Adversarial Fence Patrolling: Non-Uniform Policies for Asymmetric Environments
Authors: Yaniv Oshrat, Noa Agmon, Sarit Kraus10377-10384
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We propose novel methods for calculating the variant values, and demonstrate their performance empirically. We show analytically and empirically that the results obtained by this approach are significantly better than those of the former approach. |
| Researcher Affiliation | Academia | Yaniv Oshrat, Noa Agmon, Sarit Kraus Department of Computer Science, Bar-Ilan University oshblo@zahav.net.il, {agmon, sarit}@cs.biu.ac.il |
| Pseudocode | No | The paper mentions algorithms like ATAPS and describes them in text, but it explicitly states 'An outline of the ATAPS algorithm is provided in the supplemental material', implying no pseudocode is present in the main paper. |
| Open Source Code | No | The paper does not provide any concrete access to source code, such as a repository link or an explicit statement about code release. |
| Open Datasets | No | The paper describes a simulation-based research problem involving robot patrolling on a segmented track, and therefore does not utilize or provide access to a publicly available or open dataset. |
| Dataset Splits | No | The paper operates in a simulated environment and does not specify training, validation, or test dataset splits in the traditional sense, as it does not use a pre-existing dataset. |
| Hardware Specification | No | The paper mentions the time taken for computations (e.g., '9 days of running'), but it does not provide specific hardware details such as CPU or GPU models used for the experiments. |
| Software Dependencies | No | The paper states 'In our implementation we used the Nelder-Mead and the SLSQP methods of the function optimize.minimize() in the Python Sci Py library for scientific computing', but it does not specify the version numbers for Python or the SciPy library. |
| Experiment Setup | Yes | The paper describes parameters for the ATAPS heuristic and exhaustive searches, such as 'adjust the range r = [left, right] [0, 1] and the resolution τ [0, 1]', and provides examples like 'XSHR: r = [0.5, 1], τ = 0.03' in Figure 3. |