Weak convergence and uniform normalization in infinitary rewriting

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Documents

We study infinitary term rewriting systems containing finitely many rules. For these, we show that if a weakly convergent reduction is not strongly convergent, it contains a term that reduces to itself in one step (but the step itself need not be part of the reduction). Using this result, we prove the starkly surprising result that for any orthogonal system with finitely many rules, the system is weakly normalizing under weak convergence if{f} it is strongly normalizing under weak convergence if{f} it is weakly normalizing under strong convergence if{f} it is strongly normalizing under strong convergence. As further corollaries, we derive a number of new results for weakly convergent rewriting: Systems with finitely many rules enjoy unique normal forms, and acyclic orthogonal systems are confluent. Our results suggest that it may be possible to recover some of the positive results for strongly convergent rewriting in the setting of weak convergence, if systems with finitely many rules are considered. Finally, we give a number of counterexamples showing failure of most of the results when infinite sets of rules are allowed.
Original languageEnglish
Title of host publicationProceedings of the 21st International Conference on Rewriting Techniques and Applications,
EditorsChristopher Lynch
Number of pages14
PublisherSchloss Dagstuhl - Leibniz-Zentrum für Informatik
Publication date2010
Pages311-324
ISBN (Electronic)978-3-939897-18-7
DOIs
Publication statusPublished - 2010
Event21st International Conference on Rewriting Techniques and Applications - Edinburgh, United Kingdom
Duration: 11 Jul 201013 Jul 2010

Conference

Conference21st International Conference on Rewriting Techniques and Applications
LandUnited Kingdom
ByEdinburgh
Periode11/07/201013/07/2010
SeriesLeibniz International Proceedings in Informatics (LIPIcs)
Volume6
ISSN1868-8969

Number of downloads are based on statistics from Google Scholar and www.ku.dk


No data available

ID: 32193329