasn’t explicitly programmed • Emerges from the interaction of simpler behaviors or rules • Rules: seek food, avoid walls • Can result in unanticipated individual or group behavior

6. Flocking/Formation

7. Mapping Example

8. Level-of-Detail AI • Optimization technique like graphical LOD • Only perform AI computations if player will notice • For example • Only compute detailed paths for visible agents • Off-screen agents don’t think as often

9. Manager Task Assignment • Manager organizes cooperation between agents • Manager may be invisible in game • Avoids complicated negotiation and communication between agents • Manager identifies important tasks and assigns them to agents • For example, a coach in an AI football team

10. Example Amit [to Steve]: Hello, friend! Steve [nods to Bryan]: Welcome to CGDC. [Amit exits left.] Amit.turns_towards(Steve); Amit.walks_within(3); Amit.says_to(Steve, "Hello, friend!"); Amit.waits(1); Steve.turns_towards(Bryan); Steve.walks_within(5); Steve.nods_to(Bryan); Steve.waits(1); Steve.says_to(Bryan, "Welcome to CGDC."); Amit.waits(3); Amit.face_direction(DIR_LEFT); Amit.exits();

11. Example Player escapes in combat, pop Combat off, goes to search; if not find the player, pop Search off, goes to patrol, …

12. Example

13. Bayesian Networks • Performs humanlike reasoning when faced with uncertainty • Potential for modeling what an AI should know about the player • Alternative to cheating • RTS Example • AI can infer existence or nonexistence of player build units

14. Example

15. Bayesian Networks • Inferring unobserved variables • Parameter learning • Structure learning

16. Blackboard Architecture • Complex problem is posted on a shared communication space • Agents propose solutions • Solutions scored and selected • Continues until problem is solved • Alternatively, use concept to facilitate communication and cooperation

17. Decision Tree Learning • Constructs a decision tree based on observed measurements from game world • Best known game use: Black & White • Creature would learn and form “opinions” • Learned what to eat in the world based on feedback from the player and world

18. Filtered Randomness • Filters randomness so that it appears random to players over short term • Removes undesirable events • Like coin coming up heads 8 times in a row • Statistical randomness is largely preserved without gross peculiarities • Example: • In an FPS, opponents should randomly spawn from different locations (and never spawn from the same location more than 2 times in a row).

19. Genetic Algorithms • Technique for search and optimization that uses evolutionary principles • Good at finding a solution in complex or poorly understood search spaces • Typically done offline before game ships • Example: • Game may have many settings for the AI, but interaction between settings makes it hard to find an optimal combination

20. Flowchat

21. N-Gram Statistical Prediction • Technique to predict next value in a sequence • In the sequence 18181810181, it would predict 8 as being the next value • Example • In street fighting game, player just did Low Kick followed by Low Punch • Predict their next move and expect it

22. Neural Networks • Complex non-linear functions that relate one or more inputs to an output • Must be trained with numerous examples • Training is computationally expensive making them unsuited for in-game learning • Training can take place before game ships • Once fixed, extremely cheap to compute

23. Example

24. Planning • Planning is a search to find a series of actions that change the current world state into a desired world state • Increasingly desirable as game worlds become more rich and complex • Requires • Good planning algorithm • Good world representation • Appropriate set of actions

25. Player Modeling • Build a profile of the player’s behavior • Continuously refine during gameplay • Accumulate statistics and events • Player model then used to adapt the AI • Make the game easier: player is not good at handling some weapons, then avoid • Make the game harder: player is not good at handling some weapons, exploit this weakness

26. Production (Expert) Systems • Formal rule-based system • Database of rules • Database of facts • Inference engine to decide which rules trigger – resolves conflicts between rules • Example • Soar used experiment with Quake 2 bots • Upwards of 800 rules for competent opponent

27. Reinforcement Learning • Machine learning technique • Discovers solutions through trial and error • Must reward and punish at appropriate times • Can solve difficult or complex problems like physical control problems • Useful when AI’s effects are uncertain or delayed

28. Reputation System • Models player’s reputation within the game world • Agents learn new facts by watching player or from gossip from other agents • Based on what an agent knows • Might be friendly toward player • Might be hostile toward player • Affords new gameplay opportunities • “Play nice OR make sure there are no witnesses”

29. Smart Terrain • Put intelligence into inanimate objects • Agent asks object how to use it: how to open the door, how to set clock, etc • Agents can use objects for which they weren’t originally programmed for • Allows for expansion packs or user created objects, like in The Sims • Enlightened by Affordance Theory • Objects by their very design afford a very specific type of interaction

30. Speech Recognition • Players can speak into microphone to control some aspect of gameplay • Limited recognition means only simple commands possible • Problems with different accents, different genders, different ages (child vs adult)

31. Text-to-Speech • Turns ordinary text into synthesized speech • Cheaper than hiring voice actors • Quality of speech is still a problem • Not particularly natural sounding • Intonation problems • Algorithms not good at “voice acting”: the mouth needs to be animated based on the text • Large disc capacities make recording human voices not that big a problem • No need to resort to worse sounding solution

32. Weakness Modification Learning • General strategy to keep the AI from losing to the player in the same way every time • Two main steps 1. Record a key gameplay state that precedes a failure 2. Recognize that state in the future and change something about the AI behavior • AI might not win more often or act more intelligently, but won’t lose in the same way every time • Keeps “history from repeating itself”

33. Artificial Intelligence: Pathfinding

34. PathPlannerApp Demo

35. Representing the Search Space • Agents need to know where they can move • Search space should represent either • Clear routes that can be traversed • Or the entire walkable surface • Search space typically doesn’t represent: • Small obstacles or moving objects • Most common search space representations: • Grids • Waypoint graphs • Navigation meshes

36. Grids • 2D grids – intuitive world representation • Works well for many games including some 3D games such as Warcraft III • Each cell is flagged • Passable or impassable • Each object in the world can occupy one or more cells

37. Characteristics of Grids • Fast look-up • Easy access to neighboring cells • Complete representation of the level

38. Waypoint Graph • A waypoint graph specifies lines/routes that are “safe” for traversing • Each line (or link) connects exactly two waypoints

39. Characteristicsof Waypoint Graphs • Waypoint node can be connected to any number of other waypoint nodes • Waypoint graph can easily represent arbitrary 3D levels • Can incorporate auxiliary information • Such as ladders and jump pads • Radius of the path

40. Navigation Meshes • Combination of grids and waypoint graphs • Every node of a navigation mesh represents a convex polygon (or area) • As opposed to a single position in a waypoint node • Advantage of convex polygon • Any two points inside can be connected without crossing an edge of the polygon • Navigation mesh can be thought of as a walkable surface

41. Navigation Meshes (continued)

42. Computational Geometry • CGAL (Computational Geometry Algorithm Library) • Find the closest phone • Find the route from point A to B • Convex hull

43. Example—No Rotation

44. Space Split

45. Resulted Path

46. Improvement

47. Example 2—With Rotation

48. Example 3—Visibility Graph

49. Random Trace • Simple algorithm • Agent moves towards goal • If goal reached, then done • If obstacle • Trace around the obstacle clockwise or counter-clockwise (pick randomly) until free path towards goal • Repeat procedure until goal reached

50. Random Trace (continued) • How will Random Trace do on the following maps?