Hierarchical Approach to Transfer of Control in Semi-Autonomous Systems
Authors: Kyle Hollins Wray, Luis Pineda, Shlomo Zilberstein
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we test the SAVE model using real-world road data from Open Street Map (OSM) within 10 cities, showing the benefits of the collaboration between the agent and human. We present a series of trials with subsets of 10 cities road data from Open Street Map (OSM). Table 1 shows our results for 100 trials for each city. Figure 1 depicts a sample collaborative policy in which the human and vehicle gracefully transfer control along the route. |
| Researcher Affiliation | Academia | College of Information and Computer Sciences University of Massachusetts, Amherst, MA 01003 {wray,lpineda,shlomo}@cs.umass.edu |
| Pseudocode | No | The paper presents mathematical definitions and equations, but no structured pseudocode or algorithm blocks are included. |
| Open Source Code | No | Finally, we will provide our source code to facilitate the creation of a wide variety of strong semi-autonomous systems. |
| Open Datasets | Yes | Finally, we test the SAVE model using real-world road data from Open Street Map (OSM) within 10 cities, showing the benefits of the collaboration between the agent and human. |
| Dataset Splits | No | The paper mentions '100 trials for each city' but does not explicitly provide details about training, validation, or test dataset splits (e.g., percentages, sample counts, or specific split files). |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU models, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'Point-Based Value Iteration (PBVI)' and 'LAO*', which are algorithms/methods, but does not specify any software dependencies with version numbers (e.g., Python, PyTorch, CPLEX, etc.). |
| Experiment Setup | No | The paper mentions '100 trials for each city' in the experiments section, but does not provide specific experimental setup details such as hyperparameter values (e.g., learning rate, batch size, epochs), optimizer settings, or other system-level training configurations. |