In accordance with the previous conferences, the proceedings of ECSQARU 2021 will be published in the Springer LNCS/LNAI Lecture Notes in Artificial Intelligence series. Authors are requested to prepare their conference papers in the LNCS/LNAI format - please, follow Springer Instruction for authors. Submitted papers must be original and not under review in a journal or another venue with formally published proceedings. They will be evaluated by peer reviews based on originality, significance, technical soundness, and clarity of exposition. Authors of accepted papers are expected to attend the conference to present their work, at least one author of each paper must register for the conference.
Please read the following instructions carefully:
- authors should prepare their conference papers in the LNCS/LNAI format - please use Springer LaTeX template or MS Word template. You may be also interested in using Overleaf where respective templates are also available.
- papers must not exceed 12 pages, excluding references;
- the submissions are not blind and they should mention authors and affiliations
- papers must be submitted electronically through Easychair;
- Do not forget to attach a signed Consent to Publish required by Springer for publishing in Lecture Notes in Computer Sciences series with your final version submission.
Papers that are not in the stated format or that exceed the page limit will not be reviewed.
For ECSQARU 2021 we invite submissions of original papers on topics which include but are not limited to:
Algorithms for uncertain inference
Applications of uncertain systems
Automated planning and acting under uncertainty
Belief change & merging
Classification & clustering
Decision theory & decision graphs
Description logics with uncertainty
Foundations of reasoning under uncertainty
Fuzzy sets & fuzzy logic
Learning for uncertainty formalisms
Logics for reasoning under uncertainty
Markov decision processes
Possibility theory & possibilistic logic
Probabilistic graphical models
Qualitative uncertainty models
Uncertainty & data