Algorithms that Play and Design Games

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#artificial intelligence #video games #computational intelligence #AI

From the IEEE Computational Intelligence Society (CIS), this webinar by Julian Togelius discusses how methods from the computational intelligence toolbox--including evolutionary computation, neural networks, and Monte Carlo Tree Search--can be adapted to address the research challenges in developing algorithms that can play or design a wide variety of games as well as humans, or even better.

The race is on to develop algorithms that can play a wide variety of games as well as humans, or even better. We do this both to understand how well our algorithms can solve tasks that are designed specifically to be hard for humans to solve, and to find software that can help with game development and design through automatic testing and adaptation. After recent successes with Poker and Go, the attention is now shifting to video games such as DOOM, DoTA, and StarCraft, which provide a fresh set of challenges. Even more challenging is designing agents that can play not just a single game, but any game you give it. A different kind of challenge is that of designing algorithms that can design games, on their own or together with human designers, rather than play them. I will present several examples of how methods from the computational intelligence toolbox, including evolutionary computation, neural networks, and Monte Carlo Tree Search, can be adapted to address these formidable research challenges.

Biography: Julian Togelius is an Associate Professor in the Department of Computer Science and Engineering, New York University, USA. He is also a co-founder of the game AI company modl.ai. Julian works on artificial intelligence for games and games for artificial intelligence. His current main research directions involve search-based procedural content generation in games, general video game playing, player modelling, generating games based on open data, and fair and relevant benchmarking of AI through game-based competitions. He is the Editor-in-Chief of IEEE Transactions on Games and has been chair or program chair of several of the main conferences on AI and Games. Togelius holds a BA from Lund University, an MSc from the University of Sussex, and a PhD from the University of Essex.

From the IEEE Computational Intelligence Society (CIS), this webinar by Julian Togelius discusses how methods from the computational intelligence toolbox--including evolutionary computation, neural networks, and Monte Carlo Tree Search--can be adapted to address the research challenges in developing algorithms that can play or design a wide variety of games as well as humans, or even better.

The race is on to...

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