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Incremental Reasoning in Probabilistic Signal Temporal Logic
Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering. (KPLAB)ORCID iD: 0000-0002-8546-4431
Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering. (KPLAB)ORCID iD: 0000-0002-9595-2471
2020 (English)In: International Journal of Approximate Reasoning, ISSN 0888-613X, E-ISSN 1873-4731, Vol. 119, p. 325-352, article id j.ijar.2020.01.009Article in journal (Refereed) Published
Abstract [en]

Robot safety is of growing concern given recent developments in intelligent autonomous systems. For complex agents operating in uncertain, complex and rapidly-changing environments it is difficult to guarantee safety without imposing unrealistic assumptions and restrictions. It is therefore necessary to complement traditional formal verification with monitoring of the running system after deployment. Runtime verification can be used to monitor that an agent behaves according to a formal specification. The specification can contain safety-related requirements and assumptions about the environment, environment-agent interactions and agent-agent interactions. A key problem is the uncertain and changing nature of the environment. This necessitates requirements on how probable a certain outcome is and on predictions of future states. We propose Probabilistic Signal Temporal Logic (ProbSTL) by extending Signal Temporal Logic with a sub-language to allow statements over probabilities, observations and predictions. We further introduce and prove the correctness of the incremental stream reasoning technique progression over well-formed formulas in ProbSTL. Experimental evaluations demonstrate the applicability and benefits of ProbSTL for robot safety.

Place, publisher, year, edition, pages
Elsevier, 2020. Vol. 119, p. 325-352, article id j.ijar.2020.01.009
Keywords [en]
Knowledge representation Stream reasoning Incremental reasoning Probabilistic logic Temporal logic Runtime verification
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:liu:diva-163327DOI: 10.1016/j.ijar.2020.01.009ISI: 000517653700018OAI: oai:DiVA.org:liu-163327DiVA, id: diva2:1390395
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)CUGS (National Graduate School in Computer Science)Swedish Research CouncilELLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsLinnaeus research environment CADICS
Note

Funding agencies: National Graduate School in Computer Science, Sweden (CUGS); Swedish Research Council (VR) Linnaeus Center CADICSSwedish Research Council; ELLIIT Excellence Center at Linkoping-Lund for Information Technology; Wallenberg AI, Autonomous Systems and Softwar

Available from: 2020-01-31 Created: 2020-01-31 Last updated: 2020-03-29

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