Limited Lookahead in Imperfect-Information Games
Authors: Christian Kroer, Tuomas Sandholm
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The impact of limited lookahead is then investigated experimentally. (Abstract) In this section we experimentally investigate how much utility can be gained by optimally exploiting a limited-lookahead player. (Section 6) |
| Researcher Affiliation | Academia | Christian Kroer and Tuomas Sandholm Computer Science Department Carnegie Mellon University ckroer@cs.cmu.edu, sandholm@cs.cmu.edu |
| Pseudocode | No | The paper describes mathematical formulations and algorithms in text and equations (e.g., MIP (12)), but does not provide a clearly labeled pseudocode block or algorithm. |
| Open Source Code | No | No explicit statement or link providing concrete access to source code for the methodology described in this paper. |
| Open Datasets | Yes | We conduct experiments on Kuhn poker [Kuhn, 1950], a canonical testbed for game-theoretic algorithms, and a larger simpliļ¬ed poker game that we call KJ. |
| Dataset Splits | No | The paper refers to game environments (Kuhn poker, KJ) as testbeds but does not specify data splits (e.g., train/validation/test percentages or counts) typically associated with machine learning datasets. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running experiments are provided. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., Python, PyTorch, CPLEX) are mentioned. |
| Experiment Setup | No | The paper describes the node evaluation heuristic and noise models, but does not provide specific experimental setup details such as hyperparameters (e.g., learning rate, batch size) or training configurations. |