AAAI Publications, Workshops at the Twenty-Sixth AAAI Conference on Artificial Intelligence

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Neural-Symbolic Rule-Based Monitoring
Alan Perotti, Artur d'Avila Garcez, Guido Boella, Daniele Rispoli

Last modified: 2012-07-15

Abstract


In this paper we present a neural-symbolic system for monitoring traces of observations in sofware systems. To this end, we define an algorithm that translates a RuleR rule-based monitoring system (RS) into a rule-based neural network system (RNNS). We then show how the RNNS can perform trace monitoring effectively and analyze its performance, reporting promising preliminary results. Finally, we discuss how network learning could be used within RNNS to embed the system into a framework for iterative verification and model adaptation. It is hoped that a tight integration of verification and adaptation within the neural-symbolic approach will help support the development of self-adapting, self-healing systems.

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