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A Semantic Web Technique as Logical Inference Puzzle-Solver for Bongard Problems

Bongard Problems (BPs) are a set of 100 visual puzzles set as a benchmark test for understanding the human concept learning abilities depending on contexts. It is still obscure how the human mind recognizes objects under minimal information, like amodal problems where only minimal information are available as sensory input. Though BPs have been well known among AI researchers as solutions towards understanding such human visual perception abilities, only a little progress has been made towards solving a set of BPs. In this study, a semantic web based meta-knowledge was developed along with a hierarchical logical inference to mimic human-logical inference ability in solving the Bongard problems. We applied this method to solve a set of fourteen BPs based on visual inputs from a perceiver (through an interface) and successfully demonstrated the system to find the unique distinction between the two classes in a given problem.