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Found 5 papers, 3 papers with code
Simulating Petri nets with Boolean Matrix Logic Programming
However, logic programs struggle to deal with extensive Petri nets due to the limitations of high-level symbol manipulations.
Boolean matrix logic programming for active learning of gene functions in genome-scale metabolic network models
Techniques to autonomously drive research have been prominent in Computational Scientific Discovery, while Synthetic Biology is a field of science that focuses on designing and constructing new biological systems for useful purposes.
Human Comprehensible Active Learning of Genome-Scale Metabolic Networks
An important application of Synthetic Biology is the engineering of the host cell system to yield useful products.
Explanatory machine learning for sequential human teaching
We propose a framework for the effects of sequential teaching on comprehension based on an existing definition of comprehensibility and provide evidence for support from data collected in human trials.
Beneficial and Harmful Explanatory Machine Learning
USML is demonstrated by a measurable increase in human performance of a task following provision to the human of a symbolic machine learned theory for task performance.
