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].
On Verifying Expectations and Observations of Intelligent Agents
Authors: Sourav Chakraborty, Avijeet Ghosh, Sujata Ghosh, Franรงois Schwarzentruber
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we investigate the computational complexity of the model checking problem for POL and prove its PSPACE-completeness. We also study various syntactic fragments of POL. We exemplify the applicability of POL model checking in verifying different characteristics and features of an interactive system with respect to the distinct expectations and (matching) observations of the system. Finally, we provide a discussion on the implementation of the model checking algorithms. |
| Researcher Affiliation | Academia | 1Indian Statistical Institute, Kolkata 2Indian Statistical Institute, Chennai 3Univ Rennes, IRISA, France |
| Pseudocode | Yes | Algorithm 1 mc POL Input: M = S, , V, Exp , s S, ฯ Output: True iff M, s ฯ |
| Open Source Code | Yes | (for details, see [Chakraborty et al., 2022]and https://github.com/francoisschwarzentruber/polmc). |
| Open Datasets | No | The paper uses illustrative examples (e.g., traffic light, message passing, farming drone) but does not use or refer to any publicly available datasets for empirical training. |
| Dataset Splits | No | No empirical experiments are conducted on datasets; therefore, there is no discussion of training, validation, or test splits. |
| Hardware Specification | No | The paper discusses algorithms and complexity but does not provide any specific details about the hardware used for computation or experiments. |
| Software Dependencies | No | The paper discusses implementation strategies but does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | As a theoretical paper focusing on complexity and algorithms, there are no experimental setup details like hyperparameters or training configurations provided. |