Stratified Strategy Selection for Unit Control in Real-Time Strategy Games
Authors: Levi H. S. Lelis
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results on a simulator of an RTS game shows that SSS employing either fixed or adaptive type systems is able to substantially outperform stateof-the-art search-based algorithms in combat scenarios with up to 100 units. |
| Researcher Affiliation | Academia | Levi H. S. Lelis Departamento de Inform atica, Universidade Federal de Vic osa, Brazil levi.lelis@ufv.br |
| Pseudocode | Yes | Algorithm 1 Stratified Strategy Selection |
| Open Source Code | No | The paper mentions 'Spar Craft codebase [Churchill and Buro, 2013]' and provides a link to 'github.com/davechurchill/ualbertabot/tree/master/Spar Craft', which refers to a third-party simulation environment used in the paper, not the source code for the proposed SSS/SSS+ methods themselves. There is no explicit statement or link indicating the release of the authors' own implementation code. |
| Open Datasets | Yes | Our experiments are run in Spar Craft, a simplified combat simulation environment of Blizzard s Star Craft [Churchill and Buro, 2013]. For each combat scenario we generate 1,000 start states as explained by Churchill and Buro [2013]. |
| Dataset Splits | No | The paper describes testing algorithms on 1,000 generated start states for various combat scenarios but does not specify traditional train/validation/test dataset splits with percentages, sample counts, or explicit splitting methodologies for reproducibility. |
| Hardware Specification | No | All experiments are run on 2.66 GHz machines. This is a general clock speed but lacks specific details such as CPU model, GPU, or memory for hardware specification. |
| Software Dependencies | No | The paper mentions using 'Spar Craft' as the simulation environment and implementing 'POE in Spar Craft s codebase', but it does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | All algorithms use the same set Σ = {NOKAV, Kiter, Cluster} of scripts, and have a time limit of 40 milliseconds for each decision point. For SSS we use Tc,3 and for SSS+ we use Y = {Tc,3, Tc,2, Tc,1, Tc,0, TRGD}... PGS s and PGS+ s improvement response parameter to 0... POE: population size of 36, with the 6 fittest individuals being selected for generating 5 offsprings each. Also, POE uses a playout-based evaluation function that is limited to 25 decision points. |