Meta-Interpretive Learning: achievements and challenges
Prof. Stephen H. Muggleton
Department of Computing, Imperial College London
英国皇家工程院院士，皇家工程院机器学习方向研究主席，皇家工程学院研究教授，国际机器学习学会指导委员会委员，AAAI Fellow，IET Fellow，EurAI Fellow
【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.
【报告人简介】Stephen Muggleton is Professor of Machine Learning in the Department of Computing at Imperial College London and is internationally recognised as the founder of the field of Inductive Logic Programming. His career has concentrated on the development of theory, implementations and applications of Machine Learning, particularly in the field of Inductive Logic Programming (ILP) and Probabilistic ILP (PILP). Over the last decade he has collaborated with biological colleagues, such as Prof Mike Sternberg, on applications of Machine Learning to Biological prediction tasks. SM’s group is situated within the Department of Computing and specialises in the development of novel general-purpose machine learning algorithms, and their application to biological prediction tasks. Widely applied software developed by the group includes the ILP system Progol (publication has over 1600 citations on Google Scholar) as well as a family of related systems including ASE-Progol (used in the Robot Scientist project), Metagol and Golem.