[How do you stop racing game AI seeming unfair, but heighten competition? Black Rock's Jimenez goes in-depth to reveal the company's AI tactics for the critically acclaimed Pure.]
This article offers an alternative rubber band method to balance the AI behavior in racing games. It presents the concept in a chronological manner, demonstrating its evolution throughout the development of Pure, our recently released trick-based racing game.
Initially we will cover the three main systems behind the concept: skills, dynamic competition balancing and the "race script". We then move onto explaining its implementation in Pure and how we used the previously mentioned toolset to try and give the player the desired experience. Finally we will offer conclusions and suggest possible alternative uses for the system.
When developing the AI in Pure, we wanted to create a system which always provided convincing, fair and interesting races for the player. We wanted to challenge the player by spreading out the field, while keeping rivals close but not punishing them too much for making mistakes. Races in video games need to be managed well to make them exciting; otherwise the player will almost always stay ahead or fall behind the pack and stay there.
Rubber banding is a system that tries to maintain the tension and thrill of the race by keeping the AI characters around the player. It does so by reducing (drastically) the velocity, cornering skills, obstacle avoidance, etc., of the AI characters in front of player, and increasing (just as drastically) the skills of the ones behind.
Usually rubber band methods rely predominately on speed changes, and so are often criticized because it's obvious when AI riders are going superhumanly fast or brain dead slow.
This method is very effective, as it keeps players surrounded for the whole race. It has an important downside: it's not fair, and that unfairness is easy to spot. It can easily break the illusion of fairness in the race. No matter how well a player does during the first 75% of the race, everything is decided by how they perform at the end. A single mistake in the last section can cost the player the whole race.
On the other hand, no matter how many mistakes the player makes at the beginning, there is still a chance of winning the race. The result: players can get frustrated and feel the competition is not fair. Given all this we rejected using rubber band.
When we originally started the project we wanted to base the performance of the AI mainly on the concept of skill. Every AI character's performance was originally based exclusively on a unique set of skills. Different aspects of the behavior of the AI will do better or worse depending on the associated skill for each ability.
For instance, the "tricks performance" skill governs how well the character performs the tricks, and how often he fails them would be dictated by what we called the "jump effectiveness" skill.
The skills are represented as a real number within the range [0..1], where 0 is the worst the character can perform in the associated category and 1 is the best.
Besides performance, skills can also be used to represent personality. For instance, the character's aggressiveness (controlling how much a character will try to take you off the track) or the probability for him to oversteer/understeer the corners. Thus, you can have skills that won't noticeably modify the performance of the AI character but nonetheless change its behavior. In this article, we will only discuss the skills that affect performance.
We initially thought the game difficulty could be balanced by determining a skill range for the AI. For instance, we thought a range of [0.4..0.6] would be fine for normal difficulty, having the best character of the race 0.6 in all his skills and the worst character 0.4. This approach presented some problems:
We soon realized this was far too inflexible and put us even further from achieving a fair but challenging race. Therefore we decided to call these values "initial skills" and have another set that would be the actual skills to be applied, which would be based on the initial ones.
These new sets of "actual skills" (we will refer to them simply as "skills" from now on) were calculated by adding an offset (either positive or negative) to the initial skills. This offset depends on the state of the race. We called this system of modifying the skills dynamically during the race "Dynamic Competition Balancing" (we will refer to it as DCB in this article).