Logic Negation with Spiking Neural P Systems
This work addresses the interpretability gap in neural network reasoning for AI researchers, though it appears incremental as it applies an existing model to a specific logic task.
The paper tackles the problem of making neural network reasoning more interpretable by characterizing two logic inference rules for negation, Closed World Assumption and Negation as Finite Failure, using spiking neural P systems, a formal model of third-generation neural networks.
Nowadays, the success of neural networks as reasoning systems is doubtless. Nonetheless, one of the drawbacks of such reasoning systems is that they work as black-boxes and the acquired knowledge is not human readable. In this paper, we present a new step in order to close the gap between connectionist and logic based reasoning systems. We show that two of the most used inference rules for obtaining negative information in rule based reasoning systems, the so-called Closed World Assumption and Negation as Finite Failure can be characterized by means of spiking neural P systems, a formal model of the third generation of neural networks born in the framework of membrane computing.