Stream Reasoning using Temporal Logic and Predictive Probabilistic State Models
2016 (English)In: 23nd International Symposium on Temporal Representation and Reasoning (TIME), 2016, IEEE Computer Society, 2016, 196-205 p.Conference paper (Refereed)
Integrating logical and probabilistic reasoning and integrating reasoning over observations and predictions are two important challenges in AI. In this paper we propose P-MTL as an extension to Metric Temporal Logic supporting temporal logical reasoning over probabilistic and predicted states. The contributions are (1) reasoning over uncertain states at single time points, (2) reasoning over uncertain states between time points, (3) reasoning over uncertain predictions of future and past states and (4) a computational environment formalism that ground the uncertainty in observations of the physical world. Concrete robot soccer examples are given.
Place, publisher, year, edition, pages
IEEE Computer Society, 2016. 196-205 p.
temporal logic, robotics, execution monitoring, estimation, prediction, probabilistic logic, probabilistic robotics, autonomous systems, situation awareness
Computer Science Computer Vision and Robotics (Autonomous Systems)
IdentifiersURN: urn:nbn:se:liu:diva-132319DOI: 10.1109/TIME.2016.28ISI: 000390560600024ISBN: 978-1-5090-3825-1 (print)OAI: oai:DiVA.org:liu-132319DiVA: diva2:1043981
Temporal Representation and Reasoning (TIME)
FunderCUGS (National Graduate School in Computer Science)ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Presented at the 23nd International Symposium on Temporal Representation and Reasoning (TIME) at the Technical University of Denmark (DTU), Denmark, the 19th October 2016.2016-11-012016-10-312017-01-26