1 / 19

RECAP CSE 348 AI Game Programming

RECAP CSE 348 AI Game Programming. Héctor Muñoz-Avila. C. A. B. C. A. B. C. B. A. B. A. C. AI research. “AI” as game practitioners implemented it. B. A. C. B. A. B. C. C. B. A. B. A. C. A. C. A. A.

vea
Download Presentation

RECAP CSE 348 AI Game Programming

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. RECAPCSE 348 AI Game Programming Héctor Muñoz-Avila

  2. C A B C A B C B A B A C AI research “AI” as game practitioners implemented it B A C B A B C C B A B A C A C A A Our goal was to understand the connections and the misconceptions from both sides B C B C C B A A B C Course Goal projects

  3. Patrol • Preconditions: No Monster • Effects: patrolled • Fight • Preconditions: Monster in sight • Effects: No Monster Soldier Attack E,~D Planning Operators E Rifleman Machine Gunner Officer ~E Chase S,~E,~D S D Wander ~E,~S,~D E ~S D American German American German American German E S Spawn D ~E British Soviet British Soviet British Soviet D Controlling the AI Opponent: FSMs • FSM: States, Events and Actions • Stack Based FSM’s • Polymorphic FSM • Multi-tier FSM FSM: Monster In Sight Robocode Patrol Fight No Monster A resulting plan: Monster in sight No Monster patrolled Fight Patrol

  4. UT task: Domination Strategy: secure most locations UT action: move Bot1 to location B Controlling the AI Opponent: HFSMs Attack Wander ~E Chase Pick-up Powerup E ~S S Spawn Start Turn Right D ~E Go-through Door

  5. Controlling AI Opponent: Scripting • Autonomous agents calculate their action based on… (Nick Haynes) Wargus (Jon Martin) Desires Sensory Input Proximity to items of interest 1 1 1 Space reservation: quasi-coordination 1 1

  6. E E T P E E Controlling AI Opponent: Team Unreal tournament (Eric Lease) • (Dayne Mickelson) • Team sports • Identify high-level decisions • Multi-layered approach • Line of sight (player, npcs) • GOAP: • Agent can dynamically find alternate solutions to problems • Dead Reckoning • Predicting future state • For games: Newton physics • Estimate future trajectory: Kinematics

  7. team controlled team controlled b y computer by human player A Combat B Learning: Adaptive Behavior • Dynamic scripting: Reinforcement learning • But sometimes the problem resides in the scripts not the ordering • Use evolutionary computation to improve scripts (Megan Vasta) Neural networks • Evolve a population (each member is a candidate solution) …

  8. Learning: Adaptive Behavior (2) Allegiance (Jeff Storey) • User model • Flexibility beyond predefined difficulty levels • When/what to update Friendly Enemy Defense -1.0 Weak Strong Medium 0.4 -0.3 0.1 • Induced from a collection of data • Based on information gain formulas • Assume discrete values (Brigette Swan)

  9. Learning: Adaptive Behavior (3) • Pattern recognition • Symbols • Optimization: balancing units in an RTS game • 2. Curse of dimensionalit • Analysis of Machine learning Usage • 1. Cheap to recognize what to learn from? • 2. Cheap to store the knowledge? • 3. Cheap to use the knowledge? • 4. Does game benefit from learning? (Chris Kramer)

  10. Spatial Analysis • Random map generator: • Location of players • Map is generated step-wise by adding clumps • Terrain analysis: • Concepts: borders, corridors • Selection of new colonies • Spatial Analysis: Transport units in RTS games: (Russell Kuchar) (Jay Shipper )

  11. Spatial Analysis (2) • Wall generation • Graph representation: • (tiles, connections) • Greedy algorithm Hierarchy in RTS games (Rami Khouri)

  12. Path-Finding (1) A*: minimize f(n) = g(n) + h(n) • Grid • Graphs • Meshes (Dan Bader) Rep. simplicity versus optimality - Can be used to compute AI • (Tom Gianos) • Navigation set hierarchy String pulling • Interface tables • Reduction memory • Increase performance

  13. Path-Finding (2) (Owen Cummings) (Tom Schaible) • Path Look-Up tables • Several times faster than A* • But memory consumption is high • Solution: Area-based Look-up tables • Notion of portals • Very fast • Throwing a grenade is not so simple! • Add information to nodes • Add behavior info in edges Flying Edge Rappelling Edge Flying Edge Door Edge Vault Edge Jump Edge Hunting players in a convincing manner

  14. Path-Finding (3) • (Adam Balgach) • Racing vehicle control • Multi-layer system • Each layer defines behavior • Optimal racing line • Use of Newton physics • (Don DeLorenzo) • Avoiding obstacles • Should be smooth • Crucial in dynamic worlds Ra a Da Va Obstacle Sidestep Repulsion • Intelligent Steering • Use error correction: • current error + history error + rate error

  15. Game theory Declarative Knowledge Spectimax kind of search Initial state Goals A C B A B C • HTN approach for declarer play • Use HTN planning to generate a game tree in which each move corresponds to a different strategy, not a different card • Reduces average game-tree size to about 26,000 leaf nodes • Compute expectimax and expectimin • Evaluation functions • Pruning search space poker

  16. Game Design • (Peter Shankar) • “Meaningful play” • Outcome is discernable and integrated • Elements for meaningful play: • Semiotics • Systems • Interactivity • Choice Cultural System Experiential System Formal System • Sid Mier says: • “personal touch” is needed

  17. Hall of Fame • Winners Project 1: • Tournament: Adam Balgach, Tom Gianos. Bot: Yankees • Innovation: Tom Shaible, Don Delorenzo. For: "meta-level" FSM design of code. • Winners Project 2: • Tournament: Adam Balgach, Tom Gianos. Team: Yankees (continuing champions!) • Innovation: Swan, Brigette L, Vasta, Megan E., and Khouri, Rami H. For: a number of interesting ideas: predicting next place for firing, distributing battlefield, training examples. • Winners Project 3: • Project # 3 was no tournament. • Winners Project 4: • Tournament: Adam Balgach, Tom Gianos. Team: Yankees (unbeatable!) • Winners Project 5: • Tournament: Tom Schaible, Don Delorenzo. Team: DDTS (new champions!) • Winners Project 6: • Tournament:. Owen Cummings, Dayne Mickelson Team: Tony Wonder (new champions!) • Innovation: Tom Shaible, Don Delorenzo. For: decision trees and reinforcement learning

  18. Acknowledgements • Jon Martin and Eric Lease • All of you: • Presentations were very good • Projects were worked well (despite difficulties) • Changes: • 4 projects: robocode, UT, MadRTS, poker • UT: 2 bots only • Poker: use downloadable version

  19. The End…

More Related