  A fundamental challenge in imperfect-information games is that states do not
have well-defined values. As a result, depth-limited search algorithms used in
single-agent settings and perfect-information games do not apply. This paper
introduces a principled way to conduct depth-limited solving in
imperfect-information games by allowing the opponent to choose among a number
of strategies for the remainder of the game at the depth limit. Each one of
these strategies results in a different set of values for leaf nodes. This
forces an agent to be robust to the different strategies an opponent may
employ. We demonstrate the effectiveness of this approach by building a
master-level heads-up no-limit Texas hold'em poker AI that defeats two prior
top agents using only a 4-core CPU and 16 GB of memory. Developing such a
powerful agent would have previously required a supercomputer.
