A Probabilistic Logic for Resource-Bounded Multi-Agent Systems
Authors: Hoang Nga Nguyen, Abdur Rakib
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we propose a logic for reasoning about coalitional power under resource constraints in the probabilistic setting. We extend RB-ATL with probabilistic reasoning and provide a standard algorithm for the model-checking problem of the resulting logic Probabilistic Resource-Bounded ATL (p RB-ATL). |
| Researcher Affiliation | Academia | 1Institute for Future Transport and Cities, Coventry University, UK 2Computer Science Research Centre, University of the West of England, UK |
| Pseudocode | No | While the paper describes an algorithm for model checking in Section 4, it does so using mathematical equations and textual explanations rather than a formally structured pseudocode block or algorithm figure. |
| Open Source Code | No | The paper does not provide any links to open-source code for the methodology described. |
| Open Datasets | No | The paper uses an illustrative example system ('p RCGS Sff of the two fire-fighters') rather than a publicly available dataset for its computational demonstration. |
| Dataset Splits | No | The paper uses an illustrative example system; therefore, it does not specify dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper mentions that 'the time taken for the veriļ¬cation was less than 1 second' for the small illustrative example, but it does not specify any hardware details such as CPU, GPU, or memory. |
| Software Dependencies | No | The paper refers to 'PRISM: Probabilistic Symbolic Model Checker' and mentions mathematical methods like 'Gaussian elimination or iterative methods such as Jacobi and Gauss-Seidel', but it does not list specific software dependencies with version numbers for its implementation. |
| Experiment Setup | No | The paper describes the theoretical model and provides an example with calculated values, but it does not detail experimental setup parameters such as hyperparameters, batch sizes, or training schedules typical for empirical studies. |