Reconfigurability in Reactive Multiagent Systems
Authors: Xiaowei Huang, Qingliang Chen, Jie Meng, Kaile Su
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper proposes a logic-based approach, by generalising that of model checking multiagent systems, for the reconfigurability of reactive multiagent systems. Two reconfigurability scenarios are studied, for the resulting system being a monolithic system or an individual module, and their computational complexity results are given. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Jinan University, China 2Department of Computer Science, University of Oxford, United Kingdom 3Department of Marketing and Management, Macquarie University, Australia 4Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia |
| Pseudocode | No | The paper contains mathematical definitions and proofs, but no structured pseudocode or algorithm blocks are provided. |
| Open Source Code | No | The paper does not provide any statement or link indicating the release of open-source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not describe experiments with datasets, so no information about publicly available datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments with data, so no dataset split information for training, validation, or testing is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe experiments, so no specific hardware details used for running experiments are provided. |
| Software Dependencies | No | The paper is theoretical and does not describe experiments, so no specific ancillary software dependencies with version numbers are provided. |
| Experiment Setup | No | The paper is theoretical and does not describe experiments, so no specific experimental setup details like hyperparameters or training configurations are provided. |