A Computationally Grounded Framework for Cognitive Attitudes
Authors: Tiago de Lima, Emiliano Lorini, Elise Perrotin, François Schwarzentruber
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present some experimental results for the implemented algorithm on computation time in a concrete example. ... Table 3: Model checker performance on Example 1. The execution times correspond to the elapsed time to model check Equation (8). |
| Researcher Affiliation | Academia | 1CRIL, Univ Artois and CNRS, Lens, France 2IRIT, CNRS, Toulouse University, France 3National Institute of Advanced Industrial Science and Technology, Tokyo, Japan 4ENS Lyon, LIP, France |
| Pseudocode | Yes | Definition 11. For all state symbols s, s , we define a function trs that maps any formula of L to a QBF-formula, and a function trprog s,s that maps any program π to a a QBF-formula by mutual induction as follows: ... and trprog s,s is given by ... |
| Open Source Code | Yes | Code https://gitlab.in2p3.fr/tiago.delima/cognitiveattitudes-source-code |
| Open Datasets | No | The paper uses a conceptual 'Example 1' to demonstrate and test the performance of its implemented model checker. This example defines a problem instance rather than utilizing an existing, publicly available dataset. |
| Dataset Splits | No | The paper evaluates a model checking algorithm using a conceptual example (Example 1) with varying parameters (e.g., number of agents). It does not involve typical machine learning datasets with explicit training, validation, or test splits. |
| Hardware Specification | Yes | It was compiled with GHC 9.4.8 in a Mac Book Air with a 1.6 GHz Dual-Core Intel Core i5 processor and 16,537 GB of RAM, running mac OS Sonoma 14.4.1. |
| Software Dependencies | Yes | we implemented a symbolic model checker for the static part of the language in Haskell ... which uses the reduction to TQBF. The resulting QBF-formula is solved using the binary decision diagram (BDD) library Has Cac BDD (Gattinger 2023). It was compiled with GHC 9.4.8 |
| Experiment Setup | No | The paper describes the setup for evaluating the performance of a model checker by varying the number of agents and atomic propositions in a concrete example (Example 1), as shown in Table 3. However, it does not provide specific hyperparameters or system-level training settings as typically found in machine learning experimental setups. |