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We describe a GA-based concept learning/theory revision system DOGMA and discuss how it can be applied to relational learning. The search for better theories in DOGMA is guided by a n o vel tness function that combines the minimal description length and information gain measures. To show the eecacy of the system we compare it to other learners in three(More)
Acknowledgements As my thesis is now written, it is time to pause for a while, to take a look back and thank all the people who have been involved in the process. I would like to start by thanking my supervisor Professor Ralph Back. He has had long-sightedness in allowing me to do what I wanted to do and has had patience with the long incubation of my(More)
We study the integration of background knowledge and concept learning genetic algorithms and show h o w they have b e e n i n tegrated in the system DOGMA. Our emphasis is in speeding up the inductive learning process by using suggestions from the background knowledge to direct genetic search. We don't do theory revision by patching the old theory, rather(More)
We describe an application of DOGMA, a GA-based theory revision system, to MDL-based rule enhancement in supervised concept learning. The system takes as input classiication data and a rule-based classiication theory, produced by some rule-based learner, and builds a second, hopefully more accurate , model of the data. Unlike most theory revision systems(More)
We describe how proof rules for three advanced reenement features are mechanically veriied using the HOL theorem prover. These features are data reenement, backwards data reenement and superposition reenement of initialised loops. We also show how applications of these proof rules to actual program reenement can be checked using the HOL system, with the HOL(More)
We study the use of genetic algorithms in rule-based concept learning. The developed system, JGA, is capable of learning disjunctive concepts in First Order Logic. We take a t wo-leveled approach that combines features from both the Michigan and Pittsburgh approaches. We compare the system in several propositional domains with three well-known concept(More)