  Algebraic data types (ADTs) are a construct classically found in functional
programming languages that capture data structures like enumerated types,
lists, and trees. In recent years, interest in ADTs has increased. For example,
popular programming languages, like Python, have added support for ADTs.
Automated reasoning about ADTs can be done using satisfiability modulo theories
(SMT) solving, an extension of the Boolean satisfiability problem with
constraints over first-order structures. Unfortunately, SMT solvers that
support ADTs do not scale as state-of-the-art approaches all use variations of
the same \emph{lazy} approach. In this paper, we present an SMT solver that
takes a fundamentally different approach, an \emph{eager} approach.
Specifically, our solver reduces ADT queries to a simpler logical theory,
uninterpreted functions (UF), and then uses an existing solver on the reduced
query. We prove the soundness and completeness of our approach and demonstrate
that it outperforms the state-of-theart on existing benchmarks, as well as a
new, more challenging benchmark set from the planning domain.
