In IBM Research News: We used a number of game approaches to teach AI. But business decisions are much harder than games. So games are pointers to AI, but rarely directly applicable.
" ... Dr. Gerald Tesauro, the IBM Research scientist who taught Watson how to make wagers when its Jeopardy!, has been named an Association for the Advancement of Artificial Intelligence (AAAI) Fellow. His development of TD-Gammon, “a self-teaching neural network that learned to play backgammon at human world championship level,” and work applying machine learning across disciplines from computer virus recognition to computer chess, and other fields made him an ideal candidate for the association’s title.
You’ve worked on machines that play Jeopardy!, chess and backgammon. What is the significance of machines that can play games?
Dr. Gerald Tesauro:
In the early decades of AI, algorithms were not ready to tackle the ambiguous, ill-defined nature of real-world problems. Researchers therefore proposed that complex board games like chess and backgammon could serve as an ideal testing ground for AI algorithms (the so-called "Drosophila of AI"). Tasks such as playing grandmaster-level chess may be incredibly complex, but they can be precisely specified for the computer.
By working in these domains, researchers made enormous progress in search, learning, and simulation techniques, to the point where the best computers now surpass the best humans in virtually all classic board games. As a result, AI is now moving on to tackle real-world ambiguity head-on. ... "
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