Verifying Emergent Properties of Swarms
Authors: Panagiotis Kouvaros, Alessio Lomuscio
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We put forward a formal model for swarms that accounts for their nature of unbounded collections of agents following simple local protocols. We formally define the decision problem of determining whether a swarm satisfies an emergent property. We introduce a sound and complete procedure for solving the problem. We illustrate the technique by applying it to the Beta aggregation algorithm. |
| Researcher Affiliation | Academia | Panagiotis Kouvaros and Alessio Lomuscio Department of Computing, Imperial College London, UK |
| Pseudocode | No | The paper describes the 'Emergence Identification Procedure' in prose with three steps, but it does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for its methodology is open-source or publicly available. |
| Open Datasets | No | The paper is theoretical and illustrates its technique on the Beta aggregation algorithm. It does not use a dataset for training in the typical sense of machine learning, nor does it provide access information for a public dataset. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with dataset splits. Therefore, it does not provide training/test/validation dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types, memory) used for any computations or demonstrations. |
| Software Dependencies | No | The paper mentions 'MCMAS [Lomuscio et al., 2009]' as an epistemic model checker, but it does not specify any version numbers or other software dependencies required to reproduce the work. |
| Experiment Setup | No | The paper describes a theoretical framework and its application to a formal algorithm. It does not include specific experimental setup details such as hyperparameters, training configurations, or system-level settings, as these are not relevant to its theoretical nature. |