Meta-Interpretive Learning and Program Induction
This talk will review work at Imperial College on the development of Meta-Interpretive Learning (MIL), a technique which supports efficient predicate invention and learning of recursive logic programs by way of abduction with respect to a meta-interpreter. The approach has been applied to the learning of regular and context-free grammars, and further extended to learn dyadic datalog programs. An extension of the approach uses a meta-interpreter of Stochastic Logic Programs (SLP) to implement a Bayesian posterior distribution over the hypothesis space. An ongoing application of MIL technology will be described in which MIL technology is applied to incrementally learn a series of string transformation program induction problems previously studied by Sumit Gulwani (Microsoft Redmond). In this case learning is constrained to the provision of a small number of examples supplied by a spreadsheet user.
Speaker Details
Stephen Muggleton FREng is Professor of Machine Learning at the Department of Computing at Imperial College. He has been the recipient of two Royal Academy of Engineering Research Chairs in part supported by Microsoft (2007-2012) and Syngenta (2013-2018). His recent awards include being elected Fellow of the British Computing Society (2008), Fellow of the IET (2008), Fellow of the Royal Academy of Engineering (2010) and Fellow of the Society of Biology (2011).
His work concentrates on the development of theory, implementations and applications of Machine Learning, particularly in the fields of Inductive Logic Programming (ILP) and Probabilistic ILP (PILP). This includes the development of widely applied machine learning systems including the Progol ILP system. He has strong research collaborations involving applications of his Machine Learning algorithms to biological applications with colleagues at Imperial College.
- Date:
- Speakers:
- Stephen Muggleton
- Affiliation:
- Imperial College, London
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Jeff Running
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