Many AI problems, such as planning, grammar learning, program induction, and theory discovery, require searching in symbolic domains. Most models perform this search by evaluating a sequence of candidate solutions, generated in order by some heuristic. Human reasoning, though, is not limited to sequential trial and error.\ In particular, humans are able to reason *about *what the solution to a particular problem should look like, before comparing candidates against the data. In a program synthesis task, for instance, a human might first determine that the task at hand should be solved by a tail-recursive algorithm, before filling in the\ algorithm{\textquoteright}s details.

Reasoning in this way about solution structure confers at least two computational advantages. First, a given structure subsumes a potentially large collection of primitive solutions, and exploiting the constraints present in the structure{\textquoteright}s definition makes it possible to eval- uate the collection in substantially less time than it would take to evaluate each in turn. For example, a programmer might quickly conclude that a given algorithm cannot be implemented without recursion, without having to consider all possible non-recursive solutions. Second, it is often possible to estimate ahead of time the cost of evaluating different structures, making it possible to prioritize those that can be treated cheaply. In planning a route through an unfamiliar city, for example, one might first consider possibilities which use the subway exclusively, excluding for the moment ones that involve bus trips as well: if a successfully subway-only solution can be found, one then avoids the (potentially) exponentially more difficult bus-and-subway search problem.

Here, we consider a family of toy problems [1], in which an agent is given a balance scale, and is required to find a lighter counterfeit coin in a collection of genuine coins using at most some prescribed number of weighings. We develop a language for expressing solution structure\ that places restrictions on a set of *programs*,\ and use recent program synthesis techniques to search for a solution, encoded as a program, subject to hypothesized constraints on the program \ structure.