Rule-Based Programming of Molecular Robot Swarms for Biomedical Applications

Authors: Inbal Wiesel-Kapah, Gal A. Kaminka, Guy Hachmon, Noa Agmon, Ido Bachelet

IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental We prove the validity of the compiler output, and report on in-vitro experiments using generated nanobot swarms.
Researcher Affiliation Collaboration 1Computer Science Department, Bar Ilan University, Israel 2XLX Technologies, Israel 3Augmanity, Ltd., Israel
Pseudocode Yes Algorithm 1 (Construct Tree), which transforms FSMs into and/or graphs, is the heart of the back-end phase.
Open Source Code No The paper does not provide concrete access to source code for the methodology described.
Open Datasets No The paper states 'We use the DNA-based clamshell nanobot [Douglas et al., 2012] in the experiments.' and 'report on in-vitro experiments'. This indicates lab experiments were conducted, not the use of a publicly available dataset for training.
Dataset Splits No The paper describes in-vitro experiments but does not provide specific dataset split information (train/validation/test) needed to reproduce data partitioning.
Hardware Specification No The paper refers to nanobots (e.g., 'DNA-based clamshell nanobot') as the physical systems being programmed and experimented with, but does not provide specific hardware details (like GPU/CPU models or processor types) used for running computational experiments or the compiler itself.
Software Dependencies No The paper mentions the 'Bilbo compiler' and 'Athelas language' but does not provide specific ancillary software details like library or solver names with version numbers needed to replicate the experiment.
Experiment Setup No The 'Experimental Results' section describes the setup for in-vitro experiments (e.g., mixing nanobots and using fluorescent materials), which is a description of the biological experiment setup. However, it does not provide specific computational experiment setup details such as concrete hyperparameter values or system-level training settings for the compiler or any computational model.