Bongard Architecture: Towards Scalable and Transparent Machine Reasoning

Tetiana Grinberg, Dmitry Grinberg, Grigory Shcherbanskiy, Lev Cherbanski

Keywords: machine reasoning, artificial intelligence

TL;DR: We revive the approach to solving Bongard problems proposed by their inventor M. Bongard, and demonstrate its feasibility.

Abstract: Bongard problems, a collection of 100 image puzzles first introduced by Mikhail Bongard, have posed a long-standing challenge for traditional machine learning architectures. Despite their apparent simplicity, these puzzles demand explainability, reasoning and pattern recognition skills. Our work revives the approach to solving these problems proposed by Bongard's original research group, which has not been re-implemented or validated since. We demonstrate that this approach is able to successfully solve 100% of the problems in the Maksimov-Bongard problems (MBP) dataset which is explicitly constrained to contain only geometric puzzles. We extend the vocabulary of operators to demonstrate scalability of the architecture, yielding solutions to a considerable portion (44%) of the more widely known Bongard problems (BP) dataset. Finally, we argue for the value of Bongard-style architectures in AI applications that demand complex human-machine communication, transparency and compatibility with human cognitive processes.