Comparing Differentiable Logics for Learning Systems: A Research Preview

• URL: http://arxiv.org/abs/2311.09809v1
• Date: Thu, 16 Nov 2023 11:33:08 GMT
• Title: Comparing Differentiable Logics for Learning Systems: A Research Preview
• Authors: Thomas Flinkow (Maynooth University), Barak A. Pearlmutter (Maynooth University), Rosemary Monahan (Maynooth University)
• Abstract summary: Research on formal verification of machine learning (ML) systems indicates that learning from data alone often fails to capture underlying background knowledge. A promising approach for creating ML models that inherently satisfy constraints is to encode background knowledge as logical constraints that guide the learning process via so-called differentiable logics. In this research preview, we compare and evaluate various logics from the literature in weakly-supervised contexts, presenting our findings and highlighting open problems for future work.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Extensive research on formal verification of machine learning (ML) systems indicates that learning from data alone often fails to capture underlying background knowledge. A variety of verifiers have been developed to ensure that a machine-learnt model satisfies correctness and safety properties, however, these verifiers typically assume a trained network with fixed weights. ML-enabled autonomous systems are required to not only detect incorrect predictions, but should also possess the ability to self-correct, continuously improving and adapting. A promising approach for creating ML models that inherently satisfy constraints is to encode background knowledge as logical constraints that guide the learning process via so-called differentiable logics. In this research preview, we compare and evaluate various logics from the literature in weakly-supervised contexts, presenting our findings and highlighting open problems for future work. Our experimental results are broadly consistent with results reported previously in literature; however, learning with differentiable logics introduces a new hyperparameter that is difficult to tune and has significant influence on the effectiveness of the logics.

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