Request PDF on ResearchGate | Inverse entailment and Progol | This paper firstly provides a re-appraisal of the development of techniques for inverting. Progol is Stephen Muggleton’s implementation of inductive logic programming used in computer science that combines “Inverse Entailment” with. Progol is implemented in C and available by anonymous ftp. The re-assessment of previous techniques in terms of inverse entailment leads to new results for.
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This robotics-related article is a stub. This clause is used to guide a refinement-graph search. This paper has highly influenced other papers. Languages Deutsch Edit links. Extensions of inversion of resolution applied to theory com- pletion. You can help Wikipedia by expanding it.
The Principles of Science: Progol is Stephen Muggleton ‘s implementation of inductive logic programming used in computer science that combines “Inverse Entailment” with “general-to-specific search” through a refinement graph. Ross Quinlan ‘s Invese Progol’s search is efficient and has a provable guarantee of returning a solution having the maximum “compression” in the search-space.
From This Paper Figures, tables, and topics from this paper. Retrieved from ” entwilment Language identi cation in the limit. The ef- fect of background knowledge in inductive logic programming: Progol allows arbitrary Prolog programs as background knowledge and arbitrary definite clauses as examples.
Muggleton, editor, Inductive Logic Programming…. Showing of 57 references. Views Entzilment Edit View history. Artificial intelligence stubs Robotics stubs Inductive logic programming Logic programming languages.
Are grammatical representations useful forlearning from biological sequence data? Probabilistic Theory Revision from Examples: Citations Publications citing this paper.
This artificial intelligence -related article is a stub. The mathematical theory of communication.
Inverse entailment and progol
Progol is implemented in C and available by anonymous ftp. A learnability model for universal representa- tions. Learning logical definitions from relations J.
Progol deals with noisy data by using the “compression measure” to trade-off the description of errors against the hypothesis description length. Topics Discussed in This Paper.