Notice: 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. Full accuracy metrics and methodology are described in [1].
Imperfect-Information Games and Generalized Planning
Authors: Giuseppe De Giacomo, Aniello Murano, Sasha Rubin, Antonio Di Stasio
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our technical contribution (Theorem 4.5) is a general sound and complete mathematical technique for removing imperfect information from a possibly infinite game G to get a game Gβ, possibly infinite, of perfect information. |
| Researcher Affiliation | Academia | Giuseppe De Giacomo SAPIENZA Universit a di Roma Rome, Italy EMAIL Aniello Murano, Sasha Rubin, Antonio Di Stasio Universit a degli Studi di Napoli Federico II Naples, Italy firstEMAIL |
| 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 to source code for the methodology described. |
| Open Datasets | No | The paper uses a running example (Tree Chopping) from prior work for illustration, but does not use a publicly available or open dataset for empirical evaluation. It describes a theoretical formalization of the problem. |
| Dataset Splits | No | The paper focuses on theoretical contributions and does not involve empirical experiments with dataset splits for training, validation, and testing. |
| Hardware Specification | No | The paper focuses on theoretical contributions and does not report on any experimental setup requiring specific hardware details. |
| Software Dependencies | No | The paper focuses on theoretical contributions and does not report on any experimental setup requiring specific software dependencies with version numbers. |
| Experiment Setup | No | The paper focuses on theoretical contributions and does not describe an experimental setup with hyperparameters or training configurations. |