Measurement Maximizing Adaptive Sampling with Risk Bounding Functions
Authors: Benjamin Ayton, Brian Williams, Richard Camilli7511-7519
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through experiments on real bathymetric data and simulated measurements, we show our algorithm allows an agent to take dangerous actions only when the reward justifies the risk. We then verify through Monte Carlo simulations that failure bounds are satisfied. |
| Researcher Affiliation | Collaboration | Benjamin Ayton, Brian Williams Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology {aytonb, williams}@mit.edu Richard Camilli Woods Hole Oceanographic Institute rcamilli@whoi.edu |
| Pseudocode | Yes | Algorithm 1: Execute Risk Bounded Policy; Algorithm 2: Sample |
| Open Source Code | No | The paper does not include an unambiguous statement that the authors are releasing the code for the work described in this paper, nor does it provide a direct link to a source-code repository for their methodology. |
| Open Datasets | Yes | The location was East of Boston Harbor, from -70.890 to -70.876 degrees longitude, and 42.344 to 42.355 degrees latitude, provided by NOAA survey H10992 (National Oceanic and Atmospheric Administration 2001). |
| Dataset Splits | No | The paper mentions "Monte Carlo simulations" and using "real bathymetric data" but does not specify explicit training, validation, or test dataset splits with percentages or sample counts. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We used the following parameters: n = 20, τ = 60 sec, Σ0 = 0I m2, Σw = 12I m2, Rmin = 12.5, lmin = 12.5 m, m(x) = 16, k(x, x ) = 1.25 exp x x 2 /(2 (200 m)2) , σ = 0. ... The following parameters were changed: n = 8, τ = 2 sec, Σw = 60I m2, lmin = 25 m. The distance traveled by every action was changed to 100 m. |