Towards Epistemic-Doxastic Planning with Observation and Revision
Authors: Thorsten Engesser, Andreas Herzig, Elise Perrotin
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | 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 thorsten.engesser@irit.fr, andreas.herzig@irit.fr, perrotin@cril.fr |
| 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. |