Also see some of our talks on algorithms in combinatorial games.
Abstract: The ancient Chinese game of Go, or Wei Qi, has been used as a test bed for measuring progress in Artificial Intelligence for decades. After long periods of slow improvement, two relatively recent advances in heuristic search have led to rapid progress, culminating in Deepmind's development of their famous AlphaGo program. This lecture will explain the most important components in the creation of AlphaGo, and discuss its recent match against World #1 player Ke Jie from a Computing Science perspective.
AlphaGo is the most prominent example of many successful applications of modern heuristic search. The common novel architectural theme of these programs is that they combine all the following four elements: deep search, knowledge, simulations, and machine learning. In contrast, previous successes, such as IBM's chess machine Deep Blue, which famously defeated Garry Kasparov exactly 20 years ago, focused on just two of those components: search, and human-designed domain-specific knowledge. This lecture outlines a framework for modern heuristic search, showing many examples from diverse application areas which combine the four elements above. While we now have many successful case studies, much fundamental research remains to be done to understand how such systems work, and how to design new ones. In the last part of this talk, I will summarize some lessons learned, and outline preliminary ideas that could lead to a general framework for modern heuristic search.
Last modified: Nov 30, 2023 by Martin Müller