edoc

Generalized label reduction for merge-and-shrink heuristics

Sievers, Silvan and Wehrle, Martin and Helmert, Malte. (2014) Generalized label reduction for merge-and-shrink heuristics. In: Proceedings of twenty-eighth AAAI Conference on Artificial Intelligence and the twenty-sixth Innovative Applications of Artificial Intelligence Conference (AAAI 2014) : 27-31 July 2014, Québec City, Québec, Canada, 3. Palo Alto, Calif., pp. 2358-2366.

[img]
Preview
PDF - Published Version
324Kb

Official URL: http://edoc.unibas.ch/dok/A6328875

Downloads: Statistics Overview

Abstract

Label reduction is a technique for simplifying families of labeled transition systems by dropping distinctions between certain transition labels. While label reduction is critical to the efficient computation of merge-and-shrink heuristics, current theory only permits reducing labels in a limited number of cases. We generalize this theory so that labels can be reduced in every intermediate abstraction of a merge-and-shrink tree. This is particularly important for efficiently computing merge-and-shrink abstractions based on non-linear merge strategies. As a case study, we implement a non-linear merge strategy based on the original work on merge-and-shrink heuristics in model checking by Dräger et al.
Faculties and Departments:05 Faculty of Science > Departement Mathematik und Informatik > Informatik > Artificial Intelligence (Helmert)
UniBasel Contributors:Sievers, Silvan and Wehrle, Martin and Helmert, Malte
Item Type:Conference or Workshop Item, refereed
Conference or workshop item Subtype:Conference Paper
Bibsysno:Link to catalogue
Publisher:AAAI Press
Note:Publication type according to Uni Basel Research Database: Conference paper
Language:English
Related URLs:
Last Modified:20 Nov 2018 09:59
Deposited On:05 Jun 2015 08:52

Repository Staff Only: item control page