@inproceedings{mugg:fgcsilp,
	AUTHOR = "S.H. Muggleton",
	TITLE = "Developments in {I}nductive {L}ogic {P}rogramming",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/fgcs.pdf",
	YEAR = 1992,
	BOOKTITLE= "Proceedings of the International Conference on Fifth Generation Computer Systems 1992",
	PAGES = "1071--1073",
	PUBLISHER = "Ohmsha",
	ADDRESS= "Tokyo"}
@inproceedings{bratmugg:utube,
	AUTHOR = "I. Bratko and S.H. Muggleton and A. Varsek",
	TITLE = "Learning Qualitative Models of Dynamic Systems",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ewsl91.pdf",
	YEAR = 1991,
	BOOKTITLE= "Proceedings of the Eighth International Machine Learning Workshop",
	PUBLISHER = "Morgan-Kaufmann",
	ADDRESS= "San Mateo, Ca"}
@inproceedings{srinmug:mutacompare,
	AUTHOR = "A. Srinivasan and S.H. Muggleton and and R.D. King",
	TITLE = "Comparing the use of background knowledge by inductive logic programming systems",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/mutacompare.pdf",
	EDITOR = "L. De Raedt",
	YEAR = "1995",
	BOOKTITLE = "Proceedings of the Fifth International Inductive Logic Programming Workshop",
	PUBLISHER = "Katholieke Universteit Leuven" }
InProceedingsaedtPagMugSri97:ECML97,
  author = 	 "James Cussens and David Page and Stephen Muggleton
		  and Ashwin Srinivasan",
  title = 	 "Using {Inductive Logic Programming} for {Natural
		  Logic Processing}",
  url = "http://www.doc.ic.ac.uk/\~shm/Papers/ecml97mlnet.pdf",
  editor =	 "W. Daelemans and T. Weijters and A. van der Bosch",
  pages =	 "25--34",
  booktitle =	 "{ECML'97} -- Workshop Notes on Empirical Learning of
		  Natural Language Tasks",
  year =	 1997,
  publisher =	 "University of Economics",
  address =	 "Prague",
  note =	 "Invited keynote paper"
}
@inproceedings{dze:mug:rus:constrained,
	AUTHOR = "S. D\v{z}eroski and S.H. Muggleton and S. Russell",
	TITLE = "Learnability of Constrained Logic Programs",
	YEAR = 1993,
	BOOKTITLE = "Proceedings of the European Conference on Machine Learning ",
        PAGES = "342--347",
        PUBLISHER = "Springer-Verlag",
	ADDRESS = "London, UK"}
@inproceedings{dze:mug:rus,
	AUTHOR = "S. D\v{z}eroski and S.H. Muggleton and S. Russell",
	TITLE = "{PAC}-learnability of determinate logic programs",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/colt92.pdf",
	PAGES = "128--135",
	YEAR = 1992,
	BOOKTITLE = "Proceedings of the 5th {ACM} Workshop on Computational
Learning Theory",
        PUBLISHER = "ACM Press",
	ADDRESS = "New York, NY"}
@inproceedings{cao:hlgg,
	AUTHOR = "C. Feng and S.H. Muggleton",
	TITLE = "Towards inductive generalisation in higher order logic",
	YEAR = 1992,
        PAGES = "154--162",
        BOOKTITLE = "Proceedings of the Ninth International Workshop on Machine
                Learning",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/holgg.pdf",
        EDITOR = "D. Sleeman and P. Edwards",
        PUBLISHER = "Morgan Kaufmann",
        ADDRESS = "San Mateo, CA"}
@inproceedings{rulemaster,
	AUTHOR = "D. Michie and S.H. Muggleton and C. Riese and S. Zubrick",
	TITLE = "RuleMaster:  a second-generation knowledge-engineering
		facility",
	YEAR = 1984,
	PAGES = "591--597",
	BOOKTITLE = "Proceedings of the First Conference on Artificial
		Intelligence Applications",
	PUBLISHER = "IEEE Computer Soc."}
@inproceedings{mug:duce,
	AUTHOR = "S.H. Muggleton",
	TITLE = "Duce, an oracle based approach to constructive induction",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ijcai87.pdf",
	BOOKTITLE = "IJCAI-87",
	YEAR = 1987,
	PUBLISHER = "Kaufmann",
	PAGES = "287--292"}
@inproceedings{cigol:mugbun,
	AUTHOR = "S.H. Muggleton and W. Buntine",
	TITLE = "Machine invention of first-order predicates
		by inverting resolution",
	BOOKTITLE = "Proceedings of the 5th International Conference on
		Machine Learning",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/cigol.pdf",
	YEAR = 1988,
	PUBLISHER = "Kaufmann",
	PAGES = "339--352"}
@inproceedings{algcomp:mug,
	AUTHOR = "S.H. Muggleton",
	TITLE = "A strategy for constructing new predicates
		in first order logic",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ewsl88.pdf",
	BOOKTITLE = "Proceedings of the Third European
		Working Session on Learning",
	YEAR = 1988,
	PUBLISHER = "Pitman",
	PAGES = "123--130"}
@inproceedings{mugfeng:golem,
	AUTHOR = "S.H. Muggleton and C. Feng",
	TITLE = "Efficient induction of logic programs",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/alt90.pdf",
	BOOKTITLE = "Proceedings of the First Conference
		on Algorithmic Learning Theory",
	PAGES = "368--381",
	YEAR = 1990,
	PUBLISHER = "Ohmsha",
	ADDRESS = "Tokyo" }
@inproceedings{expform:cigol,
	AUTHOR = "S.H. Muggleton and M.E. Bain and
		J. Hayes-Michie and D. Michie",
	TITLE = "An experimental comparison of human and machine learning
		formalisms",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ml6paper.pdf",
	BOOKTITLE = "Proceedings of the Sixth International Workshop on
		Machine Learning",
	YEAR = 1989,
	PUBLISHER = "Kaufmann",
	ADDRESS = "Los Altos, CA"}
@inproceedings{mug:co.pdfig,
	AUTHOR = "S.H. Muggleton and A. Srinivasan and M. Bain",
	TITLE = "Compression, significance and accuracy",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/compsig.pdf",
	YEAR = 1992,
	BOOKTITLE = "Proceedings of the Ninth International Machine
	Learning Conference",
	PAGES = "338--347",
	EDITOR = "D. Sleeman and P. Edwards",
	PUBLISHER = "Morgan-Kaufmann",
	ADDRESS = "San Mateo, CA"}
@inproceedings{mugg:invimp,
	TITLE = "Inverting Implication",
	AUTHOR = "S.H. Muggleton",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/invimp.pdf",
	BOOKTITLE = "Proceedings of the Second Inductive Logic Programming
		Workshop",
	YEAR = 1992,
	PUBLISHER = "ICOT (Technical report TM-1182)",
	PAGES = "19--39",
	ADDRESS = "Tokyo"}
@InProceedings(Muggleton93:proc,
	Author       = "Muggleton, S.H.",
	Title        = "Optimal layered learning: {A} {PAC} approach to
		incremental sampling",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/layered.pdf",
	Booktitle    = "Proceedings of the 4th Conference on Algorithmic
		Learning Theory",
	Editor	     = "K. Jantke and S. Kobayashi and E. Tomita and
		T. Yokomori",
	Publisher    = "Springer-Verlag",
	Series	     = "LNAI 744",
	Year         = 1993,
	Pages	     = "37-44"
)
@inproceedings{mugg:bayesml,
	AUTHOR = "S.H. Muggleton",
	TITLE = "Bayesian {I}nductive {L}ogic {P}rogramming",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/bayesian.pdf",
	YEAR = 1994,
	BOOKTITLE = "Proceedings of the Eleventh International Machine
		Learning Conference",
	PAGES = "371--379",
	EDITOR = "W. Cohen and H. Hirsh",
	PUBLISHER = "Morgan-Kaufmann",
	NOTE = "Keynote presentation",
	ADDRESS = "San Mateo, CA"}
@inproceedings{mugg:bayescolt,
	AUTHOR = "S.H. Muggleton",
	TITLE = "Bayesian {I}nductive {L}ogic {P}rogramming",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/bayesian.pdf",
	YEAR = 1994,
	BOOKTITLE = "Proceedings of the Seventh Annual ACM
		Conference on Computational Learning Theory",
	PAGES = "3--11",
	EDITOR = "M. Warmuth",
	NOTE = "Keynote presentation",
	PUBLISHER = "ACM Press",
	ADDRESS = "New York"}
@incollection{mugg:muta,
        AUTHOR = "A. Srinivasan and S.H. Muggleton and R.D. King and M.J.E. Sternberg",
        TITLE = "Mutagenesis: {ILP} experiments in a non-determinate biological domain",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ilp94.pdf",
        YEAR = 1994,
        EDITOR = "S. Wrobel",
        BOOKTITLE = "Proceedings of the Fourth International
		Inductive Logic Programming Workshop",
        PUBLISHER = "Gesellschaft fur Mathematik und Datenverarbeitung MBH",
        NOTE = "GMD-Studien Nr 237"}
@incollection{mugg:slp0,
        AUTHOR = "S.H. Muggleton",
        TITLE = "Stochastic logic programs",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/slp.pdf",
        YEAR = 1996,
        EDITOR = "L. de Raedt",
        PAGES = "254--264",
        BOOKTITLE = "Advances in Inductive Logic Programming",
        PUBLISHER = "IOS Press" }
@incollection{srin:carc,
        AUTHOR = "A. Srinivasan and and R.D. King S.H. Muggleton and M.J.E. Sternberg",
        TITLE = "Carcinogenesis predictions using {ILP}",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ilp97a.pdf",
        YEAR = 1997,
        EDITOR = "N. Lavra\v{c} and S. D\v{z}eroski",
        PAGES = "273--287",
        BOOKTITLE = "Proceedings of the Seventh International Workshop
        	on Inductive Logic Programming",
        PUBLISHER = "Springer-Verlag",
        NOTE = "LNAI 1297",
        ADDRESS = "Berlin"}
@incollection{mugg:ecai98,
        AUTHOR = "S.H. Muggleton",
        TITLE = "Inductive Logic Programming: issues, results and the
		{LLL} challenge",
	ABSTRACT = "Inductive Logic Programming (ILP)
	  \cite{mugg:ilp,mugg:der} is the
	  area of AI which deals with the induction of hypothesised predicate
	  definitions from examples and background knowledge. Logic programs
	  are used as a single representation for examples, background
	  knowledge and hypotheses.  ILP is differentiated from most other
	  forms of Machine Learning (ML) both by its use of an expressive
	  representation language and its ability to make use of logically
	  encoded background knowledge.  This has allowed successful
	  applications of ILP \cite{bratmug:ilpapp} in areas such as molecular
	  biology \cite{stern:roysoc,muggks:proteins,kmuggs:muta,
	  Finn+Muggleton+Page+Srinivasan/98/Discovery} and natural language
	  \cite{mooney:nlp,CusPagMugSri97:ECML97,Cus97-ILP97} which
	  both have rich sources of background knowledge and both benefit
	  from the use of an expressive concept representation languages.
	  For instance, the ILP system Progol has recently been used to
	  generate comprehensible descriptions of the 23 most populated fold
	  classes of proteins \cite{turcotte:folds},
	  where no such descriptions had previously been formulated manually. 
	  In the natural language area ILP has not only been shown to have higher
	  accuracies than various other ML approaches in learning the
	  past tense of English \cite{mooney:foidl} but also shown to be
	  capable of learning accurate
	  grammars which translate sentences into deductive database queries
	  \cite{zelle:semantics}.
	  In both cases, follow up studies
	  \cite{tho.pdfon:semantics,dzer:nominal} have shown that these
	  ILP approaches to natural language problems extend with relative
	  ease to various languages other than English.
  
	  The area of Learning Language in Logic (LLL) is producing a number
	  of challenges to existing ILP theory and implementations. In
	  particular, language applications of ILP require revision and
	  extension of a hierarchically defined set of predicates in which
	  the examples are typically only provided for predicates at the top
	  of the hierarchy. New predicates often need to be invented, and
	  complex recursion is usually involved. Similarly the term structure
	  of semantic objects is far more complex than in other applications of
	  ILP. Advances in ILP theory and implementation related to the
	  challenges of LLL are already producing beneficial advances in
	  other sequence-oriented applications of ILP. In addition LLL
	  is starting to develop its own character as a sub-discipline
	  of AI involving the confluence of computational linguistics,
	  machine learning and logic programming.",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ecai98.pdf",
        YEAR = 1998,
	PAGES = 697,
        EDITOR = "H. Prade",
        BOOKTITLE = "Proceedings of ECAI98",
        PUBLISHER = "John Wiley",
        NOTE = "Abstract of keynote talk"}
@incollection{mugg:padd97,
        AUTHOR = "S.H. Muggleton",
        TITLE = "Declarative knowledge discovery in industrial databases",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/padd97.pdf",
	PAGES = "9--24",
        YEAR = 1997,
	EDITOR = "H.F. Arner",
        BOOKTITLE = "Proceedings of the First International Conference
		and Exhibition on The Practical Application of Knowledge
		Discovery and Data Mining (PADD-97)",
        PUBLISHER = "Practical Application Company Ltd."}
@incollection{srin:pte,
        AUTHOR = "A. Srinivasan and and R.D. King S.H. Muggleton and M.J.E. Sternberg",
        TITLE = "The predictive toxicology evaluation challenge",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ijcai97.pdf",
        YEAR = 1997,
        PAGES = "1--6",
        BOOKTITLE = "Proceedings of the Fifteenth International Joint
		Conference Artificial Intelligence (IJCAI-97)",
        PUBLISHER = "Morgan-Kaufmann"}
@inproceedings{mugg:stereo,
	TITLE = "An initial experiment into stereochemistry-based drug
		design using {ILP}",
	AUTHOR = "S.H. Muggleton and C.D. Page and A. Srinivasan",
	BOOKTITLE = "Proceedings of the Sixth Inductive Logic Programming
		Workshop (ILP96)",
        EDITOR = "S.H. Muggleton",
	PAGES = "25--40",
	YEAR = 1996,
	SERIES = "LNAI 1314",
	PUBLISHER = "Springer-Verlag",
	ADDRESS = "Berlin"}
@inproceedings{moyle:eventc,
	TITLE = "Learning programs in the event calculus",
	AUTHOR = "S. Moyle and S.H. Muggleton",
	BOOKTITLE = "Proceedings of the Seventh Inductive Logic Programming
		Workshop (ILP97)",
        EDITOR = "N. Lavra\v{c} and S. D\v{z}eroski",
	YEAR = 1997,
	PAGES = "205--212",
	SERIES = "LNAI 1297",
	PUBLISHER = "Springer-Verlag",
	ADDRESS = "Berlin"}
@incollection{muggpage:selfsat,
        AUTHOR = "S.H. Muggleton and C.D. Page",
        TITLE = "Self-saturation of definite clauses",
        YEAR = 1994,
        EDITOR = "S. Wrobel",
        BOOKTITLE = "Proceedings of the Fourth International
			Inductive Logic Programming Workshop",
        PUBLISHER = "Gesellschaft fur Mathematik und Datenverarbeitung MBH",
	PAGES = "161--174",
        NOTE = "GMD-Studien Nr 237"}
@inproceedings{srin:noise,
	TITLE = "Distinguishing exceptions from noise in non-monotonic learning",
	AUTHOR = "A. Srinivasan and S.H. Muggleton and M. Bain",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/new_nil.pdf",
	BOOKTITLE = "Proceedings of the Second Inductive Logic Programming
		Workshop",
	YEAR = 1992,
	PUBLISHER = "ICOT (Technical report TM-1182)",
	PAGES = "97--107",
	ADDRESS = "Tokyo"}
@inproceedings{mugg:poslearn,
	TITLE = "Learning from Positive data",
	ABSTRACT = " Gold showed in 1967 that not even regular grammars can be
	  exactly identified from positive examples alone. Since it is
	  known that children learn natural grammars almost exclusively
	  from positives examples, Gold's result has been used
	  as a theoretical support for Chomsky's theory of innate
	  human linguistic abilities. In this paper new results are presented
	  which show that within a Bayesian framework not only grammars, but also
	  logic programs are learnable with arbitrarily low expected
	  error from positive examples only.  In addition, we show
	  that the upper bound for expected error of a learner which maximises the
	  Bayes' posterior probability when learning from positive examples
	  is within a small additive term of one which does the
	  same from a mixture of positive and negative examples.
	  An Inductive Logic
	  Programming implementation is described which avoids the pitfalls of
	  greedy search by global optimisation of this function during the local
	  construction of individual clauses of the hypothesis.
	  Results of testing this implementation on artificially-generated data-sets
	  are reported. These results are in agreement with the theoretical
	  predictions.",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/poslearn.pdf",
	AUTHOR = "S.H. Muggleton",
	YEAR = "1996",
	PAGES = "358--376",
        EDITOR = "S.H. Muggleton",
        BOOKTITLE = "Proceedings of the Sixth International Workshop
        	on Inductive Logic Programming (Workshop-96)",
        PUBLISHER = "Springer-Verlag",
	SERIES = "LNAI 1314",
        ADDRESS = "Berlin" }
@incollection{mugg:ie,
        AUTHOR = "S.H. Muggleton",
        TITLE = "Completing inverse entailment",
	ABSTRACT = "Yamamoto has shown that the {\it Inverse Entailment}
	  (IE) mechanism described previously by the author is complete
	  for Plotkin's relative subsumption but incomplete for entailment.
	  That is to say, an hypothesised clause $H$ can be derived from an
	  example $E$ under a background theory $B$ using IE if and only
	  if $H$ subsumes $E$ relative to $B$ in Plotkin's sense. Yamamoto
	  gives examples of $H$ for which $B\cup H \models E$ but $H$ cannot
	  be constructed using IE from $B$ and $E$.  The main result of the
	  present paper is a theorem to show that by enlarging the bottom set
	  used within IE, it is possible to make a revised version of IE
	  complete with respect to entailment for Horn theories.  Furthermore,
	  it is shown for function-free definite clauses that given a
	  bound $k$ on the arity of predicates used in $B$ and $E$,
	  the cardinality of the enlarged bottom set is bounded above by
	  the polynomial function ${p(c+1)}^k$, where $p$ is the number of
	  predicates in $B,E$ and $c$ is the number of constants in
	  $B\cup\overline{E}$.",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ie.pdf",
        YEAR = 1998,
	PAGES = "245--249",
        EDITOR = "C.D. Page",
        BOOKTITLE = "Proceedings of the Eighth International Workshop
        	on Inductive Logic Programming (ILP-98)",
        PUBLISHER = "Springer-Verlag",
	SERIES = "LNAI 1446",
        ADDRESS = "Berlin" }
@inproceedings{turcotte:folds,
	AUTHOR = "M. Turcotte and S.H. Muggleton and M.J.E. Sternberg",
	TITLE = "Protein Fold Recognition",
	ABSTRACT = "Inductive Logic Programming (ILP) has been applied
	  to discover rules governing the three-dimensional topology of
	  protein structure.  The data-set unifies two sources of information;
	  SCOP and PROMOTIF.  Cross-validation results for experiments using
	  two background knowledge sets, global (attribute-valued) and
	  constitutional (relational), are presented.  The application makes
	  use of a new feature of Progol4.4 for numeric parameter estimation.
	  At this early stage of development, the rules produced can only be
	  applied to proteins for which the secondary structure is known.
	  However, since the rules are insightful, they should prove to be
	  helpful in assisting the development of taxonomic schemes.
	  The application of ILP to fold recognition represents a novel and
	  promising approach to this problem.",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/folds.pdf",
	YEAR = 1998,
        EDITOR = "C.D. Page",
	BOOKTITLE = "Proc.\ of the 8th International Workshop on Inductive Logic
	  Programming (ILP-98)",
	PAGES = "53--64",
	SERIES = "LNAI 1446",
	PUBLISHER = "Springer-Verlag",
	ADDRESS= "Berlin"}
@inproceedings{khanmug:repeat,
	AUTHOR = "K. Khan and S.H. Muggleton and R. Parson",
	TITLE = "Repeat learning using predicate invention",
	ABSTRACT = "Most of machine learning is concerned with learning a
	single concept from a sequence of examples. In {\it repeat learning}
	the teacher chooses a series of related concepts randomly and
	independently from a distribution $\cal D$. A finite sequence of
	examples is provided for each concept in the series.  The learner does
	not initially know $\cal D$, but progressively updates a posterior
	estimation of $\cal D$ as the series progresses.  This papers
	considers {\it predicate invention} within Inductive Logic Programming
	as a mechanism for updating the learner's estimation of $\cal D$. A
	new predicate invention mechanism implemented in Progol4.4 is used in
	repeat learning experiments within a chess domain. The results indicate
	that significant performance increases can be achieved. The paper
	devel.pdf a Bayesian framework and demonstrates initial theoretical
	results for repeat learning.",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/repeat.pdf",
	YEAR = 1998,
        EDITOR = "C.D. Page",
	PAGES = "165--174",
	BOOKTITLE = "Proc.\ of the 8th International Workshop on Inductive Logic
	  Programming (ILP-98)",
	PUBLISHER = "Springer-Verlag",
	SERIES = "LNAI 1446",
	ADDRESS= "Berlin"}
@book{koza1994genetic,
  title={Genetic programming II: automatic discovery of reusable programs},
  author={Koza, John R and Rice, James P},
  volume={40},
  year={1994},
  publisher={MIT press Cambridge}
}
@inproceedings{roberts:traffic,
	AUTHOR = "S. Roberts and W. Van Laerand and N. Jacobs
		and S.H. Muggleton and J. Broughton",
	TITLE = " A comparison of {ILP} and propositional systems on
		propositional data",
	ABSTRACT = "This paper presents an experimental comparison of two
	  Inductive Logic Programming algorithms, Progol and Tilde, with C4.5,
	  a propositional learning algorithm, on a propositional dataset of
	  road traffic accidents. Rebalancing methods are described for
	  handling the skewed distribution of positive and negative examples
	  in this dataset, and the relative cost of errors of commission and
	  omission in this domain.  It is noted that before the use of these
	  methods all algorithms perform worse than majority class. On
	  rebalancing, all did significantly better.  The conclusion drawn
	  from th experimental results is that on such a propositional data
	  set ILP algorithms perform competitively in terms of predictive
	  accuracy with propositional systems, but are significantly
	  outperformed in terms of time taken for learning.",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/traffic.pdf",
	YEAR = 1998,
        EDITOR = "C.D. Page",
	PAGES = "291--299",
	BOOKTITLE = "Proc.\ of the 8th International Workshop on Inductive Logic
	  Programming (ILP-98)",
	PUBLISHER = "Springer-Verlag",
	SERIES = "LNAI 1446",
	ADDRESS= "Berlin"}
@inproceedings{dzer:spanish,
	AUTHOR = "S. D\v{z}eroski and N. Jacobs and M. Molina and C. Moure and
		S.H. Muggleton and W. Van Laer",
	TITLE = "Detecting traffic problems with {ILP}",
	ABSTRACT = "Expert systems for decision support have recently been
	successfully introduced in road transport management. These systems
	include knowledge on traffic problem detection and alleviation. The
	paper describes experiments in automated acquisition of knowledge on
	traffic problem detection. The task is to detect road sections where
	a problem has occurred (critical sections) from sensor data. It is
	necessary to use inductive logic programming (ILP) for this purpose
	as relational background knowledge on the road network is essential.
	In this paper, we apply three state-pf-the-art ILP systems
	to learn how to detect traffic problems.",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/spanish.pdf",
	YEAR = 1998,
        EDITOR = "C.D. Page",
	PAGES = "281-290",
	BOOKTITLE = "Proc.\ of the 8th International Workshop on Inductive Logic
	  Programming (ILP-98)",
	PUBLISHER = "Springer-Verlag",
	SERIES = "LNAI 1446",
	ADDRESS= "Berlin"}
@inproceedings{mugg:ilp98invited,
	AUTHOR = "S.H. Muggleton",
	TITLE = "Advances in {ILP} theory and implementations",
	ABSTRACT = "A strong linkage exists between advances in applications,
	implementations and theory within Inductive Logic Programming (ILP).
	Early ILP systems, such as FOIL, Golem and LINUS learned single
	predicate definitions from positive and negative examples and
	extensional background knowledge. They also employed strong learning
	biases such as ij-determinacy.  Although these systems found a number
	of applications, they had problems in areas such as molecular
	biology and natural language learning.

	General mechanisms for inverting entailment have now been
	developed which support the use-of non-ground background knowledge,
	and the revision of multiple inter-related predicates. ILP theory
	results concerning complete refinement graph operators now allow
	efficient admissible searches. The absolute requirement for negative
	examples (rare within natural language domains) has been eased by
	Bayesian analysis of learning from positive-only examples.  Bayesian
	approaches have also supported sample complexity analysis of predicate
	invention within the framework of repeat learning.  In this framework
	it is assumed that the learner's prior is not equivalent to the
	distribution from which the teacher is sampling targets. By providing
	a series of sessions the learner is able to update the initial prior
	by adding and deleting background predicates.  Within the Bayesian
	framework stochastic logic program representations have been used to
	estimate the distribution of examples over the instance space.
	Stochastic logic programs are a generalisation of hidden Markov models
	and stochastic grammars.

	Apart from a few special cases PAC-learning results have been largely
	negative for ILP. This is in large part due to the fact that testing
	satisfiability is intractrable for most interesting subsets of
	first-order Horn logic.  The development of Bayesian approaches
	to ILP supported the development of U-learnability, which allows
	classes of distributions over the hypotheses. Here it was shown that
	for any exponential-decay distribution the class of time-bounded
	logic-programs is polynomially U-learnable.
	The use of such bounds on proof depth is common within ILP
	systems. Although logically impure, this approach allows general-purpose
	flexible representations, while maintaining termination guarantees.",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ilp98:invited.pdf",
	YEAR = 1998,
        EDITOR = "C.D. Page",
	NOTE = "Abstract of keynote presentation",
	PAGES = "9",
	BOOKTITLE = "Proc.\ of the 8th International Workshop on Inductive Logic
	  Programming (ILP-98)",
	PUBLISHER = "Springer-Verlag",
	SERIES = "LNAI 1446",
	ADDRESS= "Berlin"}
@inproceedings{mugg:biochem:ds98,
	AUTHOR = "S.H. Muggleton and A. Srinivasan and R.D. King and M.J.E. Sternberg",
	TITLE = "Biochemical knowledge discovery using {I}nductive {L}ogic
		{P}rogramming",
	ABSTRACT = "Machine Learning algorithms are being increasingly used for
	  knowledge discovery tasks. Approaches can be broadly divided 
	  by distinguishing discovery of procedural from
	  that of declarative knowledge. Client requirements determine
	  which of these is appropriate. This paper discusses an experimental
	  application of machine learning in an area related to drug
	  design. The bottleneck here is in finding appropriate constraints
	  to reduce the large number of candidate molecules
	  to be synthesised and tested. Such constraints can be viewed as
	  declarative specifications of the structural elements necessary
	  for high medicinal activity and low toxicity.
	  The first-order representation used within Inductive Logic
	  Programming (ILP) provides an appropriate description language
	  for such constraints. Within this application area knowledge
	  accreditation requires not only a demonstration of predictive
	  accuracy but also, and crucially, a certification of novel insight
	  into the structural chemistry. This paper describes
	  an experiment in which the ILP system Progol was used to
	  obtain structural constraints associated with mutagenicity
	  of molecules. In doing so Progol found a new indicator
	  of mutagenicity within a subset of previously published data.
	  This subset was already known not to be amenable to statistical
	  regression, though its complement was adequately explained by a
	  linear model. According to the combined accuracy/explanation
	  criterion provided in this paper, on both subsets comparative
	  trials show that Progol's structurally-oriented hypotheses are
	  preferable to those of other machine learning algorithms.",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ds98jnt.pdf",
	YEAR = 1998,
        EDITOR = "H. Motoda",
	BOOKTITLE = "Proc.\ of the first Conference on Discovery Science",
	PUBLISHER = "Springer-Verlag",
	ADDRESS= "Berlin"}
@inproceedings{mugg:invited:ds98,
	AUTHOR = "S.H. Muggleton",
	TITLE = "Knowledge discovery in biological and chemical domains",
	NOTE = "Abstract of keynote talk",
	ABSTRACT = "This talk will review the results of the last few years'
	  academic pilot studies involving the application of ILP to the
	  prediction of protein secondary structure, mutagenicity, structure
	  activity , pharmacophore discovery 
	  and protein fold analysis. While predictive accuracy
	  is the central performance measure of data analytical techniques which
	  generate procedural knowledge (neural nets, decision trees, etc.), the
	  performance of an ILP system is determined both by accuracy and degree
	  of stereo-chemical insight provided. ILP hypotheses can be easily stated
	  in English and exemplified diagrammatically. This allows cross-checking
	  with the relevant biological and chemical literature. Most importantly
	  it allows for expert involvement in human background knowledge
	  refinement and for final dissemination of discoveries to the
	  wider scientific community.  In several of the comparative trials
	  presented ILP systems provided significant chemical and biological
	  insights where other data analysis techniques did not.

	  In his statement of the importance of this line of research to
	  the Royal Society Sternberg emphasised the aspect
	  of joint human-computer collaboration in scientific discoveries.
	  Science is an activity of human societies.
	  It is our belief that computer-based scientific discovery must
	  support strong integration into existing the social environment of
	  human scientific communities. The discovered knowledge
	  must add to and build on existing science. The author believes that
	  the ability to incorporate background knowledge and
	  re-use learned knowledge together with the comprehensibility
	  of the hypotheses, have marked out ILP as a particularly effective
	  approach for scientific knowledge discovery.",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ds98inv.pdf",
	YEAR = 1998,
        EDITOR = "H. Motoda",
	BOOKTITLE = "Proc.\ of the first Conference on Discovery Science",
	PUBLISHER = "Springer-Verlag",
	ADDRESS= "Berlin"}
@inproceedings{muggbain:ap,
	AUTHOR = "S.H. Muggleton and M. Bain",
	TITLE = "Analogical Prediction",
	YEAR = 1999,
	BOOKTITLE = "Proc.\ of the 9th International Workshop on Inductive Logic
	  Programming (ILP-99)",
	ABSTRACT = "Inductive Logic Programming (ILP) involves constructing an hypothesis
	$H$ on the basis of background knowledge $B$ and training examples
	$E$. An independent test set is
	used to evaluate the accuracy of $H$. This paper concerns an alternative
	approach called Analogical Prediction (AP). AP takes $B,E$ and
	then for each test example $\langle x,y\rangle$
	forms an hypothesis $H_x$ from $B,E,x$. Evaluation of AP
	is based on estimating the probability that $H_{x}(x)=y$ for a randomly chosen
	$\langle x,y\rangle$. AP has been implemented
	within CProgol4.4. Experiments in the paper show that on English past tense data
	AP has significantly higher predictive accuracy on this data than both
	previously reported
	results and CProgol in inductive mode. However, on KRK illegal AP does not
	outperform CProgol in inductive mode. We conjecture that AP has
	advantages for domains in which a large proportion of the examples
	must be treated as exceptions with respect to the hypothesis vocabulary.
	The relationship of AP to analogy and instance-based learning
	is discussed. Limitations of the given implementation of
	AP are discussed and improvements suggested.",
	PAGES = "234-244",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ap.pdf",
	PUBLISHER = "Springer-Verlag",
	ADDRESS= "Berlin"}
@inproceedings{parskhanmug:trec,
	AUTHOR = "R. Parson and K. Khan and S.H. Muggleton",
	TITLE = "Theory recovery",
	ABSTRACT = "In this paper we examine the problem of repairing incomplete
	  background knowledge using Theory Recovery.  Repeat Learning
	  under ILP considers the problem of updating background knowledge in
	  order to progressively increase the performance of an ILP algorithm
	  as it tackles a sequence of related learning problems.  Theory
	  recovery is suggested as a suitable mechanism.  A bound is derived
	  for the performance of theory recovery in terms of the information
	  content of the missing predicate definitions.  Experiments are
	  described that use the logical back-propagation ability of
	  Progol 5.0 to perform theory recovery.  The experimental results are
	  consistent with the derived bound.",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/trec.pdf",
	YEAR = 1999,
	BOOKTITLE = "Proc.\ of the 9th International Workshop on Inductive Logic
	  Programming (ILP-99)",
	PUBLISHER = "Springer-Verlag",
	ADDRESS= "Berlin"}
@inproceedings{mugg:slplearn,
         AUTHOR = "S.H. Muggleton",
         TITLE = "Learning Stochastic Logic Programs",
         BOOKTITLE = "Proceedings of the AAAI2000 workshop
	 		on Learning Statistical Models from
			Relational Data",
	 URL = "http://www.doc.ic.ac.uk/\~shm/Papers/slplearn.pdf",
         EDITOR = "Lise Getoor and David Jensen",
         PUBLISHER = "AAAI",
         YEAR = 2000}
@inproceedings{mugg:s.pdfem,
         AUTHOR = "S.H. Muggleton",
         TITLE = "Semantics and derivation for Stochastic Logic Programs",
         BOOKTITLE = "Proceedings of the UAI2000 workshop
	 		on Knowledge-Data Fusion",
	 URL = "http://www.doc.ic.ac.uk/\~shm/Papers/slpsem.pdf",
         EDITOR = "Richard Dybowski",
         PUBLISHER = "UAI",
         YEAR = 2000}
@inproceedings{mugg:icml2k,
	AUTHOR = "S.H. Muggleton and C.H. Bryant and A. Srinivasan",
	TITLE = "Learning {C}homsky-like Grammars for Biological Sequence Families",
	URL = "ftp://ftp.cs.york.ac.uk/pub/aig/Papers/bryant/bryant_icml2k.pdf",
	BOOKTITLE = "Proceedings of the Seventeenth International
	Conference on Machine Learning",
	PAGES = "631--638",
	PUBLISHER = "San Francisco, CA: Morgan Kaufmann",
	ADDRESS = "Stanford University, USA",
	YEAR = 2000,
	ABSTRACT = "This paper presents a new method of measuring
	performance when positives are rare and investigates whether
	Chomsky-like grammar representations are useful for learning
	accurate comprehensible predictors of members of biological
	sequence families. The positive-only learning framework of the
	Inductive Logic Programming (ILP) system CProgol is used to
	generate a grammar for recognising a class of proteins known
	as human neuropeptide precursors (NPPs). As far as these
	authors are aware, this is both the first biological grammar
	learnt using ILP and the first real-world scientific
	application of the positive-only learning framework of
	CProgol. Performance is measured using both predictive
	accuracy and a new cost function, {\em Relative Advantage}
	($RA$). The $RA$ results show that searching for NPPs by using
	our best NPP predictor as a filter is more than 100 times more
	efficient than randomly selecting proteins for synthesis and
	testing them for biological activity. The highest $RA$ was
	achieved by a model which includes grammar-derived
	features. This $RA$ is significantly higher than the best $RA$
	achieved without the use of the grammar-derived features."}

@inproceedings{mugg:ecml2k,
	AUTHOR = "S.H. Muggleton and C.H.Bryant and A.Srinivasan",
	TITLE = "Measuring Performance when Positives are Rare:
	Relative Advantage versus Predictive Accuracy - a
	Biological Case-study",
	URL = "ftp://ftp.cs.york.ac.uk/pub/aig/Papers/bryant/bryant_ecml2k.pdf",
	BOOKTITLE = "Proceedings of the 11th European Conference on
	Machine Learning",
	EDITOR = "R.Lopez de Mantaras and E.Plaza",
	SERIES = "Lecture Notes in Computer Science",
	PUBLISHER = "\copyright Springer Verlag",
	ADDRESS = "http://www.springer.de/comp/lncs/index.html",
	YEAR = 2000,
	ABSTRACT = "This paper presents a new method of measuring
	performance when positives are rare and investigates
	whether Chomsky-like grammar representations are
	useful for learning accurate comprehensible
	predictors of members of biological sequence
	families. The positive-only learning framework of the
	Inductive Logic Programming (ILP) system CProgol is
	used to generate a grammar for recognising a class of
	proteins known as human neuropeptide precursors
	(NPPs). Performance is measured using both predictive
	accuracy and a new cost function, {\em Relative
	Advantage} ($RA$). The $RA$ results show that
	searching for NPPs by using our best NPP predictor as
	a filter is more than 100 times more efficient than
	randomly selecting proteins for synthesis and testing
	them for biological activity.  Predictive accuracy is
	not a good measure of performance for this domain
	because it does not discriminate well between NPP
	recognition models: despite covering varying numbers
	of (the rare) positives, all the models are awarded a
	similar (high) score by predictive accuracy because
	they all exclude most of the abundant negatives."}
@inproceedings{bryant:aisb99,
	AUTHOR = "C.H.Bryant and S.H. Muggleton and C.D.Page and
	M.J.E.Sternberg",
	TITLE = "Combining {A}ctive {L}earning with {I}nductive {L}ogic
	{P}rogramming to close the loop in {M}achine {L}earning",
	URL = "ftp://ftp.cs.york.ac.uk/pub/aig/Papers/bryant/bryant_aisb99.pdf",
	BOOKTITLE = "Proceedings of AISB'99 Symposium on AI and
	Scientific Creativity",
	EDITOR = "S. Colton",
	PAGES = "59--64",
	PUBLISHER = "The Society for the Study of Artificial
	Intelligence and Simulation of Behaviour (AISB)",
	ADDRESS = "http://www.cogs.susx.ac.uk/aisb/",
	YEAR = 1999,
	ABSTRACT = "Machine Learning (ML) systems that produce
	human-comprehensible hypotheses from data are
	typically open loop, with no direct link between
	the ML system and the collection of data.  This
	paper describes the alternative, {\it Closed Loop
	Machine Learning}.  This is related to the area of
	Active Learning in which the ML system actively
	selects experiments to discriminate between
	contending hypotheses.  In Closed Loop Machine
	Learning the system not only selects but also
	carries out the experiments in the learning domain.
	ASE-Progol, a Closed Loop Machine Learning system,
	is proposed.  ASE-Progol will use the ILP system
	Progol to form the initial hypothesis set.  It will
	then devise experiments to select between competing
	hypotheses, direct a robot to perform the
	experiments, and finally analyse the experimental
	results.  ASE-Progol will then revise its
	hypotheses and repeat the cycle until a unique
	hypothesis remains.  This will be, to our
	knowledge, the first attempt to use a robot to
	carry out experiments selected by Active Learning
	within a real world application."}
@inproceedings{muggbryant:logprop,
	AUTHOR = "S.H. Muggleton and C.H. Bryant",
	TITLE = "Theory completion using Inverse Entailment",
	ABSTRACT = "The main real-world applications of Inductive Logic Programming (ILP) to
	  date involve the ``Observation Predicate Learning''
	  (OPL) assumption, in which both the examples and hypotheses define the
	  same predicate. However, in both scientific discovery and language learning
	  potential applications exist in which OPL does not hold.
	  OPL is ingrained within the theory and performance testing of
	  Machine Learning.  A general ILP technique called
	  ``Theory Completion using Inverse Entailment'' (TCIE) is introduced which
	  is applicable to non-OPL applications.  TCIE is based on inverse
	  entailment and is closely allied to abductive inference. The implementation
	  of TCIE within Progol5.0 is described. The implementation uses
	  contra-positives in a similar way to Stickel's Prolog Technology
	  Theorem Prover.  Progol5.0 is tested on two different
	  data-sets.  The first dataset involves a grammar which translates numbers
	  to their representation in English. The second dataset involves
	  hypothesising the function of unknown genes within a network of
	  metabolic pathways. On both datasets near complete recovery of
	  performance is achieved after relearning when randomly chosen
	  portions of background knowledge are removed.
	  Progol5.0's running times for experiments in this paper
	  were typically under 6 seconds on a standard laptop PC.",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/logprop.pdf",
	YEAR = 2000,
	BOOKTITLE = "Proc.\ of the 10th International Workshop on Inductive Logic
	  Programming (ILP-00)",
	PUBLISHER = "Springer-Verlag",
	PAGES = "130--146",
	ADDRESS= "Berlin"}
@inproceedings{alimugg:ilp2000,
   Author = {A. Tamaddoni-Nezhad and S.H. Muggleton},
   Title = {Searching the Subsumption Lattice by a Genetic Algorithm},
   Booktitle = {Proceedings of the 10th International Conference on
		Inductive Logic Programming},
   Editor = {J. Cussens and A. Frisch},
   Publisher = {Springer-Verlag},
   URL = "http://www.doc.ic.ac.uk/\~shm/Papers/gailp00.pdf",
   Year  = 2000,
   ISBN  = {3-540-67795-X},
   Pages = {243--252}
}
@inproceedings{alimugg:ga_ilp,
	AUTHOR = "A. Tamaddoni-Nezhad and S.H. Muggleton",
	TITLE = "Using Genetic Algorithms for Learning Clauses in First-Order Logic",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/gailp.pdf",
	YEAR = 2001,
	EDITORS = "Spector and L. and E. Goodman and A. Wu and W.B. Langdon
		and H.-M. Voigt and M. Gen and S. Sen and M. Dorigo and
		S. Pezeshk and M. Garzon and E. Burke",
	BOOKTITLE = "Proceedings of the Genetic and Evolutionary Computation
		Conference, GECCO-2001",
	PAGES = "639--646",
	PUBLISHER = "Morgan Kaufmann Publishers",
	ADDRESS= "San Francisco, CA"}
@inproceedings{diffslp:mugg,
	AUTHOR = "S.H. Muggleton",
	TITLE = "Learning structure and parameters of Stochastic Logic Programs",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/diffslp.pdf",
	YEAR = 2002,
	PAGES = "198--206",
	Booktitle = {Proceedings of the 12th International Conference on
		Inductive Logic Programming},
	PUBLISHER = "Springer-Verlag" }
@inproceedings{alimugg:ga_ilp02,
	AUTHOR = "A. Tamaddoni-Nezhad and S.H. Muggleton",
	TITLE = "A genetic algorithms approach to {ILP}",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/gailp02.pdf",
	YEAR = 2002,
	PAGES = "285--300",
	Booktitle = {Proceedings of the 12th International Conference on
		Inductive Logic Programming},
	PUBLISHER = "Springer-Verlag" }
@inproceedings{puechmuggl:comparison,
	AUTHOR = "A. Puech and S.H. Muggleton",
	TITLE = "A Comparison of Stochastic Logic Programs and {B}ayesian
		Logic Programs",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/puech-paper2.pdf",
	YEAR = 2003,
	Booktitle = {IJCAI03 Workshop on Learning Statistical Models from
		Relational Data},
	PUBLISHER = "IJCAI" }
@inproceedings{muggaliwat:enzymes,
	AUTHOR = "S.H. Muggleton and A. Tamaddoni-Nezhad and H. Watanabe",
	TITLE = "Induction of enzyme classes from biological databases",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ilp03_eclass.pdf",
	YEAR = 2003,
	PAGES = "269-280",
	Booktitle = {Proceedings of the 13th International Conference on
		Inductive Logic Programming},
	SERIES = "LNAI 2835",
	PUBLISHER = "Springer-Verlag" }
@inproceedings{coltonmugg:ILPmaths,
	AUTHOR = "S. Colton and S.H. Muggleton",
	TITLE = "{ILP} for Mathematical Discovery",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ILPmaths.pdf",
	PAGES = "93--111",
	YEAR = 2003,
	SERIES = "LNAI 2835",
	Booktitle = {Proceedings of the 13th International Conference on
		Inductive Logic Programming},
	PUBLISHER = "Springer-Verlag" }
inproceedings{lau2003learning,
  title={Learning programs from traces using version space algebra},
  author={Lau, Tessa and Domingos, Pedro and Weld, Daniel S},
  booktitle={Proceedings of the 2nd international conference on Knowledge capture},
  pages={36--43},
  year={2003},
  organization={ACM}
}
@inproceedings{lodhimugg:ensemble,
	AUTHOR = "H. Lodhi and S.H. Muggleton",
	TITLE = "Modelling Metabolic Pathways Using Stochastic Logic
		Programs-Based Ensemble Methods",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ensemble.pdf",
	YEAR = 2004,
	Booktitle = {Proceedings of the 2nd International Conference on
		Computational  Methods in System Biology},
	PUBLISHER = "Springer-Verlag" }
@inproceedings{alimugg:metabduce,
	AUTHOR = "A. Tamaddoni-Nezhad and A. Kakas and
		S.H. Muggleton and F. Pazos",
	TITLE = "Modelling inhibition in metabolic pathways through
		Abduction and Induction",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/metabduce.pdf",
	PAGES = "305--322",
	YEAR = 2004,
	Booktitle = {Proceedings of the 14th International Conference on
		Inductive Logic Programming},
	SERIES = "LNAI 3194",
	PUBLISHER = "Springer-Verlag" }
@inproceedings{alimuggl:ijcai03,
         AUTHOR = "A. Tamaddoni-Nezhad and S.H. Muggleton and J. Bang",
	TITLE = " A {B}ayesian Model for Metabolic Pathways",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/metabayes.pdf",
	YEAR = 2003,
	Booktitle = {International Joint Conference on Artificial
		Intelligence (IJCAI03) Workshop
		on Learning Statistical Models from Relational Data},
	pages =        {50-57},
	PUBLISHER = "IJCAI" }
@inproceedings{mugg:mlsysbio,
	AUTHOR = "S.H. Muggleton",
	TITLE = "Machine Learning for Systems Biology",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/mlsysbio.pdf",
	YEAR = 2005,
	PAGES = "416--423",
	Booktitle = {Proceedings of the 15th International Conference on
		Inductive Logic Programming},
	SERIES = "LNAI 3625",
	PUBLISHER = "Springer-Verlag" }
@inproceedings{mugg:svilp,
	AUTHOR = "S.H. Muggleton and H. Lodhi and A. Amini and M.J.E. Sternberg",
	TITLE = "Support {V}ector {I}nductive {L}ogic {P}rogramming",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/svilp.pdf",
	YEAR = 2005,
	PAGES = "163--175",
	Booktitle = {Proceedings of the 8th International Conference on
		Discovery Science},
	SERIES = "LNAI 3735",
	PUBLISHER = "Springer-Verlag" }
@inproceedings{mugg:learnstoch,
	AUTHOR = "H. Watanabe and S.H. Muggleton",
	TITLE = "Learning {S}tochastic {L}ogical {A}utomaton",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/stochlog.pdf",
	YEAR = 2005,
	Booktitle = {Proceedings of the 19th Annual Conferences of JSAI},
	PAGES = "201--211",
	SERIES = "LNCS 4012",
	PUBLISHER = "Springer-Verlag" }
@inproceedings{mugg:accelfidj,
	AUTHOR = "A. Fidjeland, W. Luk, S.H. Muggleton",
	TITLE = "Scalable acceleration of inductive logic programs",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/accelfidj.pdf",
	YEAR = 2002,
	PAGES = "252 - 259",
	Booktitle = {IEEE international conference on field-programmable technology},
	PUBLISHER = "IEEE"}
@inproceedings{alimug:metabduceieee,
	AUTHOR = "A. Tamaddoni-Nezhad and R. Chaleil and A. Kakas and S.H. Muggleton",
	TITLE = "Abduction and induction for learning models of inhibition in metabolic networks",
	YEAR = 2005,
	Booktitle = {Proceedings of the Fourth International Conference on
		Machine Learning and Applications, ICMLA'05},
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/metabduceieee.pdf",
	PUBLISHER = "IEEE Computer Society" }
@inproceedings{mugg:chemturing,
	AUTHOR = "S.H. Muggleton",
	TITLE = "Towards {U}niversal {C}hemical {T}uring {M}achines",
	YEAR = 2006,
	PAGES = "1527--1529",
	Booktitle = {Proceedings of the Twenty-First National Conference
	                on Artificial Intelligence, AAAI-06},
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/chemturing.pdf",
	PUBLISHER = "AAAI Press" }
@inproceedings{alimugg:largescale,
	AUTHOR = "A. Tamaddoni-Nezhad and R. Greaves and S.H. Muggleton",
	TITLE = "Large-scale online learning using Analogical Prediction",
	YEAR = 2006,
	Booktitle = {Short Paper Proceedings of the 16th International
		Conference on Inductive Logic Programming},
	PUBLISHER = "University of Corunna" }
@inproceedings{alimugg:qg-ga,
	AUTHOR = "S.H. Muggleton and A. Tamaddoni-Nezhad",
	TITLE = "{QG/GA}: A Stochastic Search Approach for {P}rogol",
	YEAR = 2006,
	Booktitle = {Proceedings of the 16th International Conference on
		Inductive Logic Programming},
	SERIES = "LNAI 4455",
	PAGES = "37--39",
	PUBLISHER = "Springer-Verlag" }
@inproceedings{arvmugg:slpabduce,
	AUTHOR = "A. Arvanitis and S.H. Muggleton and J. Chen and H. Watanabe",
	TITLE = "Abduction with Stochastic Logic Programs based on a
		Possible Worlds Semantics",
	YEAR = 2006,
	Booktitle = {Short Paper Proceedings of the 16th International
		Conference on Inductive Logic Programming},
	PUBLISHER = "University of Corunna" }
@inproceedings{chenmugg:blpvslp,
	AUTHOR = "J. Chen and S.H. Muggleton",
	TITLE = "A Revised Comparison of {B}ayesian Logic Programs and
		Stochastic Logic Programs",
	YEAR = 2006,
	Booktitle = {Short Paper Proceedings of the 16th International
		Conference on Inductive Logic Programming},
	PUBLISHER = "University of Corunna" }
@inproceedings{chenmugg:protslp,
	AUTHOR = "J. Chen and S.H. Muggleton",
	TITLE = "Multi-Class Protein Fold Prediction Using Stochastic
		Logic Programs",
	YEAR = 2006,
	Booktitle = {Short Paper Proceedings of the 16th International
		Conference on Inductive Logic Programming},
	PUBLISHER = "University of Corunna" }
@inproceedings{oteromugg:mccarthy,
	AUTHOR = "R. Otero and S.H. Muggleton",
	TITLE = "On {McCarthy}'s Appearance and Reality Problem",
	YEAR = 2006,
	Booktitle = {Short Paper Proceedings of the 16th International
		Conference on Inductive Logic Programming},
	PUBLISHER = "University of Corunna" }
@inproceedings{muggpahl:blprmm,
	AUTHOR = "S.H. Muggleton and N. Pahlavi",
	TITLE = "The Complexity of Translating BLPs to RMMs",
	YEAR = 2006,
	Booktitle = {Short Paper Proceedings of the 16th International
		Conference on Inductive Logic Programming},
	PUBLISHER = "University of Corunna" }
@inproceedings{muggpahl:hodatalog,
	AUTHOR = "N. Pahlavi and S.H. Muggleton",
	TITLE = "Towards Efficient Higher-order Logic Learning in a First-order Datalog Framework",
	YEAR = 2012,
	Booktitle = {Latest Advances in Inductive Logic Programming},
	URL = "http://ilp11.doc.ic.ac.uk/short_papers/ilp2011_submission_46.pdf",
	PUBLISHER = "Imperial College Press",
	PAGES = "209--216"}
@inproceedings{watmugg:compactheory,
	AUTHOR = "H. Watanabe and K. Inoue and Stephen Muggleton",
	TITLE = "Complexity Analysis of Abductive Action Theory",
	YEAR = 2006,
	Booktitle = {Short Paper Proceedings of the 16th International
		Conference on Inductive Logic Programming},
	PUBLISHER = "University of Corunna" }
@inproceedings{chenmugg:problabels,
	AUTHOR = "J. Chen and S.H. Muggleton and J. Santos",
	TITLE = "Learning Probabilistic Learning Models from Examples
		(extended abstract)",
	YEAR = 2007,
	Booktitle = {Proceedings of the 17th International
		Conference on Inductive Logic Programming},
	SERIES = "LNAI 4894",
	PAGES = "22--23",
	PUBLISHER = "Springer-Verlag" }
@inproceedings{alimug:ierefinement,
	AUTHOR = "A. Tamaddoni-Nezhad and S.H. Muggleton",
	TITLE = " A Note on Refinement Operators for {IE}-Based {ILP} Systems",
	YEAR = 2008,
	Booktitle = {Proceedings of the 18th International
		Conference on Inductive Logic Programming},
	SERIES = "LNAI 5194",
	PAGES = "297--314",
	NOTE = " DOI: 10.1007/978-3-540-85928-4_23",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ierefinement.pdf",
	PUBLISHER = "Springer-Verlag" }
@inproceedings{lodhimug:msvilp,
	AUTHOR = "H. Lodhi and S.H. Muggleton and M.J.E. Sternberg",
	TITLE = "Learning Large Margin First Order Decision Lists for Multi-Class Classification",
	YEAR = 2009,
	Booktitle = {Proceedings of the 12th International
		Conference on Discovery Science},
	SERIES = "LNAI 5808",
	PAGES = "163--183",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/msvilp.pdf",
	PUBLISHER = "Springer-Verlag" }
@inproceedings{santosmug:head,
	AUTHOR = "J. Santos and A. Tamaddoni-Nezhad and S.H. Muggleton",
	TITLE = "An {ILP} System for Learning Head Output Connected Predicates",
	YEAR = 2009,
	Booktitle = {Proceedings of the 14th Portuguese
		Conference on Artificial Intelligence},
	SERIES = "LNAI 5816",
	PAGES = "150--159",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/EPIA09.pdf",
	PUBLISHER = "Springer-Verlag" }
@inproceedings{fidjmug:fpga08,
	AUTHOR = "A. Fidjeland and W. Luk and S.H. Muggleton",
	TITLE = "A Customisable Multiprocessor for Application-Optimised Inductive Logic Programming",
	YEAR = 2008,
	Booktitle = {Proceedings of Visions of Computer Science -
		BCS International Academic Conference},
	SERIES = "LNAI 5816",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/fpga08.pdf",
	PUBLISHER = "British Computer Society" }
@inproceedings{mugsantos:progolem,
	AUTHOR = "S.H. Muggleton and J. Santos and A. Tamaddoni-Nezhad",
	TITLE = "{ProGolem}: a system based on relative minimal generalisation",
	YEAR = 2010,
	Booktitle = {Proceedings of the 19th International
		Conference on Inductive Logic Programming},
	SERIES = "LNCS 5989",
	PAGES = "131--148",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/progolem.pdf",
	PUBLISHER = "Springer-Verlag" }
@inproceedings{mugsantos:toplog,
	AUTHOR = "S.H. Muggleton and J. Santos and A.  Tamaddoni-Nezhad",
	TITLE = "{TopLog}: {ILP} Using a Logic Program Declarative Bias",
	YEAR = 2010,
	Booktitle = {Proceedings of the International
		Conference on Logic Programming 2008},
	SERIES = "LNCS 5366",
	PAGES = "687--692",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/toplog.pdf",
	PUBLISHER = "Springer-Verlag" }
@inproceedings{mugpaes:chess,
	AUTHOR = "S.H. Muggleton and A. Paes and V. Santos Costa and
		G. Zaverucha",
	TITLE = "Chess revision: acquiring the rules of chess variants
		through {FOL} theory revision from examples",
	YEAR = 2010,
	Booktitle = {Proceedings of the 19th International
		Conference on Inductive Logic Programming (ILP 2009)},
        EDITOR = "Luc De Raedt",
	SERIES = "LNCS 5989",
	PAGES = "123--130",
	PUBLISHER = "Springer-Verlag",
        ADDRESS = "Berlin",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/paeschess.pdf",
	PUBLISHER = "Springer-Verlag" }
@inproceedings{watmugg:large,
	AUTHOR = "H. Watanabe and S.H. Muggleton",
	TITLE = "Can {ILP} be applied to large datasets?",
	YEAR = 2010,
	Booktitle = {Proceedings of the 19th International
		Conference on Inductive Logic Programming (ILP 2009)},
        EDITOR = "Luc De Raedt",
	SERIES = "LNAI 5989",
	PAGES = "249--256",
	PUBLISHER = "Springer-Verlag",
        ADDRESS = "Berlin",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/watlarge.pdf",
	PUBLISHER = "Springer-Verlag" }
@inproceedings{chen:dtlp,
	AUTHOR = "J. Chen S.H. Muggleton",
	TITLE = "Decision-Theoretic Logic Programs",
	YEAR = 2010,
	NOTE = {19th International
		Conference on Inductive Logic Programming, Poster presentation},
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/dtlp.pdf" }
@inproceedings{pahlmug:hollfoll,
	AUTHOR = "N.  Pahlavi and S.H. Muggleton",
	TITLE = "Can {HOLL} outperform {FOLL}?",
	YEAR = 2011,
	SERIES = "LNAI 6489",
	Booktitle = {Proceedings of the 20th International
		Conference on Inductive Logic Programming (ILP 2010)},
        EDITOR = "Paolo Frasconi and Francesca A. Lisi",
	PUBLISHER = "Springer-Verlag",
        ADDRESS = "Berlin",
	PAGES = "198--205" }
@inproceedings{alimugg:stochrefine,
	AUTHOR = "A.  Tamaddoni-Nezhad and S.H. Muggleton",
	TITLE = "Stochastic Refinement",
	YEAR = 2011,
	SERIES = "LNAI 6489",
	Booktitle = {Proceedings of the 20th International
		Conference on Inductive Logic Programming (ILP 2010)},
	PAGES = "222--237",
        EDITOR = "Paolo Frasconi and Francesca A. Lisi",
	PUBLISHER = "Springer-Verlag",
        ADDRESS = "Berlin",
        URL = "http://www.doc.ic.ac.uk/\~shm/Papers/Stochastic_refinement.pdf" }
@inproceedings{mugg:uic,
	AUTHOR = "S.H. Muggleton and J. Chen and H. Watanabe and S. Dunbar
		and C. Baxter and R. Currie and J.D. Salazar and 
		J. Taubert and M.J.E. Sternberg",
	TITLE = "Variation of background knowledge in an industrial
		application of {ILP}",
	YEAR = 2011,
	SERIES = "LNAI 6489",
	Booktitle = "Proceedings of the 20th International
		Conference on Inductive Logic Programming (ILP 2011)",
	PAGES = "158--170",
        EDITOR = "Paolo Frasconi and Francesca A. Lisi",
	PUBLISHER = "Springer-Verlag",
        ADDRESS = "Berlin",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/uic.pdf" }
@inproceedings{mugg:mctoplog,
	AUTHOR = "S.H. Muggleton and D. Lin and A. Tamaddoni-Nezhad",
	TITLE = "{MC}-{T}opLog: Complete Multi-clause Learning Guided by A Top Theory",
	YEAR = 2012,
	SERIES = "LNAI 7207",
	BOOKTITLE = "Proceedings of the 21st International
		Conference on Inductive Logic Programming (ILP 2011)",
        EDITOR = "Stephen H. Muggleton and Alireza Tamaddoni-Nezhad and
		Francesca A. Lisi",
	PAGES = "238--254",
	PUBLISHER = "Springer-Verlag",
        ADDRESS = "Berlin",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/mctoplog.pdf" }
@inproceedings{mugg:eco,
	AUTHOR = "A. Tamaddoni-Nezhad and D. Bohan and A. Raybould and S.H. Muggleton",
	TITLE = "Machine Learning a Probabilistic Network of Ecological Interactions",
	YEAR = 2012,
	SERIES = "LNAI 7207",
	BOOKTITLE = "Proceedings of the 21st International
		Conference on Inductive Logic Programming (ILP 2011)",
        EDITOR = "Stephen H. Muggleton and Alireza Tamaddoni-Nezhad and
		Francesca A. Lisi",
	PUBLISHER = "Springer-Verlag",
        ADDRESS = "Berlin",
	PAGES = "332--346"}
@inproceedings{uic2,
	AUTHOR = "D. Lin and J. Chen and H. Watanabe and S.H. Muggleton and P. Jain and M. Sternberg and C. Baxter and R. Currie and S. Dunbar and M. Earll and D. Salazar",
	TITLE = "Does Multi-clause Learning Help in Real-world Applications?",
	YEAR = 2012,
	SERIES = "LNAI 7207",
	BOOKTITLE = "Proceedings of the 21st International
		Conference on Inductive Logic Programming (ILP 2011)",
        EDITOR = "Stephen H. Muggleton and Alireza Tamaddoni-Nezhad and
		Francesca A. Lisi",
	PAGES = "221--237",
	PUBLISHER = "Springer-Verlag",
        ADDRESS = "Berlin",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/uicmulticlause.pdf" }
@inproceedings{mugg:metagolD,
	AUTHOR = "S.H. Muggleton and D. Lin",
	TITLE = "Meta-Interpretive Learning of Higher-Order Dyadic Datalog: Predicate Invention revisited",
	YEAR = 2013,
        BOOKTITLE = "Proceedings of the 23rd International Joint
		Conference Artificial Intelligence (IJCAI 2013)",
	PAGES = "1551--1557",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/metagold.pdf" }
@inproceedings{mugg:metabias,
	AUTHOR = "D. Lin and E. Dechter and K. Ellis and J.B. Tenenbaum and
		S.H. Muggleton",
	TITLE = "Bias reformulation for one-shot function induction",
	YEAR = 2014,
	BOOKTITLE = "Proceedings of the 23rd European Conference
		on Artificial Intelligence (ECAI 2014)",
	PUBLISHER = "IOS Press",
	ADDRESS = "Amsterdam",
	PAGES = "525--530",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/metabias.pdf" }
@inproceedings{mugg:metabayes,
	AUTHOR = "S.H. Muggleton and D. Lin and J. Chen and A. Tamaddoni-Nezhad
",
	TITLE = "MetaBayes: Bayesian Meta-Interpretative Learning using
		Higher-Order Stochastic Refinement",
	YEAR = 2014,
	BOOKTITLE = "Proceedings of the 23rd International
		Conference on Inductive Logic Programming (ILP 2013)",
	PAGES = "1--17",
	PUBLISHER = "Springer-Verlag",
        EDITOR = "Gerson Zaverucha and Vitor Santos Costa and Aline Marins Paes",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/metabayeslong07.pdf",
        NOTE = "LNAI 8812",
        ADDRESS = "Berlin"}
@inproceedings{mugg:minmeta,
	AUTHOR = "A. Cropper and S.H. Muggleton",
	TITLE = "Logical minimisation of meta-rules within
		Meta-Interpretive Learning",
	YEAR = 2015,
	BOOKTITLE = "Proceedings of the 24th International
		Conference on Inductive Logic Programming",
	PAGES = "65--78",
	PUBLISHER = "Springer-Verlag",
        NOTE = "LNAI 9046",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/minmeta.pdf" }
@inproceedings{atn:predeco,
	AUTHOR = "A. Tamaddoni-Nezhad and D. Bohan and A. Raybould and
		S.H. Muggleton",
	TITLE = "Towards machine learning of predictive models from
		ecological data",
	YEAR = 2015,
	BOOKTITLE = "Proceedings of the 24th International
		Conference on Inductive Logic Programming",
	PAGES = "159--173",
	PUBLISHER = "Springer-Verlag",
        NOTE = "LNAI 9046" ,
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/predeco.pdf" }
@inproceedings{mugg:metagolO,
	AUTHOR = "A. Cropper and S.H. Muggleton",
	TITLE = "Learning Efficient Logical Robot Strategies Involving Composable Objects",
	YEAR = 2015,
        BOOKTITLE = "Proceedings of the 24th International Joint
		Conference Artificial Intelligence (IJCAI 2015)",
	PAGES = "3423--3429",
	PUBLISHER = "IJCAI",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/metagolo.pdf" }
@inproceedings{cropmugg:datacurate,
	AUTHOR = "A. Cropper and A. Tamaddoni-Nezhad and and S.H. Muggleton",
	TITLE = "Meta-Interpretive Learning of Data Transformation Programs",
	YEAR = 2016,
	Booktitle = {Proceedings of the 25th International
                Conference on Inductive Logic Programming},
	PAGES = "46--59",
	PUBLISHER = "Springer-Verlag",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/datacurate.pdf" }
@inproceedings{farqmugg:typedmil,
	AUTHOR = "C. Farquhar and G. Grov A. Cropper and S.H. Muggleton and A. Bundy",
	TITLE = "Typed meta-interpretive learning for proof strategies",
	YEAR = 2015,
	Booktitle = {Short Paper Proceedings of the 25th International
		Conference on Inductive Logic Programming},
	PUBLISHER = "National Institute of Informatics, Tokyo",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/typemilproof.pdf" }
@inproceedings{croppmugg:picomp,
	AUTHOR = "A. Cropper and S.H. Muggleton",
	TITLE = "Can predicate invention compensate for incomplete background knowledge?",
	YEAR = 2015,
	Booktitle = {Thirteenth Scandinavian Conference on Artificial Intelligence (SCAI 2015)},
	PUBLISHER = "IOS Press",
	PAGES = {27--36},
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/scai2015.pdf" }
@inproceedings{daimugg:logvis,
	AUTHOR = "W-Z Dai and S.H. Muggleton and Z-H Zhou",
	TITLE = "Logical {V}ision: Meta-Interpretive Learning for Simple
		Geometrical Concepts",
	YEAR = 2015,
	Booktitle = {Late Breaking Paper Proceedings of the 25th International Conference on Inductive Logic Programming},
	PUBLISHER = "CEUR",
	PAGES = {1--16},
	URL = "http://ceur-ws.org/Vol-1636"}
@inproceedings{mugg:metagolAI,
	AUTHOR = "A. Cropper and S.H. Muggleton",
	TITLE = "Learning Higher-Order Logic Programs Through Abstraction and Invention",
	YEAR = 2016,
        BOOKTITLE = "Proceedings of the 25th International Joint
		Conference Artificial Intelligence (IJCAI 2016)",
	PAGES = "1418--1424",
	PUBLISHER = "IJCAI",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/metafunc.pdf" }
@inproceedings{mugg:CompInv,
	AUTHOR = {U. Schmid and C. Zeller and T. Besold and A. Tamaddoni-Nezhad
		and S.H. Muggleton},
	TITLE = "How does Predicate Invention affect Human Comprehensibility?",
	YEAR = 2017,
	BOOKTITLE = "Proceedings of the 26th International
		Conference on Inductive Logic Programming",
	PUBLISHER = "Springer-Verlag",
        EDITOR = "Alessandra Russo and James Cussens",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/compinv.pdf",
        PAGES = "52--67",
        ADDRESS = "Berlin"}
@inproceedings{mugg:milachallenges,
	AUTHOR = {S.H. Muggleton},
	TITLE = "Meta-Interpretive Learning: achievements and challenges",
	YEAR = 2017,
	BOOKTITLE = "Proceedings of the 11th International
		Symposium on Rule Technologies, RuleML+RR 2017",
	PUBLISHER = "Springer-Verlag",
        EDITOR = "Roman Kontchakov and Fariba Sadri",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/rulemlabs.pdf",
        PAGES = "1--7",
        NOTE = "LNCS 10364" ,
        ADDRESS = "Berlin"}
@inproceedings{mugg:logvismoon,
	AUTHOR = {W-Z Dai and S.H. Muggleton and J. Wen and 
		A. Tamaddoni-Nezhad and Z-H. Zhou},
	TITLE = "Logical Vision: One-Shot Meta-Interpretive Learning from
		Real Images ",
	YEAR = 2017,
	BOOKTITLE = "Proceedings of the 27th International
		Conference on Inductive Logic Programming",
	PUBLISHER = "Springer-Verlag",
        EDITOR = "Nicholas Lachiche and Christel Vrain",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/logvismoon.pdf",
        PAGES = "46--62",
        ADDRESS = "Berlin"}
@inproceedings{connmugg:predorder,
	AUTHOR = "H. Conn and S.H. Muggleton",
	TITLE = "The Effect of Predicate Order on Curriculum Learning in {ILP}",
	YEAR = 2017,
	Booktitle = {Late Breaking Paper Proceedings of the 27th International
		Conference on Inductive Logic Programming},
	PUBLISHER = "CEUR",
	PAGES = {17--21},
	URL = "http://ceur-ws.org/Vol-2085"}
@inproceedings{mugg:ActiveAgentMIL,
	AUTHOR = {Celine Hocquette and S.H. Muggleton},
	TITLE = "How Much Can Experimental Cost Be Reduced in Active Learning of Agent Strategies?",
	YEAR = 2018,
	BOOKTITLE = "Proceedings of the 28th International
		Conference on Inductive Logic Programming",
	PUBLISHER = "Springer-Verlag",
        EDITOR = "Fabrizio Riguzzi and Elena Bellodi and Riccardo Zese",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/activemil.pdf",
        PAGES = "38--53",
        ADDRESS = "Berlin"}
@inproceedings{patsantzismugg:relevance18,
	AUTHOR = "S. Patsantzis and S.H. Muggleton",
	TITLE = "Which background knowledge is relevant?",
	YEAR = 2018,
	Booktitle = {Late Breaking Paper Proceedings of the 28th International
		Conference on Inductive Logic Programming},
	PUBLISHER = "CEUR",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/relevance18.pdf"}
@inproceedings{mugghoc:botinv,
	AUTHOR = "C. Hocquette and S.H. Muggleton",
	TITLE = "Complete Bottom-Up Predicate Invention in Meta-Interpretive Learning",
	YEAR = 2020,
        BOOKTITLE = "Proceedings of the 29th International Joint
		Conference Artificial Intelligence (IJCAI 2020)",
	PAGES = "2312--2318",
	PUBLISHER = "IJCAI",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/botinv.pdf" }
@inproceedings{muggcrop:ILP30,
	AUTHOR = "A. Cropper and S. Dumancic and S.H. Muggleton",
	TITLE = "Turning 30: New Ideas in Inductive Logic Programming",
	YEAR = 2020,
        BOOKTITLE = "Proceedings of the 29th International Joint
		Conference Artificial Intelligence (IJCAI 2020)",
	PAGES = "4833--4839",
	PUBLISHER = "IJCAI",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ILP30.pdf" }
@inproceedings{muggcrop:holearnpredinv,
	AUTHOR = "A. Cropper and R. Morel and S.H. Muggleton",
	TITLE = "Learning Higher-Order Programs through Predicate Invention",
	YEAR = 2020,
        BOOKTITLE = "Proceedings of the 34th Conference
		on Artificial Intelligence (AAAI 2020)",
	PAGES = "13655--13658",
	PUBLISHER = "AAAI",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/holearnpredinv.pdf" }
@inproceedings{muggdai:neuralmil,
	AUTHOR = "W-Z. Dai and S.H. Muggleton",
	TITLE = "Abductive Knowledge Induction From Raw Data",
	YEAR = 2021,
        BOOKTITLE = "Proceedings of the 30th Conference
		on Artificial Intelligence (IJCAI 2021)",
	PUBLISHER = "IJCAI",
	PAGES = "1845--1851",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/abdmetarawIJCAI.pdf" }
@inproceedings{muggdai:abdoptim,
	AUTHOR = "Y.X. Huang and W.Z. Dai and L.W. Cai and S.H. Muggleton
                        and Y. Jiang",
	TITLE = "Fast abductive learning
                        by similarity-based consistency optimization",
	YEAR = 2021,
        BOOKTITLE = "Advances in Neural Information Processing Systems",
	VOLUME = "34",
	PAGES = "26574--26584",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/NIPSabdopt.pdf"}
@inproceedings{muggdai:abdground,
	AUTHOR = "L.W. Cai and W.Z. Dai and  Y.X. Huang and Y.F. Li7				and S.H. Muggleton and Y. Jiang",
	TITLE = "Abductive Learning with Ground Knowledge Base",
	YEAR = 2021,
        BOOKTITLE = "Proceedings of the 30th Conference
		on Artificial Intelligence (IJCAI 2021)",
	PAGES = "1815-1821",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/IJCAIabdgrnd.pdf"}
@inproceedings{mugg:reduce1,
	AUTHOR = "S.H. Muggleton",
	TITLE = "ReDuce: Linear-time Inductive Compression using Greedy
		Folding",
	YEAR = 2025,
        BOOKTITLE = "Proceedings of the 5th International Joint Conference
			on Learning and Reasoning, IJCLR 2025",
	NOTE = "In Press",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/Reduce.pdf"}