Multi-Robot Adversarial Patrolling Strategies via Lattice Paths
Authors: Jan Buermann, Jie Zhang
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comparing to iteratively updating the Markov transition matrices, our methods significantly reduces the time and space complexity of solving the problem. We use this method to tackle four penetration configurations. Figure 4: The runtime for finding an optimal p for 1, 2 and 3 robots (2nd step) and the additional Markov matrix approach runtime (1st step, finding the probability functions). For our analysis, we implemented an algorithm similar to FINDP of Agmon et al. [2011] in Python using Mathematica for efficient and accurate root-finding. |
| Researcher Affiliation | Academia | Jan Buermann and Jie Zhang University of Southampton, UK {J.Buermann, Jie.Zhang}@soton.ac.uk |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code, such as a repository link or an explicit statement of code release. |
| Open Datasets | No | The paper focuses on theoretical modeling and analytical derivations for adversarial patrolling strategies, not on training models with empirical datasets. Therefore, the concept of a 'publicly available dataset' in the context of machine learning training data is not applicable here. |
| Dataset Splits | No | The paper does not describe a machine learning or data-driven experiment that would require training, validation, or test splits of a dataset. |
| Hardware Specification | No | The paper mentions 'IRIDIS High Performance Computing Facility', but this is a general description and does not provide specific hardware details like CPU/GPU models, processor types, or memory amounts. |
| Software Dependencies | No | The paper mentions 'Python' and 'Mathematica' as tools used for analysis and implementation, but it does not specify any version numbers for these software components or any other libraries. |
| Experiment Setup | No | The paper describes the problem parameters (e.g., d segments, t time steps) and the analytical approach to find optimal `p`, but it does not provide specific experimental setup details such as hyperparameters, optimizer settings, or system-level training configurations commonly found in computational experiments. |