Decidable Verification of Golog Programs over Non-Local Effect Actions
Authors: Benjamin Zarrieß, Jens Claßen
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We contribute to the further exploration of the boundary between decidability and undecidability for Golog, showing that for our new classes of action theories in the two-variable fragment of first-order logic, verification of CTL properties of programs over ground actions is decidable. |
| Researcher Affiliation | Academia | Benjamin Zarrieß Theoretical Computer Science TU Dresden, Germany benjamin.zarriess@tu-dresden.de Jens Claßen Knowledge-Based Systems Group RWTH Aachen University, Germany classen@kbsg.rwth-aachen.de |
| Pseudocode | No | The paper does not contain any explicit pseudocode blocks or algorithms labeled as such. It presents definitions and mathematical formulations. |
| Open Source Code | No | The paper does not provide any statements about making source code for their methodology openly available, nor does it include a link to a code repository. |
| Open Datasets | No | The paper is theoretical and does not use or reference any datasets for training. Examples provided are illustrative, not empirical data. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation with dataset splits (training, validation, or test). Therefore, no such information is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require specific hardware. No hardware specifications are mentioned. |
| Software Dependencies | No | The paper describes theoretical frameworks (e.g., Situation Calculus, C2, ES logic) but does not mention any specific software dependencies with version numbers required for replication of empirical work, as no experiments are conducted. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details, hyperparameters, or training configurations, as no empirical experiments are conducted. |