Robot Shark Gaming

Taking a bite out of the competition with artificial intelligence applied to games.

About Robot Shark Gaming

We build state-of-the-art artificial intelligence programs for studying and playing strategic games: card games, video games, fantasy sports, and even professional sports. Our AI is guided by game theory that describes the principles of effective strategic decision-making and defines optimal play. Generated by computational algorithms that are mathematically proven to succeed, our programs play optimal strategies with little to no human guidance or intervention. When necessary, our AI is backed by high performance supercomputers, allowing our programs to tackle even the most complex strategic scenarios. By asking the AI what it would do in any given scenario, we can learn how to improve our own game.

Robot Shark Gaming was founded by Richard Gibson in 2015 and is situated in Victoria, BC, Canada.


Poker AI

We consult with professional poker players and build poker bots for a variety of poker variants. Formerly apart of the University of Alberta's renowned Computer Poker Research Group, we use the same technology behind the world's best poker bots. We also provide software tools for analyzing the bots' strategies. Unlike other tools on the market, our bots report game-theoretic optimal strategies for not just the post-flop play, but for the pre-flop too. Want to know the correct range to call an opening min raise from the big blind heads-up? We can tell you.

Video Game Development

We provide game development support for companies looking to boost their coding force. Our latest efforts have helped improve Super Mega Baseball 2. We have integrated new gameplay features, such as strategic defensive positioning, and improved AI decision-making, such as when to pull the pitcher for a pinch hitter. We have also applied our mathematical expertise to optimally balance and diversify the game's fictional teams, as well as design a novel matchmaking system to ensure online matches are as fair and competitive as possible.

Future Projects

The algorithms we use are general enough to be applied to many domains, such as strategy video games, professional sports, and fantasy sports. While rule-based and statistic-based approaches are commonly used in these fields, we believe our game-theoretic approach is superior. Our goal is to continue to create improved AI opponents in video games, shed new light on how to effectively coach and manage a professional sports team, and defeat the competition in fantasy sports.

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