Bounded Suboptimal Search with Learned Heuristics for Multi-Agent Systems
Authors: Markus Spies, Marco Todescato, Hannes Becker, Patrick Kesper, Nicolai Waniek, Meng Guo2387-2394
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In the following, we present experimental results of applying the proposed approach to the use case of Autonomous Valet Parking (AVP). |
| Researcher Affiliation | Industry | Bosch Center for Artificial Intelligence (BCAI), Renningen, Germany {markus.spies2, marco.todescato, hannes.becker, patrick.kesper, nicolai.waniek, meng.guo2}@de.bosch.com |
| Pseudocode | No | No pseudocode or algorithm blocks are present in the paper. Figure 1b is a diagram, not a pseudocode representation. |
| Open Source Code | No | The paper does not contain any statement about open-sourcing the code or providing a link to a code repository. |
| Open Datasets | No | The paper describes generating its own training data ('We generated training data with an optimal planner... to solve sampled problem instances.') but does not provide access information (link, DOI, or formal citation for a publicly available dataset). |
| Dataset Splits | Yes | We generated training data with an optimal planner... We created two different test and validation datasets according to Section 4.2. |
| Hardware Specification | Yes | All reported timing information in the experimental section refer to a server with 2.10 GHz Intel(R) Xeon(R) E5-2695 v4 CPUs and a GTX 1080 TI graphics card. |
| Software Dependencies | No | The paper describes the use of a deep convolutional neural network but does not specify any software dependencies (e.g., libraries, frameworks) with version numbers. |
| Experiment Setup | No | The paper describes the network architecture (e.g., '14 convolutional layers, consisting of 64 3x3 filters') and loss functions, but does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs, optimizer settings) or system-level training details. |