RECAP CSE 348 AI Game Programming Héctor Muñoz-Avila

Course Goal Our goal was to understand the connections and the misconceptions from both sides AI research A C B ABC AC B C B A B A C B A C BC A C A B A C B B C A AB C A B C A B C “AI” as game practitioners implemented it projects (me) (you)

Controlling the AI Opponent: FSMs FSM: States, Events and Actions Stack Based FSM’s Polymorphic FSM Multi-tier FSM Spawn D Wander ~E,~S,~D ~E D Attack E,~D ~E E E D ~S Chase S,~E,~D E S S D Soldier RiflemanOfficer BritishSoviet AmericanGerman Machine Gunner BritishSoviet AmericanGerman BritishSoviet AmericanGerman Robocode Planning Operators Patrol  Preconditions: No Monster  Effects: patrolled Fight  Preconditions: Monster in sight  Effects: No Monster PatrolFight Monster In Sight No Monster FSM: A resulting plan: Patrol patrolled Fight No Monster Monster in sight (Finite State Machines in Games

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

Controlling NPCs Individual 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 (Elizabeth Carter) Squad Tactics decentralized centralized Commander Captain Sergeant Soldier Orders Information (danny powell, andrew pro, Kofi White) Special Forces

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

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

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

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

Game Genres Sport Games Dead Reckoning  Military origins  Use in Sport Games Possible transitions for modeling behaviors (Dylan Evans, Matt Kenig Robocode Turn and Go GoTag Up FreezeSlide Turn and Look Go Back Go Halfway

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

Other Game AI Topics Game Trees (Mike Pollock, Kipp W. Hickman, Chirs Boston) Used to determine game difficulty With appropriate evaluation functions avoid needing to construct the whole tree EF(state) = w 1 f 1 (state) + w 2 f 2 (state) + … + w n f n (state) 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

2010 Hall of Fame Project # 1. Robocode. Tournament winner: Chris Boston, Kipp Hickmann, Michael Pollock "The Enraged Armored Mob" (TEAM). Innovation winner: Elizabeth Carter (Reinforcement Learning) Project # 2. DOM. Tournament winner: Chris Boston, Kipp Hickmann, Michael Pollock "Tactical Efficient Anti-social Macabre" (TEAM) Innovation winner: TEAM Project # 3. Special Forces Tournament winner: Chris Boston, Kipp Hickmann, Michael Pollock Target Extermination Aiming Maneuvering (TEAM). Innovation winner: Mike Rowan, Anthony Scimeca. The Cover Up. Project # 4: Wargus Tournament winners: Constantin Savtchenko, 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

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

Final Summary AI research A C B ABC AC B C B A B A C B A C BC A C A B A C B B C A AB C A B C A B C “AI” as game practitioners implemented it A* AI Planning  HTN Planning Heuristic evaluation Machine learning  Decision Trees  Reinforcement learning  Dynamic scripting Game trees 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 Other crucial topics Player modeling Story line, drama NPC behavior Individual Team