Temporal Logics Over Finite Traces with Uncertainty (Technical Report)
This addresses the limitation of classical temporal logics in real-life dynamic systems with uncertainty, particularly for applications like business process modelling and planning, though it appears incremental as it builds on existing logics and techniques.
The authors tackled the problem of handling uncertainty in dynamic systems by proposing a new probabilistic temporal logic over finite traces with superposition semantics, where all possible evolutions remain possible until observed, and they developed automata-based mechanisms for probabilistic inferences and identified a computationally efficient fragment that can be discovered from event log data using existing techniques.
Temporal logics over finite traces have recently seen wide application in a number of areas, from business process modelling, monitoring, and mining to planning and decision making. However, real-life dynamic systems contain a degree of uncertainty which cannot be handled with classical logics. We thus propose a new probabilistic temporal logic over finite traces using superposition semantics, where all possible evolutions are possible, until observed. We study the properties of the logic and provide automata-based mechanisms for deriving probabilistic inferences from its formulas. We then study a fragment of the logic with better computational properties. Notably, formulas in this fragment can be discovered from event log data using off-the-shelf existing declarative process discovery techniques.