@article{Maggi_Montali_Peñaloza_2020, title={Temporal Logics Over Finite Traces with Uncertainty}, volume={34}, url={https://ojs.aaai.org/index.php/AAAI/article/view/6583}, DOI={10.1609/aaai.v34i06.6583}, abstractNote={<p>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.</p>}, number={06}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Maggi, Fabrizio M and Montali, Marco and Peñaloza, Rafael}, year={2020}, month={Apr.}, pages={10218-10225} }