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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
First-Order Coalition Logic
Authors: Davide Catta, Rustam Galimullin, Aniello Murano
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We introduce First-Order Coalition Logic (FOCL)... we provide a sound and complete axiomatisation of FOCL... While discussing the satisfiability problem for FOCL, we reopen the question of the recursive axiomatisability of SL... The rest of the paper is structured as follows: Section 2 defines the syntax and semantics of FOCL, Section 3 examines its expressiveness, Section 4 presents a complete axiomatisation of FOCL, Section 5 addresses complexity, and Section 6 concludes with directions for future work. |
| Researcher Affiliation | Academia | Davide Catta1, Rustam Galimullin2 and Aniello Murano3 1LIPN, CNRS UMR 7030, Universit e Sorbonne Paris Nord, Villetaneuse, France 2University of Bergen, Norway 3University of Naples Federico II, Italy EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 An algorithm for model checking FOCL 1: procedure MC(G, s, φ) 2: case φ = ((a1, ..., an)) ψ 3: guess t S such that s, a1, ..., an, t R 4: return MC(G, t, ψ) 5: case φ = xψ 6: guess a Ac 7: return MC(G, s, ψ[a/x]) 8: case φ = xψ 9: universally choose a Ac 10: return MC(G, s, ψ[a/x]) 11: end procedure |
| Open Source Code | No | The paper does not contain an explicit statement about releasing code, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper is theoretical and introduces a logic. It uses conceptual examples like Concurrent Game Structures (CGSs) in Figure 1 for illustration, not as empirical datasets for experiments. No publicly available or open datasets are used or referenced. |
| Dataset Splits | No | The paper does not involve experiments with empirical datasets; therefore, there is no mention of dataset splits. |
| Hardware Specification | No | The paper focuses on theoretical logic and complexity analysis. There is no mention of any specific hardware (e.g., CPU, GPU, memory) used to run experiments or computations. |
| Software Dependencies | No | The paper is theoretical and does not describe implementation details that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper does not present any experimental results or evaluations. Consequently, there are no details regarding an experimental setup, hyperparameters, or training configurations. |