In PvP games, bot AI is often one of the last things you’ll develop. We needed it to be one of the first.

Most game AI takes the form of an ‘expert system’ — a carefully crafted set of rules that give bots their operating orders based on the current state of the game. This scripted approach is incredibly complex, especially for deep strategy games (like the ones we love to play and make).

Imagine a hypothetical real-time strategy game: the number of competing objectives to consider can be overwhelming. Should I focus on my economy and generating more income? Should I send out scouts to understand the enemy’s position and strategy? Should I build up my infantry, or should I rush the enemy’s base with what I’ve got?

Now imagine building an expert system AI that takes into account all of those variables and player decision points in a game that’s in early production, with systems and mechanics changing dramatically from week to week. Any scripted AI systems built during early production will require constant upkeep to stay compatible with always-evolving game mechanics and systems.

But our Alpha-Driven Development philosophy puts a higher value on having effective bots from the earliest stages of development, for a few reasons:

But if expert systems (the most common AI framework for strategy games) are costly and difficult to implement early, how could we build a bot system that works well and scales? We brought in an AI programming expert, Martin Hesselborn, to help us figure it out.

Our answer was a less-common approach to AI: a ‘searching system’ that, instead of making decisions based on a heavily scripted decision-making framework, simulates a variety of potential choices available in the current game state, and measures all of their outcomes to determine the best course of action.

“One thing that struck me about the game OMG’s building is that you can figure out quite a bit about the game rules by simply trying out an ability and seeing what happens,” Martin recalls. “So instead of building out a complex script for every single ability in the game — which, of course, would be changing all of the time — I wondered if a searching system could do the trick. I believed it could.”

“The system ultimately has two components: an evaluation function, and search. Search runs a bunch of experiments based on all of the available choices, and then simulates the outcomes some amount of time into the future,” he explains. “Evaluation then looks at those outcomes, and decides based on some relatively straightforward criteria — like the health of the player’s units versus the AI’s — which outcome was best.”

The evaluation criteria can be tweaked to enable different bot personalities, and the bot difficulty scales surprisingly well based on how far into the future it looks. The more simulations of potential future choices the AI makes, the more accurate its decision-making (and the more deadly an opponent it makes).

This approach rolls with the punches. Because all a searching AI system does is simulate the choices available to a player with no rationale or consideration, and then measure its outcome according to easy-to-evaluate rules, this approach is compatible with virtually any new game mechanic we introduce.

A few stars aligned to make this approach feasible for the game OMG is building:

We’re quite pleased with the effectiveness of the AI we’ve built. Big thanks to Martin for his expert guidance and contribution early on! By the way, One More Game is hiring.