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We present a method of automatically synthesizing steps to solve searchproblems. Given a specification of a search problem, our approach uses symbolicexecution to analyze the specification in order to extract a set of constraintswhich model the problem. These constraints are used in a process called modelcounting, which is leveraged to compute probability distributions relatingsearch steps to predicates about an unknown target. The probabilitydistribution functions determine an information gain objective function basedon Shannon entropy, which, when maximized, yields the next optimal step of thesearch. We prove that our algorithm converges to a correct solution, anddiscuss computational complexity issues. We implemented a domain specificlanguage in which to write search problem specifications, enabling our staticanalysis phase. Our experiments demonstrate the effectiveness of our approachon a set of search problem case studies inspired by the domains of softwaresecurity, computational geometry, AI for games, and user preference ranking.

Symbolic Execution + Model Counting + Entropy Maximization = Automatic Search Synthesis