Neural Meta-Symbolic Reasoning and Learning

TL;DR

NEMESYS is a neural meta-symbolic system that uses differentiable reasoning to perform and learn meta-level reasoning tasks efficiently, mimicking human-like general intelligence.

Contribution

It introduces the first neural meta-symbolic system capable of meta reasoning and learning through differentiable forward-chaining in first-order logic.

Findings

NEMESYS can adapt to various tasks by modifying meta-level programs.

It can learn meta-level programs from examples.

Demonstrates self-introspection and reasoning about reasoning.

Abstract

Deep neural learning uses an increasing amount of computation and data to solve very specific problems. By stark contrast, human minds solve a wide range of problems using a fixed amount of computation and limited experience. One ability that seems crucial to this kind of general intelligence is meta-reasoning, i.e., our ability to reason about reasoning. To make deep learning do more from less, we propose the first neural meta-symbolic system (NEMESYS) for reasoning and learning: meta programming using differentiable forward-chaining reasoning in first-order logic. Differentiable meta programming naturally allows NEMESYS to reason and learn several tasks efficiently. This is different from performing object-level deep reasoning and learning, which refers in some way to entities external to the system. In contrast, NEMESYS enables self-introspection, lifting from object- to meta-level reasoning and vice versa. In our extensive experiments, we demonstrate that NEMESYS can solve different kinds of tasks by adapting the meta-level programs without modifying the internal reasoning system. Moreover, we show that NEMESYS can learn meta-level programs given examples. This is difficult, if not impossible, for standard differentiable logic programming