Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
A Logic for Reasoning About Game Strategies
Authors: Dongmo Zhang, Michael Thielscher
AAAI 2015 | Venue PDF | LLM Run Details | Input Tokens: 15,980 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,071 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper introduces a modal logic for reasoning about game strategies. The logic is based on a variant of the well-known game description language for describing game rules and further extends it with two modalities for reasoning about actions and strategies. We develop an axiomatic system and prove its soundness and completeness with respect to a specific semantics based on the state transition model of games. |
| Researcher Affiliation | Academia | Dongmo Zhang The University of Western Sydney Australia EMAIL Michael Thielscher The University of New South Wales Australia EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository link, explicit statement of code release) for open-source code. |
| Open Datasets | No | This is a theoretical paper that does not involve the use of datasets for training or evaluation. |
| Dataset Splits | No | This is a theoretical paper and does not discuss training/validation/test dataset splits. |
| Hardware Specification | No | This is a theoretical paper and does not report on experiments that would require hardware specifications. |
| Software Dependencies | No | This is a theoretical paper and does not list specific software dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper and does not describe experimental setup details such as hyperparameters. |