Paper accepted at the European Conference on Artificial Intelligence (ECAI)

Progression for Monitoring in Temporal ASP


In recent years, there has been growing interest in the application of temporal reasoning approaches and non-monotonic logics from artificial intelligence in dynamic systems that generate data. A well-known approach to temporal reasoning is the use of a progression technique, which allows for the online computation of logical consequences of a logical knowledge base over time. We consider a progression technique for Temporal Here and There and Temporal Equilibrium Logic, which is the logic underlying answer programming over linear-temporal logic (LTL). Compared to usual LTL online computation, where the goal is to check whether a trace is compliant with a temporal specification, our approach provides also the means to compute non-monotonic temporal reasoning over a trace of observations. Besides formal notions and results, we also present an algorithm for performing progression to monitor a dynamic system, which has been implemented as a proof of concept and allows for handling expressive application scenarios.

Ignacio D. Lopez-Miguel
Ignacio D. Lopez-Miguel
PhD student

I am a PhD student at the Technical University of Vienna (TU Wien)