Using the Shapley Value to Analyze Algorithm Portfolios
Authors: Alexandre Fréchette, Lars Kotthoff, Tomasz Michalak, Talal Rahwan, Holger Hoos, Kevin Leyton-Brown
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply the methodology outlined above to the SAT competition (SAT Competitions Website 2014) to assess the state of the art in SAT solving, and specifically, to fairly quantify the contributions of the participating solvers to the state of the art. We conducted experiments on solvers and problem instances from all SAT competitions for which the required performance data is publicly available the 2014, 2013, 2011 and 2009 competitions. |
| Researcher Affiliation | Academia | Alexandre Fr echette Dept. of Computer Science Univ. of British Columbia, Canada afrechet@cs.ubc.ca Lars Kotthoff Dept. of Computer Science Univ.y of British Columbia, Canada larsko@cs.ubc.ca Tomasz Michalak Univ. of Oxford, UK Univ. of Warsaw, Poland tomasz.michalak@cs.ox.ac.uk Talal Rahwan Masdar Inst. of Science and Technology Abu Dhabi, United Arab Emirates trahwan@gmail.com Holger H. Hoos Dept. of Computer Science Univ. of British Columbia, Canada hoos@cs.ubc.ca Kevin Leyton-Brown Dept.of Computer Science Univ. of British Columbia, Canada kevinlb@cs.ubc.ca |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide a specific link or explicit statement about the availability of the source code for the methodology described. |
| Open Datasets | Yes | We conducted experiments on solvers and problem instances from all SAT competitions for which the required performance data is publicly available the 2014, 2013, 2011 and 2009 competitions. |
| Dataset Splits | No | The paper describes the problem instances used from SAT competitions but does not specify any training/validation/test splits of these instances for its own analysis. It evaluates the contribution of solvers to the Virtual Best Solver on these competition instances. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers for the implementation of its methodology or analysis tools. It mentions specific SAT solvers as subjects of study, but not as dependencies for their own analysis software. |
| Experiment Setup | No | The paper defines a scoring function used as a characteristic function in the coalitional game, but it does not provide details on experimental setup in terms of hyperparameters, training configurations, or system-level settings for any model or algorithm they are training for their analysis. |