In a breakthrough result, after five years of striving from 14 different international teams from nine countries, two teams have cracked the human-like play barrier!
It’s especially satisfying that the prize has been won in the 2012 Alan Turing Centenary Year. Where to now for human-like bots?
Next year we hope to propose a new and exciting challenge for bot creators to push their technologies to the next level of human-like performance.
The winners are the UT^2 team from the University of Texas at Austin, and Mihai Polceanu, a doctoral student from Romania, currently studying Artificial Intelligence in Brest, France.
The UT^2 team consists of Professor Risto Miikkulainen, and doctoral students Jacob Schrum and Igor Karpov. Full results can be found on the results page.
The two teams will share the $7000 first prize from sponsor 2K Games.
Here are some thoughts from Mihai about his bot
[…] my idea was to make the bot record other players at runtime instead of having a database of movements. This way, if the bot sees a non-violent player (shooting at the bot but around it, or shooting with a non-dangerous weapon) it would trigger a special behavior, mirroring.
This makes the bot mimic another player in realtime, and therefore “borrowing” the humanness level. I thought that if my bot would meet a human, then it would seem human itself.
I know this idea is not too new, actually, it was inspired by some “how to be a salesman” articles I skimmed, which said that if not too obvious, mimicking can make a peer more comfortable with a conversation.
The bot records keyframes of the target’s actions and plays them back with a small delay, and without full fidelity, so that it appears somewhat independent (mimic, not copy).
Due to the lack of long-term memory and the realtime nature of the mirroring module, I was obliged to use classic graph navigation, which I customized in order to hide traces of bot-like movement such as the brief stops on navpoints, aiming behavior and elevator rides.
The bot’s movement and aim are completely separate so that it can concentrate its aim to what requires attention while moving freely.
The bot also has the ability to remember its target, follow it when out of sight and dodging behavior based on the firing direction of its opponent.
Also inspired from how human players generally play, the bot will forget its target if another opponent is more aggressive.
I believe that BotPrize and other related competitions are a great way to test new ideas or old ones in new contexts, a great challenge for programmers and why not other fields of activity.
The UT^2 team had this to say
The complex gameplay and 3-D environments of “Unreal Tournament 2004” require that bots mimic humans in a number of ways, including moving around in 3-D space, engaging in chaotic combat against multiple opponents and reasoning about the best strategy at any given point in the game.
Even displays of distinctively human irrational behavior can, in some cases, be emulated.
“People tend to tenaciously pursue specific opponents without regard for optimality,” said Schrum.
“When humans have a grudge, they’ll chase after an enemy even when it’s not in their interests. We can mimic that behavior.”
In order to most convincingly mimic as much of the range of human behavior as possible, the team takes a two-pronged approach.
Some behavior is modeled directly on previously observed human behavior, while the central battle behaviors are developed through a process called neuroevolution, which runs artificially intelligent neural networks through a survival-of-the-fittest gauntlet that is modeled on the biological process of evolution.
“In the case of the BotPrize,” said Schrum, “a great deal of the challenge is in defining what ‘human-like’ is, and then setting constraints upon the neural networks so that they evolve toward that behavior.
“If we just set the goal as eliminating one’s enemies, a bot will evolve toward having perfect aim, which is not very human-like.
So we impose constraints on the bot’s aim, such that rapid movements and long distances decrease accuracy.
By evolving for good performance under such behavioral constraints, the bot’s skill is optimized within human limitations, resulting in behavior that is good but still human-like.”
Miikkulainen said that methods developed for the BotPrize competition should eventually be useful not just in developing games that are more entertaining, but also in creating virtual training environments that are more realistic, and even in building robots that interact with humans in more pleasant and effective ways.
The UT^2 team has made their bot available at this location if you want to try it out (you’ll also need a copy of Unreal Tournament 2004).