Learning Neuro-Symbolic World Models with Conversational Proprioception

Abstract

The recent emergence of Neuro-Symbolic Agent (NeSA) approaches to natural language-based interactions calls for the investigation of model-based approaches. In contrast to model-free approaches, which existing NeSAs take, learning an explicit world model has an interesting potential especially in the explainability, which is one of the key selling points of NeSA. To learn useful world models, we leverage one of the recent neuro-symbolic architectures, Logical Neural Networks (LNN). Here, we describe a method that can learn neuro-symbolic world models on the TextWorld-Commonsense set of games. We then show how this can be improved further by adding a proprioception that gives better tracking of the internal logic state and model. Also, the game-solving agents performance in a TextWorld setting shows a great advantage over the baseline with 85\% average steps reduction and $\times$2.3 average scoring.