Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Interdependent Scheduling Games
Authors: Andres Abeliuk, Haris Aziz, Gerardo Berbeglia, Serge Gaspers, Petr Kalina, Nicholas Mattei, Dominik Peters, Paul Stursberg, Pascal Van Hentenryck, Toby Walsh
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We implemented the ILP and solved 1000 randomly generated instances where (a) general rewards are drawn from [50,100] and (b) rewards are uniform. The dependency graphs are generated by first randomly permuting the list of all services; then for each service i, drawing a random number of child services c 2 {0, 1, 2} and adding edge (i, i + c) with probability 0.5. Increasing the number/likelihood of dependencies by increasing the potential number of children or increasing the connection probability significantly increases runtime. Figure 1 shows the results for different parameters using Gurobi 6.5 on a computer equipped with an 2.0 GHz Intel Xeon E5405 CPU with 4 GB of RAM. |
| Researcher Affiliation | Collaboration | Andres Abeliuk Data61/NICTA EMAIL Haris Aziz Data61/NICTA and UNSW EMAIL Gerardo Berbeglia University of Melbourne EMAIL Serge Gaspers UNSW and Data61/NICTA EMAIL Petr Kalina Czech Technical University EMAIL Nicholas Mattei Data61/NICTA and UNSW EMAIL Dominik Peters University of Oxford EMAIL Paul Stursberg Technische Universit at M unchen EMAIL Pascal Van Hentenryck University of Michigan EMAIL Toby Walsh UNSW and Data61/NICTA EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper states: 'We implemented the ILP and solved 1000 randomly generated instances'. This indicates custom generated data, not a publicly available dataset with specific access information. |
| Dataset Splits | No | The paper mentions 'randomly generated instances' but does not provide specific dataset split information (like percentages or counts for training, validation, or test sets). |
| Hardware Specification | Yes | Figure 1 shows the results for different parameters using Gurobi 6.5 on a computer equipped with an 2.0 GHz Intel Xeon E5405 CPU with 4 GB of RAM. |
| Software Dependencies | Yes | Figure 1 shows the results for different parameters using Gurobi 6.5 on a computer equipped with an 2.0 GHz Intel Xeon E5405 CPU with 4 GB of RAM. |
| Experiment Setup | Yes | We implemented the ILP and solved 1000 randomly generated instances where (a) general rewards are drawn from [50,100] and (b) rewards are uniform. The dependency graphs are generated by first randomly permuting the list of all services; then for each service i, drawing a random number of child services c 2 {0, 1, 2} and adding edge (i, i + c) with probability 0.5. Increasing the number/likelihood of dependencies by increasing the potential number of children or increasing the connection probability significantly increases runtime. |