Lecture: A Model-Oriented Approach for Lifting Symmetries in Answer Set Progra...
Master data
Title: | A Model-Oriented Approach for Lifting Symmetries in Answer Set Programming |
Description: | When solving combinatorial problems, pruning symmetric solution candidates from the search space is essential. Most of the existing approaches are instance-specific and focus on the automatic computation of Symmetry Breaking Constraints (SBCs) for each given problem instance. However, the application of such approaches to large-scale instances or advanced problem encodings might be problematic since the computed SBCs are propositional and, therefore, can neither be meaningfully interpreted nor transferred to other instances. As a result, a time-consuming recomputation of SBCs must be done before every invocation of a solver. To overcome these limitations, we introduce a new model-oriented approach for Answer Set Programming that lifts the SBCs of small problem instances into a set of interpretable first-order constraints using a form of machine learning called Inductive Logic Programming. After targeting simple combinatorial problems, we aim to extend our method to be applied also for advanced decision and optimization problems. |
Keywords: | General Earth and Planetary Sciences, General Engineering, General Environmental Science |
Type: | Registered lecture |
Homepage: | https://sites.google.com/view/iclpdc2022/iclp-2022-doctoral-consortium?pli=1 |
Event: | 18th Logic Programming Doctoral Consortium (DC) ICLP 2022 (Haifa) |
Date: | 02.08.2022 |
lecture status: | stattgefunden (Präsenz) |
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Fakultät für Technische Wissenschaften
Institut für Artificial Intelligence und Cybersecurity
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AT - A-9020 Klagenfurt |
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