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 | Conference PDF | Archive PDF | Plain Text | 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 andres.abeliuk@data61.csiro.au Haris Aziz Data61/NICTA and UNSW haris.aziz@data61.csiro.au Gerardo Berbeglia University of Melbourne g.berbeglia@mbs.edu Serge Gaspers UNSW and Data61/NICTA sergeg@cse.unsw.edu.au Petr Kalina Czech Technical University petr.kalina@fel.cvut.cz Nicholas Mattei Data61/NICTA and UNSW nicholas.mattei@data61.csiro.au Dominik Peters University of Oxford dominik.peters@cs.ox.ac.uk Paul Stursberg Technische Universit at M unchen paul.stursberg@ma.tum.de Pascal Van Hentenryck University of Michigan pvanhent@umich.edu Toby Walsh UNSW and Data61/NICTA tw@cse.unsw.edu.au |
| 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. |