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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.

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RECAP CSE 348 AI Game Programming

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  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 (me) (you) 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 (Finite State Machines in Games

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

  5. Controlling NPCs Squad Tactics Special Forces Individual centralized decentralized • Animation Controller approach • Layers categorized by regions of body that they affect • Reputations: Create global reputations based on average of other’s opinions • Autonomous behavior by establishing ownership of the objects Commander Orders Information Captain Sergeant Soldier (Elizabeth Carter) (dannypowell, andrew pro, Kofi White)

  6. Path-Finding A* • Navigation • Navigation set hierarchy • Interface tables • Reduction memory • Increase performance

  7. Decision Tree team controlled team controlled b y computer by human player induction A Combat B Controlling AI Opponent: Learning Induction of Decision Trees • Reinforcement Learning Training script 1 Training script 2 …. Training script n Counter Strategy 1 Counter Strategy 2 …. Counter Strategy n Knowledge Base Revision Manually Extract Tactics from Evolved Counter Strategies Evolutionary Algorithm Evolve Domain Knowledge If owning locations 1 and 2, and 3 then defend locations 1, 2, and 3 DOM

  8. Game Genres First-Person Shooters • Racing games (Emily Cohen) • Racing vehicle control • Multi-layer system • Each layer defines behavior • Optimal racing line • A lot of path finding issues • Assigning values to locations • and to paths (ConstantinSavtchenko , Zubair R. Chaudary, Kenneth H. Rentschler) (Jim Pratt, Qihan Long, Austin Borden)

  9. Game Genres Wargus Role Playing Games • RTS • RTS Game Components • Civilization • Build • Unit • Resource • Research • Combat • Level of Detail • Reputation system (Anthony Scimeca, Mike Rowan) (Xu Lu)

  10. Game Genres • Sport Games • Dead Reckoning • Military origins • Use in Sport Games • Possible transitions for modeling behaviors Robocode Go Tag Up Turn and Go Freeze Slide Turn and Look Go Back Go Halfway (Dylan Evans, Matt Kenig

  11. Other Crucial Topics Player Modeling Story line, drama • Hierarchical model of what a player can do • Heuristic values for preference of states  determine player strategy • Taxonomy of storylines • Propp’s approach: lineal story • Barthes: allow ramifications • Dialog managers using • finite state machines • planning • Fractions versus behavior (Mike Pollock, Kipp W. Hickman, Chirs Boston) (Mike Chu, Joey Blekicki, Stephen Kish )

  12. Other Game AI Topics Game Trees • Programming Projects • Finite State Machines • RTS • Team-based simulation • Simulate some of the real game developing conditions: • Working with someone else’s code • tight deadlines • need lots of trial and error to tune the AI • Used to determine game difficulty With appropriate evaluation functions avoid needing to construct the whole tree EF(state) = w1f1(state) + w2f2(state) + … + wnfn(state) (Mike Pollock, Kipp W. Hickman, Chirs Boston)

  13. 2010 Hall of Fame • Project # 1. Robocode. • Tournament winner: Chris Boston, KippHickmann, Michael Pollock "The Enraged Armored Mob" (TEAM). • Innovation winner: Elizabeth Carter (Reinforcement Learning) • Project # 2. DOM. • Tournament winner: Chris Boston, KippHickmann, Michael Pollock "Tactical Efficient Anti-social Macabre" (TEAM) • Innovation winner: TEAM • Project # 3. Special Forces • Tournament winner: Chris Boston, KippHickmann, Michael Pollock Target Extermination Aiming Maneuvering (TEAM). • Innovation winner: Mike Rowan, Anthony Scimeca. The Cover Up. • Project # 4: Wargus • Tournament winners: • ConstantinSavtchenko , Zubair R. Chaudary, Kenneth H. Rentschler.Segfault • Jim Pratt, Qihan Long, Austin Borden • Almost all beat default connected map • Most beat default connected map variant • Only the two above beat disconnected map

  14. Acknowledgements • All of you: • Presentations were geneally very good • Projects were worked well (despite difficulties) • All master groups made their projects work • Changes for future iterations of this course: • Adjust Wargus, Balance Special Forces

  15. 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 B C B C C B A A B C Final Summary • Programming • Finite State Machines • RTS • Team-based simulation • Last project: AI that works in any map • Genres • First-person shooter • Real-time strategy • Racing games • Team sports • Role-playing games • Path finding • Look-up tables • Waypoints • A* • AI Planning • HTN Planning • Heuristic evaluation • Machine learning • Decision Trees • Reinforcement learning • Dynamic scripting • Game trees • Other crucial topics • Player modeling • Story line, drama • NPC behavior • Individual • Team

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