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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Formal Verification of Parameterised Neural-symbolic Multi-agent Systems
Authors: Panagiotis Kouvaros, Elena Botoeva, Cosmo De Bonis-Campbell
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present an implementation and discuss experimental results obtained on a social dilemma game based on guarding. |
| Researcher Affiliation | Academia | Panagiotis Kouvaros1 , Elena Botoeva2 and Cosmo De Bonis-Campbell2 1Technology and Innovation School, University of Limassol 2School of Computing, University of Kent |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions using the "VENMAS toolkit [Akintunde et al., 2020b]" but does not state that the authors are releasing their own code for the methodology described in this paper. |
| Open Datasets | No | The paper describes training a neural observation function within a simulated game environment, but it does not provide concrete access information for a publicly available or open dataset. |
| Dataset Splits | No | The paper describes training an agent in a simulated game but does not specify dataset splits (e.g., train/validation/test percentages or counts) needed to reproduce data partitioning. |
| Hardware Specification | Yes | The experiments were performed on a standard PC running Ubuntu 22.04 with 16GB RAM and processor Intel(R) Core i5-4460. |
| Software Dependencies | Yes | The experiments were performed on a standard PC running Ubuntu 22.04 with 16GB RAM and processor Intel(R) Core i5-4460. We relied on Gurobi v10.0 [Gu et al., 2016] to solve the mixed integer linear programs generated by VENMAS. |
| Experiment Setup | Yes | During the training, the game was played by 4 agents, and the parameters were set as Mh = 4, Gr = 2, Rr = 1 and Ur = 3. The produced neural network has two hidden layers of four Re LU activated neurons, takes as input a single neuron, representing the normalised health points of the agent, and outputs the estimated Q-values of the two actions rest and guard . |