Intelligence Games

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Discover what your mind can do

Games have always been a favorite playground for artificial intelligence research. All major AI ideas have quickly found their way into game-playing agents. This seminar will review the most remarkable milestones of game AI, from simple rule-based solutions for Tic-Tac-Toe and Connect Four to super-human performance in Go and Poker.

Following the evolution of these ideas is an entertaining tour-de-force through the history of artificial intelligence itself. Mathematically inclined students from all fields are welcome to participate.

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Every participant needs to give a 45 minute talk and hand-in a 10 page report. These give 4 credit points according to computer science rules.

Odd One Out Brain Games - Level 5

Games were already considered as important benchmarks by the pioneers of artificial intelligence in the ies. Initially, game AI research focused on algorithms that would achieve provably perfact play. The basic algorithms are called "minimax search" and "alpha-beta pruning".

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A number of relatively simples games could be fully solved this way. Most interesting games are too complex to be solved exactly. Before the emergence of learning, heuristic search tree pruning was the best way to achieve good or even super-human strength with realistic resources. Finding and implementing good heuristics is very difficult and time consuming.

Learning a good playing strategy from high-level human games or from scratch will be much simpler, if it works. Video games are an especially fruitful domain for learning. This is particularly challenging when the agent can only access the screen content, but not the internal game state. AlphaGo and its successors lifted learning-based agent to an entirely new level.

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Authors: Yannakakis , Georgios N. This is the first textbook dedicated to explaining how artificial intelligence AI techniques can be used in and for games. After introductory chapters that explain the background and key techniques in AI and games, the authors explain how to use AI to play games, to generate content for games and to model players.


The book will be suitable for undergraduate and graduate courses in games, artificial intelligence, design, human-computer interaction, and computational intelligence, and also for self-study by industrial game developers and practitioners. Georgios N. He received his Ph.

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His research lies at the crossroads of artificial intelligence, computational creativity, affective computing, and human-computer interaction with an emphasis on the domain of games. He has published over journal and conference papers in the aforementioned fields, his research has been supported by numerous national and European grants, and it has been featured in Science Magazine and New Scientist among other publications.

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Julian Togelius is an Associate Professor in the Dept. He works on all aspects of computational intelligence and games and on selected topics in evolutionary computation and evolutionary reinforcement learning. His current main research directions involve search-based procedural content generation, game adaptation through player modelling, automatic game design, and fair and relevant benchmarking of game AI through competitions.

This book provides a unique and entertaining review of the interplay between them.