Building a Sports AI Architecture

1. Building a Sports AI Architecture Ushhan D. Gundevia November 8, 2004

2. Terry Wellmann • He has been programming since 1983 • Responsible for architecting and writing AI for Microsoft’s NBA Inside Drive franchise • He was also the lead programmer on All-Star Baseball for N64 • Has a Computer Science Degree from Purdue terry.wellmann@high-voltage.com

3. If you would like to know more • http://xbox.ign.com/articles/372/372418p1.html • http://www.mobygames.com/developer/sheet/view/by_genre/developerId,126714/

4. Challenges • Real teams spend countless hours practicing together – Why? • To improve an individuals skills and abilities • To train a group of independently thinking individuals to instinctively react to a situation in a manner predictable by other teammates • To stimulate cohesive group decision making in a game is difficult

5. Things to keep in mind • Keep it simple • Break down decisions into various levels • Spend time on planning • Don’t be afraid to make mistakes • Do not underestimate the power of Randomness

6. Agent Plans • Examples of Offensive, Defensive and Shared plans • Pass • Shoot • Drive to basket • Run the play • Rescue a trapped ball handler • …

7. Pseudo code for Agent Plan Class AgentPlan { … float EvaluateInitiation(); float EvaluateContinuation(); void Initiate(); void Update(); … }

8. Why float? • The EvaluateInitiation and Evaluate-Continuation functions return float values • Always building complex systems, with several plans compared to determine which one is used • Each plan is a building block in a complex system • Each plan individually evaluates the current situation to determine its feasibility • Returns a value between -1.0 and 1.0 • Execute plans if value > 0 • To strongly encourage a plan, return a value > 1

9. Team Management • Its implemented using a FSM • Each state encapsulates a different goal and a set of responsibilities • Basketball can be assumed to have 3 kinds of states • Offensive States • Defensive States • Common States

10. Offensive and Defensive States • They mirror each other • Responsible for all aspects of the game play that the user can interact with. • They are ? • Inbound • Transition • Frontcourt • Rebound • Recover Loose Ball • Free Throw

11. Common States • Neutral situations when the ball is not in play and neither team is in offense or defense • They are ? • Pre-game • Tip-off • Time-out • Quarter Break • Substitution • Post Game

12. State Transition and Flows

13. Pseudo Code - Frontcourt Offense State Class FrontCourtOffense : public TeamStateBase { protected: enum TBallHandlerPlans { eBallHandlerShot, eBallHandlerPass, eBallHandlerDrive, eBallHandlerPlayMovement, … }; enum TNonBallHandlerPlans { eNonBallHandlerRescueBallHandler, eNonBallHandlerGetOpenForPass, eNonBallHandlerSetPick eNonBallHandlerPlayMovement … } public: … int Update(int); int ReInit(void); … };

14. What’s that? • The key elements are the two enumerated types that define priority order • We might also have 2 different update functions for a BallHandler and a NonBallHandler for simplicity • In some situations priorities might not be enough, me may need some explicit if-then logic • Any Idea where?

15. Pseudo Code for Update Fn for (each plan to evaluate) { //get the proper evaluation value if (planToCheck == currentPlan) evalValue = EvaluateContinuation(); else evalValue = EvaluateInitiation(); //is the current plan the best option //simple priority order plan selection if (evalValue > bestEvalValue) { bestEvalValue = evalValue; bestPlan = currentPlan; } //handle situations the priority method can’t else if (bestValue > 0.0f && bestPlan == shot) //is the drive plan a valid option if (currentPlan == drive && evalValue > 0.0f) //set the best plan to drive, but leave the bestValue as it is – Why? bestPlan = drive; //if the best plan is the current plan, update it else initiate it if (bestPlan == currentPlan) currentPlan->Update(); else currentPlan->Initiate(); }

16. Agent AI • A well thought-out collections of Utility Functions and data used by the plans to evaluate and execute the things they represent

17. Shot Plan • Taking a closer look at the shot plan • Three things to keep in mind • Potential success rate • Players skills and abilities – denoted by rating values • Penalties – e.g. defensive pressure applied to the player • Type of shot • Is Dunk possible? • How far is he from the basket? • Does he have a clear path to the basket? • Can he dunk? • Is Lay-up possible? • Same questions as above, but every professional NBA player can lay-up • Last option is Jump shot • Tell the player how to perform that shot

18. High-Level Agent AI • Command issuing functions • Decisions that require more logic then a simple random no. • Whether to do a flashy 360° dunk or a simple one • Wrapper for Agent Mechanics

19. Agent Mechanics • Low-Level AI • Manage and select the animations • Simple decisions that require no more than Random nos. • Like commands given to people who have no knowledge about basketball, but can follow simple directions. e.g. walk, jog, run

20. Conclusion • Sports games present a unique set of challenges • The rules are well defined and allow little room for creativity • Professional athletes have personalities and styles that are recognized by millions of fans • User should be able to capture the abilities, personalities and emotions of the athletes being stimulated

21. References • Building a Sports AI Architecture – AI Game Programming Wisdom 2 • Learning Goals in Sports Games – Jack van Rijswijck www.gdconf.com/archives/2003/Van_Ryswyck_Jack.doc • A very nice paper dealing with learning in Sports Games, using Soccer as the base.

22. Thank You