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Combining Runtime Verification and Property Adaptation through Neural-Symbolic Integration

We propose a framework for combining runtime verification and learning in connectionist models. This framework is expected to offer an improved efficiency of the verification process through parallel computation, and a new mechanism of compliance with soft-constraints through learning (adaptation). The goal of business process adaptation is to discover, monitor and improve real processes by extracting knowledge from real-time event logs readily available in today’s information systems. Within this wider framework, we have developed a run-time verification system for linear temporal logic, called RuleRunner, which we have designed explicitly with the goal of translating into a neural network for parallel computation and learning. In this paper, using results from neural-symbolic integration, we introduce the translation algorithm that enables the encoding of any RuleRunner specifications into standard CILP recurrent neural networks. The obtained network is shown to converge to the evaluation status of RuleRunner for each cell of the trace. Initial experimental results, also reported in this paper, indicate that the sparse version of the network representation scales better than the symbolic implementation of RuleRunner. The resulting system is a neural network capable of efficient runtime verification and offering well-known learning capabilities, which are being investigated. The framework will be applied to the adaptation of business processes capable of tolerating soft-violations occurring in real practice.