6 citations · 23 across the 8 of their papers we have counts for
8 papers · 1 filter
Human Comprehensible Active Learning of Genome-Scale Metabolic Networks
Lun Ai, Shi-Shun Liang, Wang-Zhou Dai +3
An important application of Synthetic Biology is the engineering of the host cell system to yield useful products. However, an increase in the scale of the host system leads to hug…
Explanatory machine learning for sequential human teaching
Lun Ai, Johannes Langer, Stephen H. Muggleton +1
The topic of comprehensibility of machine-learned theories has recently drawn increasing attention. Inductive Logic Programming (ILP) uses logic programming to derive logic theorie…
Automated Biodesign Engineering by Abductive Meta-Interpretive Learning
Wang-Zhou Dai, Liam Hallett, Stephen H. Muggleton +1
The application of Artificial Intelligence (AI) to synthetic biology will provide the foundation for the creation of a high throughput automated platform for genetic design, in whi…
Inductive logic programming at 30
Andrew Cropper, Sebastijan Dumančić, Richard Evans +1
Inductive logic programming (ILP) is a form of logic-based machine learning. The goal is to induce a hypothesis (a logic program) that generalises given training examples. As ILP t…
Top Program Construction and Reduction for polynomial time Meta-Interpretive Learning
Stassa Patsantzis, Stephen H. Muggleton
Meta-Interpretive Learners, like most ILP systems, learn by searching for a correct hypothesis in the hypothesis space, the powerset of all constructible clauses. We show how this…
Abductive Knowledge Induction From Raw Data
Wang-Zhou Dai, Stephen H. Muggleton
For many reasoning-heavy tasks involving raw inputs, it is challenging to design an appropriate end-to-end learning pipeline. Neuro-Symbolic Learning, divide the process into sub-s…