Building a Sports AI
Architecture
Ushhan D. Gundevia
November 8, 2004
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 AllStar Baseball for N64
 Has a Computer Science Degree from
Purdue

terry.wellmann@high-voltage.com
If you would like to know more


http://xbox.ign.com/articles/372/372418p1.html
http://www.mobygames.com/developer/sheet/vi
ew/by_genre/developerId,126714/
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
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

Agent Plans

Examples of Offensive, Defensive and
Shared plans
 Pass
 Shoot
 Drive
to basket
 Run the play
 Rescue a trapped ball handler
…
Pseudo code for Agent Plan
Class AgentPlan
{
…
float EvaluateInitiation();
float EvaluateContinuation();
void Initiate();
void Update();
…
}
Why float?

The EvaluateInitiation and EvaluateContinuation 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
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
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
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
State Transition and Flows
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);
…
};
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 ifthen logic

 Any
Idea where?
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();
}
Agent AI

A well thought-out collections of Utility
Functions and data used by the plans to
evaluate and execute the things they
represent
Shot Plan

Taking a closer look at the shot plan
 Three

Potential success rate



Players skills and abilities – denoted by rating values
Penalties – e.g. defensive pressure applied to the player
Type of shot




things to keep in mind
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
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
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

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
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.
Thank You