Towards a Solution to Bongard Problems: A Causal Approach
This addresses a specific, long-standing challenge in AI for researchers interested in symbolic reasoning and interpretability, but it appears incremental as it builds on existing RL and contrastive learning techniques.
The paper tackles the unsolved class of Bongard Problems by reformulating them into a reinforcement learning setting to access counterfactuals and using contrastive learning for feature extraction, resulting in experiments that analyze the setup and interpret the feature space.
Even though AI has advanced rapidly in recent years displaying success in solving highly complex problems, the class of Bongard Problems (BPs) yet remain largely unsolved by modern ML techniques. In this paper, we propose a new approach in an attempt to not only solve BPs but also extract meaning out of learned representations. This includes the reformulation of the classical BP into a reinforcement learning (RL) setting which will allow the model to gain access to counterfactuals to guide its decisions but also explain its decisions. Since learning meaningful representations in BPs is an essential sub-problem, we further make use of contrastive learning for the extraction of low level features from pixel data. Several experiments have been conducted for analyzing the general BP-RL setup, feature extraction methods and using the best combination for the feature space analysis and its interpretation.