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Great endeavors have been made to study AI's ability in abstract reasoning,along with which different versions of RAVEN's progressive matrices (RPM) areproposed as benchmarks. Previous works give inkling that without sophisticateddesign or extra meta-data containing semantic information, neural networks maystill be indecisive in making decisions regarding RPM problems, afterrelentless training. Evidenced by thorough experiments and ablation studies, weshowcase that end-to-end neural networks embodied with felicitous inductivebias, intentionally design or serendipitously match, can solve RPM problemselegantly, without the augment of any extra meta-data or preferences of anyspecific backbone. Our work also reveals that multi-viewpoint withmulti-evaluation is a key learning strategy for successful reasoning. Finally,potential explanations for the failure of connectionist models ingeneralization are provided. We hope that these results will serve asinspections of AI's ability beyond perception and toward abstract reasoning.Source code can be found in https://github.com/QinglaiWeiCASIA/RavenSolver.

Multi-Viewpoint and Multi-Evaluation with Felicitous Inductive Bias Boost Machine Abstract Reasoning Ability