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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Towards Epistemic-Doxastic Planning with Observation and Revision
Authors: Thorsten Engesser, Andreas Herzig, Elise Perrotin
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We introduce a simple specification language for reasoning about actions with knowledge and belief. We demonstrate our approach on well-known false-belief tasks such as the Sally Anne Task and compare it to other action languages. Our logic leads to an epistemic planning formalism that is expressive enough to model second-order false-belief tasks, yet has the same computational complexity as classical planning. |
| Researcher Affiliation | Academia | Thorsten Engesser1, Andreas Herzig2, Elise Perrotin3 1IRIT, Toulouse, France 2IRIT, CNRS, Toulouse, France 3CRIL, CNRS, Lens, France EMAIL, EMAIL, EMAIL |
| Pseudocode | No | No pseudocode or clearly labeled algorithm blocks were found. |
| Open Source Code | No | No statement regarding open-source code availability or a link to a code repository was found. |
| Open Datasets | No | The paper uses the Sally-Anne Task as a conceptual example to demonstrate the formalism, not as a dataset for training or empirical evaluation. No public dataset information or access details were provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments requiring dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | No specific software dependencies with version numbers were mentioned. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or system-level training settings. |