Game playing is a classic discipline of AI and had a major break through in the 90s when Deep Blue defeated Kasparov and arguably became the world’s best chess player. First, we will look which algorithms made that success possible and how they are still used within Stockfish, one of the leading chess engines. Here, we will cover Minimax and AlphaBeta pruning. 

However, the emphasis of this talk will be on Monte Carlo Tree Search and its advanced use in AlphaZero that relies on zero human heuristics and without even an opening library. You will learn how it trains using self play on a convolutional ResNet architecture. At the end, we will briefly look at a great game between Stockfish and AlphaZero and why the era of classic chess engines might be over.