Large-Neighbourhood Search for Optimisation in Answer-Set Solving
Authors: Thomas Eiter, Tobias Geibinger, Nelson Higuera Ruiz, Nysret Musliu, Johannes Oetsch, Daria Stepanova5616-5625
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally demonstrate the effectiveness of clingo-lns on different benchmark problems.4 Unless stated otherwise, clingo was called with no additional command-line parameters, i.e, it uses a single solving thread and employs branch-and-bound-based optimisation. |
| Researcher Affiliation | Collaboration | 1 Institute for Logic and Computation, TU Wien, Favoritenstraße 9 11, 1040 Vienna, Austria, 2 CD-Lab Artis, TU Wien, Favoritenstraße 9 11, 1040 Vienna, Austria, 3 Bosch Center for AI, Robert Bosch Campus 1, 71272 Renningen, Germany |
| Pseudocode | Yes | Algorithm 1: LNS optimisation for a minimisation problem |
| Open Source Code | Yes | Our Python implementation of LNS with ASP in the loop, the solver clingo-lns, is publicly available.3 3http://www.kr.tuwien.ac.at/research/projects/bai/aaai22.zip. |
| Open Datasets | Yes | Weighted Strategic Companies... the instances are those of the 3rd (Calimeri, Ianni, and Ricca 2014), 4th (Alviano et al. 2013) and 5th (Calimeri et al. 2016) ASP Competition with random weights from [1, 1000] added. |
| Dataset Splits | No | The paper describes using |
| Hardware Specification | Yes | All experiments were run on a cluster with 13 nodes, each having 2 Intel Xeon CPUs E5-2650 v4 (max. 2.90GHz, 12 physical cores, no hyperthreading), with memory limit 20GB. |
| Software Dependencies | Yes | We used clingo v 5.5.1 and clingo-dl v 1.2.1. |
| Experiment Setup | Yes | The time limit to explore individual neighbourhoods was 20 seconds. The size of each neighbourhood was set to relax about 80% of the atoms over plays/3. |