Tradezilla cont. (page 3 of 3)
A Different Kind of Brute Force
Instead of constantly updating our AI in response to user feedback, I tried to think about a better way to advance the artificial intelligence.
The main goal of the system is to build an intelligent "Artificial General Manager". One that can win baseball games and championships within the world of Baseball Mogul -- an extremely detailed simulation of Major League Baseball.
We know how "real" General Managers are made. Millions of years of evolution have created the human brain. Like the user feedback we've already been using, evolution is a "brute force" method. It creates billions of organisms, each with different traits, and only the best ones survive.
Evolving a General Manager
I decided to use this system for creating an intelligent General Manager (or, as it turns out, a group of intelligent General Managers, each fine tuned to their own circumstances).
<Because Baseball Mogul can simulate an entire season in less than 30 seconds, I realized that by leaving my computer running for a few days I could simulate over 10,000 seasons -- the equivalent of thousands of entire careers for a real-life a General Manager.
In the simulation, one General Manager career correlates to one generation of natural selection. We started the simulation with 30 randomly generated General Managers. Each General Manager was defined in 28 dimensions, from the value they placed on young relief pitchers to whether they prefered drafting high school or college players.
All of these 28 dimensions interacted with each other. So one General Manager might have a penchant for drafting left-handed college pitchers with great "control" ratings while choosing to trade for power-hitting veteran outfielders. We ended up with over 2.6 x 1064 different possible General Manager profiles.
In addition, these profiles also incorporated how General Managers worked within the constraints of their organization. One profile might define a GM that always paid top dollar for the newest free agent, while another profile describes a GM that only does this when he has a surplus in his budget.
We then began simulating our artificial baseball world. Every 5-10 seasons we would stop and measure every GM's success, based on games and championships won. The bottom 80% of GMs would be fired and the top 20% would retire. However, the top 20% also became the "parents" of the next generation of GMs. For each surviving GM, we randomly altered his profile, like the mutations that power evolution.
This process continued for 11,000 simulated baseball seasons -- enough to take us past the year 13000. We then filtered through the successful profiles, compared them to the other successful profiles, and essentially distilled a set of unique profiles that were the most successful.
We copied these profiles back into Baseball Mogul 2007, and they are now used as the actual Artificial General Managers that you must haggle with on a daily basis in order to make trades and improve your team.
Footnotes
Although we use some different algorithms for assessing "replacement level", the logic above is essentially the same.