题目: Meta-Interpretive Learning: achievements and challenges
报告人: Stephen Muggleton 教授 帝国理工学院
摘要: Meta-Interpretive Learning (MIL) is a recent Inductive Logic
Programming technique aimed at supporting learning of recursive definitions.
A powerful and novel aspect of MIL is that when learning a predicate
definition it automatically introduces sub-definitions, allowing
decomposition into a hierarchy of reuseable parts. MIL is based on
an adapted version of a Prolog meta-interpreter. Normally such a
meta-interpreter derives a proof by repeatedly fetching first-order Prolog
clauses whose heads unify with a given goal. By contrast, a meta-interpretive
learner additionally fetches higher-order meta-rules whose heads unify with
the goal, and saves the resulting meta-substitutions to form a program. This
talk will overview theoretical and implementational advances in this new area
including the ability to learn Turing computabale functions within a
constrained subset of logic programs, the use of probabilistic
representations within Bayesian meta-interpretive and techniques
for minimising the number of meta-rules employed. The talk will also
summarise applications of MIL including the learning of regular and
context-free grammars, learning from visual representions with repeated
patterns, learning string transformations for spreadsheet applications,
learning and optimising recursive robot strategies and learning tactics
for proving correctness of programs. The talk will conclude by pointing to
the many challenges which remain to be addressed within this new area.