Combining Compact Representation and Incremental Generation in Large Games with Sequential Strategies
Authors: Branislav Bosansky, Albert Xin Jiang, Milind Tambe, Christopher Kiekintveld
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally compare CS-DO with the standard approaches and analyze the impact of the size of the support on the performance of the algorithms. Results show that CS-DO dramatically improves the convergence rate in games with non-trivial support. |
| Researcher Affiliation | Academia | Branislav Boˇsansk y1,2, Albert Xin Jiang3, Milind Tambe4, Christopher Kiekintveld5 1 Agent Technology Center, Faculty of Electrical Engineering, Czech Technical University in Prague 2 Department of Computer Science, Aarhus University 3 Department of Computer Science, Trinity University, San Antonio, Texas 4 Computer Science Department, University of Southern California, Los Angeles 5 Computer Science Department, University of Texas at El Paso |
| Pseudocode | Yes | Figure 2: The best-response algorithm for player 1. |
| Open Source Code | No | The paper does not provide any concrete access to source code (no specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described. |
| Open Datasets | No | The paper uses variants of search games (Transit Game, Border Protection Game) inspired by existing games, citing prior work. However, it does not provide concrete access information (specific link, DOI, repository name, formal citation with authors/year, or reference to established benchmark datasets) for a publicly available or open dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. It evaluates custom game scenarios without mentioning train/validation/test splits. |
| Hardware Specification | No | The paper only states: 'Experiments were run using a single thread on a standard PC.' This does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | Yes | We used IBM CPLEX 12.5 to solve the linear programs. |
| Experiment Setup | Yes | Each of the algorithms was given a maximum of 7 GB of memory for a process |