  The Abstraction and Reasoning Corpus (ARC) is a general artificial
intelligence benchmark that poses difficulties for pure machine learning
methods due to its requirement for fluid intelligence with a focus on reasoning
and abstraction. In this work, we introduce an ARC solver, Generalized Planning
for Abstract Reasoning (GPAR). It casts an ARC problem as a generalized
planning (GP) problem, where a solution is formalized as a planning program
with pointers. We express each ARC problem using the standard Planning Domain
Definition Language (PDDL) coupled with external functions representing
object-centric abstractions. We show how to scale up GP solvers via domain
knowledge specific to ARC in the form of restrictions over the actions model,
predicates, arguments and valid structure of planning programs. Our experiments
demonstrate that GPAR outperforms the state-of-the-art solvers on the
object-centric tasks of the ARC, showing the effectiveness of GP and the
expressiveness of PDDL to model ARC problems. The challenges provided by the
ARC benchmark motivate research to advance existing GP solvers and understand
new relations with other planning computational models. Code is available at
github.com/you68681/GPAR.
