Project description
The objective of AIM is the development and evaluation of new algorithmic approaches to inconsistency measurement. The latter is a research field concerned with the development of measures that quantitatively assess the severity of inconsistency in logical knowledge bases. Inconsistency measures can be used to compare different formalisations of knowledge, to help debug flawed knowledge bases, and guide automatic repair methods. For example, inconsistency measures have been used to estimate reliability of agents in multi-agent systems, to analyse inconsistencies in news reports, to support collaborative software requirements specifications, to allow for inconsistency-tolerant reasoning in probabilistic logic, and to monitor and maintain quality in database settings.
In AIM, we develop a series of algorithms for a wide range of inconsistency measures. In particular, we employ a diverse set of problem solving paradigms, such as satisfiability (SAT) solving, answer set programming (ASP), automated planning, and others, to approach the computation of inconsistency measures of various complexities and to find the best possible algorithms. We analyse the practical performance of these algorithms with in-depth experimental evaluations and create a comprehensive repository of benchmarks for future algorithmic studies. Achieving the overall objective of AIM to create practical systems for inconsistency measurement allows a more scalable application of inconsistency measures in the mentioned applications domains and beyond.
Project leader
- Matthias Thimm, University of Hagen
People
- Isabelle Kuhlmann, University of Hagen
Publications
- Jandson Santos Ribeiro Santos, Matthias Thimm. Measuring Inconsistency with the Tableau Method. In Journal of Applied Logic. August 2023. bibtex pdf
- Andreas Niskanen, Isabelle Kuhlmann, Matthias Thimm, Matti Järvisalo. MaxSAT-Based Inconsistency Measurement. In Proceedings of the 26th European Conference on Artificial Intelligence (ECAI'23). September 2023. bibtex pdf
- Isabelle Kuhlmann, Andreas Niskanen, Matti Järvisalo. Algorithms for Inconsistency Measures Based on Minimal Unsatisfiability. In Proceedings of 18th European Conference on Logics in Artificial Intelligence (JELIA'23). September 2023.
- Carl Corea, Isabelle Kuhlmann, Matthias Thimm, John Grant. Measuring and Resolving Inconsistency in Declarative Process Specifications (Extended Abstract). In AAAI 2023 Bridge Program on Artificial Intelligence and Business Process Management. February 2023. bibtex pdf
- Isabelle Kuhlmann, Anna Gessler, Vivien Laszlo, Matthias Thimm. A Comparison of ASP-Based and SAT-Based Algorithms for the Contension Inconsistency Measure. In Proceedings of the 15th international conference on Scalable Uncertainty Management (SUM'22). October 2022. bibtex pdf
- Isabelle Kuhlmann, Matthias Thimm. Algorithms for Inconsistency Measurement using Answer Set Programming. In Leila Amgoud, Richard Booth (Eds.), Proceedings of the 19th International Workshop on Non-Monotonic Reasoning (NMR'21), pages 159-168. November 2021. bibtex pdf
-
Isabelle Kuhlmann, Matthias Thimm. An Algorithm for the Contension Inconsistency Measure using Reductions to Answer Set Programming. In Jesse Davis, Karim Tabia (Eds.), Proceedings of the 14th International Conference on Scalable Uncertainty Management (SUM'20), pages 289-296, Springer International Publishing, volume 12322 of Lecture Notes in Artificial Intelligence. September 2020.
Winner of best student paper award bibtex pdf