While the spotlight for AI in games is often on first-person shooters or real-time strategy games, sports games also present some rather unique challenges and solutions. There certainly isn’t as much research or industry experience available to learn from, but enough to get a good overview of what happens behind closed doors at Electronic Arts or Take 2 every year!

This week’s question comes from Anthony, who’s looking to do a masters project relating to football games:

“What are people’s opinions on sports game AI? […] I’d think that sports AI would be one of the tougher tests for developers as you don’t have the “freedom” that might come from a fantasy style game (given that most players would never have had real life experience of those situations).

Almost everyone has played sports at some point in their lives and would have a certain level of expectation of how a game would play. Does anyone know of any good academic resources I could look into in this area?”

Generally speaking, the AI of a sports game is structured in a similar way than for FPS or RTS games, with layers for animation and control at the bottom, individual decision making in the middle, and tactical reasoning at the top. Within each of those layers, however, there are bigger differences compared to other types of games.

Screenshot 1: Madden NFL 2008 has some of the best football AI to date.

Character Animation

While players generally have high expectations of sports simulations, the motion of the players is by far the most important. These game require an increased level of realism for actors moving, handling a ball, or even interacting with other characters. Everything else is built on top of this.

To confirm the importance of animation in sports games, look into the amount of research done in this field! Many leading animation projects are co-funded or sponsored by EA and other publishers. In particular, read papers from recent Siggraph conferences, you’ll be surprised how many of them use AI-related technology:

• Character Animation reviews of recent papers about motion synthesis here at AiGameDev.com.

• Publications from Jovan Popovic’s work at MIT and Zoran Popovic’s work from the University of Washington, among others.

There’s lots of room for improvement in this field, but motion synthesis requires a tough stomach! The fact that NaturalMotion, a provider of middleware for animation, is working on a football simulation (called Backbreaker) is a sign that there’s more work to be done in this field.

Physics and procedural simulation certainly has a role to play, but realistic animation can only be achieved using motion capture these days — for now and the foreseeable future. Studios end up using simulation for short clips when a physical reaction is needed, then blending back to motion capture as soon as possible to provide purposeful behaviors again.

Screenshot 2: Playing the perfect animation to intercept a projectile is technically very difficult.

Individual Behaviors

At the mid-level in the architecture, the AI for individuals is much simpler than the animation layer. Typically, this is done using multiple steering behaviors that control the characters by combining basic rules like: staying away from opponents, moving towards a target, finding open space, etc.

The rest of the I at an individual is very similar to traditional first- or third-person shooter AI. It typically involves simple decision making techniques like finite state machines to execute the tactics passed down from the higher level AI. There’s not too much active research in this field, as it’s much better understood.

See the following pages:

• Craig Reynold’s steering behaviors for an idea of how this works in theory, and consider OpenSteer for the implementation.

• Mat Buckland’s book Programming Game AI by Example, which has a section on how to apply these ideas to a football game.

Screenshot 3: The basic behaviors of players is implemented as a finite state machine.

Tactical AI

From a tactical perspective, sports games seem to be simpler than the average shooter. There’s only one pitch size rather than multiple levels, there’s no complex geometry. The only thing you have to take into account is the different members on the team. Granted, it’s not a trivial problem. But the fact that so many details from other games are missing means that the AI developers can focus on what’s important: adapting to the player in both offense and defense.

As it turns out, there has been some research on adaptive AI in sports games, particularly in the Madden NFL games. Over the years, the developers have gone from simple cheating by using the player’s choices to help counter his tactics, to using

• This document (PDF) has a pretty impressive history of the AI in Madden games, from the mid-90s until 2004.

• Also, check out these research projects at the University of Alberta (as pointed out by Anthony), using statistics to learn both online and offline.

Screenshot 4: The initial placement and purpose of each player is critical for tactics.

Do you have any insights on AI technology in sports games? How do you feel about the behaviors of these games?