It's an exciting time to be involved in artificial intelligence! New algorithms are surfacing after decades of research, modern hardware is making a huge impact, and companies/developers are taking it more seriously than ever. One of those emerging algorithms is Monte-Carlo Tree Search, or MCTS for short.
This algorithm was discovered in 2006, and its application to board games has been extremely succesful so far. Now MCTS is making its way into AAA video games and simulations too. Over the next few days, we'll be digging into MCTS in depth as we release The AI Sandbox™.
Bonuses Available for 2014-8-29 12:00:00 GMT+00:00...
Yes, it's finally launch week! Thanks to those of you who helped with testing and feedback in our Community Alpha, and everyone else for your patience. All details are revealed; find out more about The AI Sandbox™ and our first release called Dig Into MCTS below...
1. So Why MCTS?
2. Common Challenges
3. About The AI Sandbox
This video covers the current state of AI, where and why Monte-Carlo Tree Search fits in, the inspiration for our work on The AI Sandbox™, and what's included in Dig Into MCTS.
The MCTS algorithm is the first simulation to be released within The AI Sandbox™ and the team has spent many months on the topic — developing, refining and tuning the implementation.
For what reason should you dig into Monte-Carlo Tree Search? We've covered at least four great reasons in the past few videos...
In short, MCTS seems like the ideal algorithm for AI enthusiasts! It's certainly been keeping us busy over the weekends and evenings too...
Part of the challenge applying any advanced artificial intelligence solution is that it can take significant time and effort to get up-and-running. Over the many years since we started our research projects, we've bumped into many problems. (It can be both depressing and annoying.)
Here's a quick guide — partly as a warning — of things you'll likely encounter:
Luckily, the state of modern game engines is improving fast and there are many libraries & tools for scientific computing too. This reduces the time you'll spend on infrastructure to months, depending how many compromises you're willing to make!
Over the past years, we've been experimenting with a variety of artificial intelligence techniques and applying them into games. We made many mistakes in the process, as it took a lot more work to build certain prototypes that we would have hoped... but at lest we suffered so you don't have to!
In the process, we've built up an engine and supporting tools that have significantly reduced our turn-around times for building prototypes or doing research. It's broken down into three parts:
Combining these three separate worlds together has been an interesting challenge! But it's also been a very rewarding process...
The first simulation we're releasing within The AI Sandbox™ focuses on Monte-Carlo Tree Search. The product itself has multiple levels, depending how involved you want to get!
Note: The AI Sandbox supports Windows (from 7 to 8.1) and Linux (as .deb on Ubuntu officially).
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