asprin: Customizing Answer Set Preferences without a Headache
Authors: Gerhard Brewka, James Delgrande, Javier Romero, Torsten Schaub
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We then report results of evaluations we have performed.The asprin system We implemented asprin by means of the ASP-Python integration of clingo 4.4 (Gebser et al. 2014); it is publicly available at (potassco). In fact, Algorithm 1 and 2 are implemented in Python and interact with a continuously running clingo object through ground and solve functions. The current asprin library includes more and less as regards cardinality and weight, respectively, subset and superset, aso (Brewka et al. 2003), partial orders of (Giunchiglia and Maratea 2012), lexico, pareto, and and neg (Son and Pontelli 2006). Although asprin s unique characteristics lie in its flexibility and generality, let us investigate how much this costs confronted with dedicated implementations, and also analyze the effect of preference composition. To this end, we have gathered 193 benchmark instances from eight different classes: 15-Puzzle, Crossing, and Valves stem from the ASP competitions7 of 2009 and 2013; Ricochet Robots from (Gebser et al. 2013a), Circuit Diagnosis from (Siddiqi 2011) and adapted to ASP in (Gebser et al. 2013b), Metabolic Network Expansion from (Schaub and Thiele 2009), Transcription Network Repair from (Gebser et al. 2010), and Timetabling from (Banbara et al. 2013). |
| Researcher Affiliation | Academia | Gerhard Brewka Universit at Leipzig Leipzig, Germany brewka@informatik.uni-leipzig.de James Delgrande Simon Fraser University Burnaby, B.C., Canada jim@cs.sfu.ca Javier Romero Torsten Schaub Universit at Potsdam Potsdam, Germany {javier,torsten}@cs.uni-potsdam.de Affiliated with Inria Rennes, France, and SFU, Canada. |
| Pseudocode | Yes | Algorithm 1: solve Opt(P, s) and Algorithm 2: solve Opt All(P, s) |
| Open Source Code | No | The paper states 'The asprin system... is publicly available at (potassco)' and provides a URL 'http://potassco.sourceforge.net'. However, this is a general project website and not a direct link to a specific source code repository for the methodology described in the paper. |
| Open Datasets | Yes | 15-Puzzle, Crossing, and Valves stem from the ASP competitions7 of 2009 and 2013; Ricochet Robots from (Gebser et al. 2013a), Circuit Diagnosis from (Siddiqi 2011) and adapted to ASP in (Gebser et al. 2013b), Metabolic Network Expansion from (Schaub and Thiele 2009), Transcription Network Repair from (Gebser et al. 2010), and Timetabling from (Banbara et al. 2013). |
| Dataset Splits | No | The paper discusses the use of 193 benchmark instances from various sources but does not specify any explicit train/validation/test dataset splits, percentages, or methodology for partitioning the data. |
| Hardware Specification | Yes | Each run was restricted to 900s CPU time (on Intel Xeon dual-core processors with 3.4 GHz and 4 GB RAM under Linux). |
| Software Dependencies | Yes | We implemented asprin by means of the ASP-Python integration of clingo 4.4 (Gebser et al. 2014) |
| Experiment Setup | Yes | Each run was restricted to 900s CPU time (on Intel Xeon dual-core processors with 3.4 GHz and 4 GB RAM under Linux). and An effective way is to suppress memorized phase assignments (Pipatsrisawat and Darwiche 2007) among consecutive solve calls, as offered by clingo s option --forget. |