Title: Meta-Interpretive Learning of Logic Programs [Slides]
Abstract: 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.
Bio: Stephen Muggleton is Professor of Machine Learning and Head of
the Computational Bioinformatics Laboratory at Imperial College London.
Muggleton received his Bachelor of Science degree in Computer Science (1982)
and Doctor of Philosophy in Artificial Intelligence (1986) supervised by
Donald Michie at the University of Edinburgh. Following his PhD, Muggleton
went on to work as a postdoctoral research associate at the Turing Institute
in Glasgow (1987-1991) and later an EPSRC Advanced Research Fellow at
Oxford University Computing Laboratory (1992-1997) where he founded the
Machine Learning Group. In 1997, he moved to the University of York and
in 2001 to Imperial College London.
Muggleton's interests are primarily in Artificial intelligence. From 1997-2001
he held the Chair of Machine Learning at the University of York and from 2001-2006
the EPSRC Chair of Computational Bioinformatics at Imperial College in London.
Since 2013 he holds the Syngenta/Royal Academy of Engineering Research Chair
as well as the post of Director of Modelling for the Imperial College Centre
for Integrated Systems Biology. He is known for founding the field of
Inductive Logic Programming. In this field, he has made contributions to theory
introducing predicate invention, inverse entailment and stochastic logic programs.
Title: Semantics for Practitioners: Lessons from the W3C/OGC Spatial Data on the Web Working Group [Slides]
Abstract: Gartner said in December 2015 that smart cities will use 1.6 billion connected things in 2016 rising to 3.3 billion in 2018.
Large scale sensor network systems are not only features of cities and consumer products; but also of the interlinked industries of agriculture,
manufacturing, food and healthcare. The AIOTI alliance of the European Commission concluded in November 2015 that “The biggest challenge will be
to overcome the fragmentation of vertically-oriented closed systems, architectures and application areas and move towards horizontal open systems
and platforms that support multiple applications.” This reflects an environment where a great number of industry alliances have formed with a
vertical silo approach for service and data interoperability.
The Spatial Data on the Web Working Group was jointly convened by the W3C and OGC standards organisations to employ Web standards and principles to
address that need for horizontal interoperability. It started work in January 2015, chaired by myself and Ed Parsons of Google. The Group developed
several best practice guidelines and formal recommendations with formal adoption October 2017. They are targeted at both the newer, dynamic IoT Web
applications and also the traditional environmental and land management applications, but always with the Web developer-as-consumer in mind.
Ontologies founded on description logic, including the well-known Time ontology and Semantic Sensor Network ontology are part of the revamped package
produced by the Group. In this talk I will focus on the challenges we addressed and the compromises we made to take the benefits of formal ontology
modelling into the wide world of the Web of Things.
Bio: Dr Taylor is an Associate Professor at the Australian National University which she joined in January 2016. Prior, she worked
on a UN 'big data' project with Australian Bureau of Statistics, after 20 years at CSIRO as a principal research scientist leading projects and
research groups in the polyonymous IT research division. She has worked as an IT practitioner in consulting, publishing, education and government,
in Sydney, Montreal and Oxford.
Her research has focused on data management, integration, mining and machine learning in interdisciplinary contexts, especially employing logic-based
and semantic approaches. Much of her recent work addresses data issues in IoT.
She has lectured in logic programming, networking, software engineering and agile project management. Currently she lectures in data mining and convenes
the ANU's postgraduate programs in applied data analytics. From 2015-2017 she co-Chaired the joint Spatial Data on the Web working group of the W3C and the OGC.
Dr Taylor holds a BSc (Hons 1) in Computer Science from UNSW 1983 and a PhD in Computer Science and Technology from the ANU in 1996. She is a Visiting Reader at
the University of Surrey UK, and has been a Visiting Fellow at University of Melbourne. She serves on the Editorial boards of Knowledge-Based Systems and
International Journal of Distributed Sensor Networks and many conference programme committees including ISWC, ESWC, AAAI, JIST and WWW. In 2013 her project
in semantic sensor networks was awarded an AIIA National i-award.
Title: Business Process Compliance [Slides]
Abstract: Compliance means to check whether the specifications of a system are in agreement with a set of reference specifications. In this talk
we will consider the problem to determine the compliance of business processes against regulations. To this end we enrich business processes with semantic
annotations and we formalise regulation in a suitable logic. We also discuss that the formalisations (of processes and regulations) should be conceptually
sounds (in the sense that they soundly implement the semantics of the phenomenon they are meant to model).
Bio: Guido Governatori is the team leader of the Business Processes and Legal Informatics Team at Data61, CSIRO. Before joining NICTA in 2008
he had academic positions at the University of Queensland, Queensland University of Technology, Griffith University and Imperial College London.
He has over 250-refereed publications in the field of logic; agents, business processes and legal reasoning that, according to Google Scholars were
cited over 6000 times with an h-index of 42. He won the 2015 Australian Computer Society ICT Researcher of the Year Gold Award. His BPM 2007 paper
won the 2017 BPM Test of Time Award for the most influential paper on business process management from the 2007 and 2008 BPM conferences. The paper is
considered as one of the most influential paper in the BPM field and started the novel area of process compliance. He served as chair of the major
international conferences on Artificial Intelligence and Law, Deontic Logic and Legal Informatics. He was a chief investigator in a number of
Australian Research Council (ARC) grants and projects funded by the European Union Commission.