- Titel
- Contextualized Programs for Ontology-Mediated Probabilistic System Analysis
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-796053
- Schriftenreihe
- LTCS-Report
- Bandnummer
- 18-09
- Erstveröffentlichung
- 2018
- DOI
- https://doi.org/10.25368/2022.244
- Abstract (EN)
- Modeling context-dependent systems for their analysis is challenging as verification tools usually rely on an input language close to imperative programming languages which need not support description of contexts well. We introduce the concept of contextualized programs where operational behaviors and context knowledge are modeled separately using domain-specific formalisms. For behaviors specified in stochastic guarded-command language and contextual knowledge given by OWL description logic ontologies, we develop a technique to efficiently incorporate contextual information into behavioral descriptions by reasoning about the ontology. We show how our presented concepts support and facilitate the quantitative analysis of context-dependent systems using probabilistic model checking. For this, we evaluate our implementation on a case study issuing a multi-server system.
- Freie Schlagwörter (DE)
- Ontologie, probabilistischer Logikprogrammierung, Beschreibungslogik, kontextualisiertes Lernen
- Freie Schlagwörter (EN)
- ontology, probabilistic logic programming, description logic, contextualized learning
- Klassifikation (DDC)
- 004
- Klassifikation (RVK)
- ST 136
- Publizierende Institution
- Technische Universität Dresden, Dresden
- Version / Begutachtungsstatus
- angenommene Version / Postprint / Autorenversion
- URN Qucosa
- urn:nbn:de:bsz:14-qucosa2-796053
- Veröffentlichungsdatum Qucosa
- 20.06.2022
- Dokumenttyp
- Bericht
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
CC BY 4.0