Whether you’re playing poker against a single opponent or find yourself in a bidding war over a home purchase with another prospective buyer, you are operating under conditions of imperfect information. You know what cards you’re holding in the poker game, and you also know how much above the home’s asking price you can afford, but you don’t know your opponent’s hand in the card game or how high the other home buyer is willing to go. 

A paper co-authored by MIT researchers and presented in April at the International Conference on Learning Representations in Rio De Janeiro won’t tell you what to do in these situations, specifically. But it does offer new insights into so-called imperfect-information games that involve two contestants facing off in a “zero-sum” competition, where one player’s gain means the other player’s loss.

MIT researchers on the project include Sobhan Mohammadpour, a PhD student in MIT’s Department of Electrical Engineering and Computer Science (EECS) and the Laboratory for Information and Decision Systems (LIDS); and Gabriele Farina, an assistant professor in EECS and a principal investigator at LIDS. Additional co-authors include Max Rudolph of the University of Texas at Austin (UT), Nathan Lichtlé of the University of California at Berkeley (UCB), Alexandre Bayen of UCB, J. Zico Kolter of Carnegie Mellon University (CMU), Amy X. Zhang ’11, MNG ’12 of UT; Eugene Vinitsky of New York University; and Samuel Sokota of CMU. 

The focus of the new work is on algorithms that could be used to train neural networks to participate in imperfect-information games.

Source link