  The Abstraction and Reasoning Corpus (ARC) aims at benchmarking the
performance of general artificial intelligence algorithms. The ARC's focus on
broad generalization and few-shot learning has made it difficult to solve using
pure machine learning. A more promising approach has been to perform program
synthesis within an appropriately designed Domain Specific Language (DSL).
However, these too have seen limited success. We propose Abstract Reasoning
with Graph Abstractions (ARGA), a new object-centric framework that first
represents images using graphs and then performs a search for a correct program
in a DSL that is based on the abstracted graph space. The complexity of this
combinatorial search is tamed through the use of constraint acquisition, state
hashing, and Tabu search. An extensive set of experiments demonstrates the
promise of ARGA in tackling some of the complicated object-centric tasks of the
ARC rather efficiently, producing programs that are correct and easy to
understand.
