Machine Learning - special issue on inductive logic programming
On the notion of interestingness in automated mathematical discovery
International Journal of Human-Computer Studies - Special issue on Machine Discovery
Automated Theory Formation in Pure Mathematics
Automated Theory Formation in Pure Mathematics
Discovery of frequent DATALOG patterns
Data Mining and Knowledge Discovery
ECML '93 Proceedings of the European Conference on Machine Learning
ILP '96 Selected Papers from the 6th International Workshop on Inductive Logic Programming
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Protocols from perceptual observations
Artificial Intelligence - Special volume on connecting language to the world
Learning rules of a card game from video
Artificial Intelligence Review
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Systems able to learn from visual observations have a great deal of potential for autonomous robotics, scientific discovery, and many other fields as the necessity to generalise from visual observation (from a quotidian scene or from the results of a scientific enquiry) is inherent in various domains. We describe an application to learning rules of a dice game using data from a vision system observing the game being played. In this paper, we experimented with two broad approaches: (i) a predictive learning approach with the Progol system, where explicit concept learning problems are posed and solved, and (ii) a descriptive learning approach with the HR system, where a general theory is formed with no specific problem solving task in mind and rules are extracted from the theory.