Progol is an implementation of

inductive logic programming Inductive logic programming (ILP) is a subfield of symbolic artificial intelligence which uses logic programming as a uniform representation for examples, background knowledge and hypotheses. The term "''inductive''" here refers to philosophical ...

that combines inverse entailment with general-to-specific search through a refinement graph.

Features

Inverse entailment is used with mode declarations to derive the bottom clause, the most-specific clause within the mode language which subsume a given example. This clause is used to guide a refinement-graph search. Unlike the searches of Ehud Shapiro's model inference system (MIS) and J. Ross Quinlan's

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, Progol's search has a provable guarantee of returning a solution having the maximum compression in the search-space. To do so it performs an admissible A*-like search, guided by compression, over clauses which subsume the most specific clause. Progol deals with noisy data by using a compression measure to trade off the description of errors against the hypothesis description length. Progol allows arbitrary

Prolog Prolog is a logic programming language that has its origins in artificial intelligence, automated theorem proving, and computational linguistics. Prolog has its roots in first-order logic, a formal logic. Unlike many other programming language ...

programs as background knowledge and arbitrary definite clauses as examples.

History

Progol was introduced by Stephen Muggleton in 1995. In 1996, it was used by Ashwin Srinivasan, Muggleton,

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and Ross King to predict the

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activity in nitroaromatic compounds. This was considered a landmark application for

, as a general purpose inductive learner had discovered results that were both novel and meaningful to domain experts. Progol proved very influential in the field, and the widely-used inductive logic programming system

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builds directly on Progol.

References

{{reflist Inductive logic programming