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Found 11 papers, 6 papers with code
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.
Meta-Interpretive Learning as Metarule Specialisation
We modify the MIL metarule specialisation operator to return new metarules rather than first-order clauses and prove the correctness of the new operator.
Automated Biodesign Engineering by Abductive Meta-Interpretive Learning
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 which a learning machine is used to iteratively optimise the system through a design-build-test-learn (DBTL) cycle.
Inductive logic programming at 30
Inductive logic programming (ILP) is a form of logic-based machine learning.
Top Program Construction and Reduction for polynomial time Meta-Interpretive Learning
We give an algorithm for Top program construction and show that it constructs a correct Top program in polynomial time and from a finite number of examples.
Abductive Knowledge Induction From Raw Data
In this paper, we present Abductive Meta-Interpretive Learning ($Meta_{Abd}$) that unites abduction and induction to learn neural networks and induce logic theories jointly from raw data.
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.
Turning 30: New Ideas in Inductive Logic Programming
Common criticisms of state-of-the-art machine learning include poor generalisation, a lack of interpretability, and a need for large amounts of training data.
Learning higher-order logic programs
Our theoretical results show that learning higher-order programs, rather than first-order programs, can reduce the textual complexity required to express programs which in turn reduces the size of the hypothesis space and sample complexity.
Can Meta-Interpretive Learning outperform Deep Reinforcement Learning of Evaluable Game strategies?
World-class human players have been outperformed in a number of complex two person games (Go, Chess, Checkers) by Deep Reinforcement Learning systems.
