Automatic Generation of Alternative Starting Positions for Simple Traditional Board Games
Authors: Umair Ahmed, Krishnendu Chatterjee, Sumit Gulwani
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present experimental results that include discovery of states of varying hardness levels for several simple gridbased board games. The presence of such states for standard game variants like 4 4 Tic-Tac-Toe opens up new games to be played that have never been played as the default start state is heavily biased. |
| Researcher Affiliation | Collaboration | Umair Z. Ahmed IIT Kanpur umair@iitk.ac.in Krishnendu Chatterjee IST Austria krishnendu.chatterjee@ist.ac.at Sumit Gulwani Microsoft Research, Redmond sumitg@microsoft.com |
| Pseudocode | No | The paper describes algorithms and methods but does not provide formal pseudocode blocks or algorithms labeled as such. |
| Open Source Code | No | The paper does not provide a direct link to open-source code for the methodology described, nor does it state that the code is available in supplementary materials or upon request. |
| Open Datasets | No | The paper refers to existing games and their rules but does not use or cite a publicly available dataset in the context of machine learning training, validation, or testing. |
| Dataset Splits | No | The paper does not discuss typical dataset splits like training, validation, and testing as it relates to machine learning models. Instead, it describes an approach to generate game states. |
| Hardware Specification | No | The paper mentions "running times" for experiments but does not specify any hardware details like CPU, GPU, or memory used. |
| Software Dependencies | No | The paper mentions "CUDD" tool, but does not provide a specific version number. No other software dependencies with version numbers are listed. |
| Experiment Setup | Yes | For the classification of a board position, we run the game between the depth-k1 vs the depth-k2 strategy 30 times. If player 1 wins (i) more than 2 3 times (20 times), then it is identified as easy (E); (ii) less than 1 3 times (10 times), then it is identified as hard (H); (iii) else as medium (M). |