@article{QuinlanC95,
        title = {Induction of Logic Programs: {FOIL} and Related Systems},
        author = {J. R. Quinlan and R. M. Cameron-Jones},
        journal = {New Generation Computing, special issue on Inductive Logic Programming},
        number = {3-4},
        pages = {287--312},
        volume = 13,
        year = 1995,
        url = {http://www.rulequest.com/Personal/q+cj.ngc95.ps},
        description = {foil, particularly dealing with closed worlds and making clauses more understandable},
	biburl = {http://www.bibsonomy.org/bibtex/229521d514edc325b3e1e3e7a88751f3f/emanuel},
	keywords = {applications article enumerative_ip foil ilp induction inductive_programming machine_learning program_synthesis}
}

@article{CameronJonesQ94,
        title = {Efficient Top-Down Induction of Logic Programs},
        address = {New York, NY, USA},
        author = {R. Mike Cameron-Jones and J. Ross Quinlan},
        journal = {SIGART Bulletin},
        number = 1,
        pages = {33--42},
        publisher = {ACM},
        volume = 5,
        year = 1994,
        url = {http://doi.acm.org/10.1145/181668.181676},
        description = {foil in 1994},
	abstract = {FOIL is a system for inducing function-free Horn clause definitions of relations from example and extensionally defined background relations. It demonstrates the successful application of a general to specific approach to clause induction using heuristically guided search. This paper describes the current version of FOIL, assesses its performance and notes areas for improvement. The successful application of similar methods in other systems is reviewed to demonstrate their general utility.},
	biburl = {http://www.bibsonomy.org/bibtex/26d35fd8bdec05ccce1cd5ef370063fd1/emanuel},
	keywords = {applications article enumerative_ip foil ilp induction inductive_programming machine_learning program_synthesis}
}

@article{Quinlan90,
        title = {Learning Logical Definitions from Relations},
        author = {J. R. Quinlan},
        journal = {Machine Learning},
        note = {original foil paper},
        number = 3,
        pages = {239--266},
        volume = 5,
        year = 1990,
        url = {http://dx.doi.org/10.1007/BF00117105},
        description = {original foil paper},
	abstract = {This paper describes FOIL, a system that learns Horn clauses from data expressed as relations. FOIL is based on ideas that have proved effective in attribute-value learning systems, but extends them to a first-order formalism. This new system has been applied successfully to several tasks taken from the machine learning literature.},
	biburl = {http://www.bibsonomy.org/bibtex/226261dd95ddc089124d3e8b44caca699/emanuel},
	keywords = {applications article enumerative_ip foil ilp induction inductive_programming machine_learning program_synthesis}
}

@article{MuggletonR94,
        title = {Inductive Logic Programming: Theory and Methods},
        author = {Stephen H. Muggleton and Luc De Raedt},
        journal = {Journal of Logic Programming},
        pages = {629--679},
        volume = {19,20},
        year = 1994,
        url = {http://www.doc.ic.ac.uk/~shm/Papers/lpj.pdf},
        biburl = {http://www.bibsonomy.org/bibtex/23fee87ed2a546d568c27b46efedf4a03/emanuel},
	keywords = {article ilp induction machine_learning survey}
}

@article{Muggleton91ILP,
        title = {Inductive Logic Programming},
        author = {Stephen Muggleton},
        journal = {New Generation Computing},
        number = 4,
        pages = {295--318},
        volume = 8,
        year = 1991,
        description = {seminal paper on ILP},
	biburl = {http://www.bibsonomy.org/bibtex/253a5b049eb6d07f2b186a19856872227/emanuel},
	keywords = {article ilp induction machine_learning}
}

@article{KitzelmannS06a,
        title = {Inductive Synthesis of Functional Programs: An Explanation Based Generalization Approach},
        address = {Cambridge, MA, USA},
        author = {Emanuel Kitzelmann and Ute Schmid},
        journal = {Journal of Machine Learning Research},
        pages = {429--454},
        publisher = {MIT Press},
        volume = 7,
        year = 2006,
        url = {http://jmlr.csail.mit.edu/papers/v7/kitzelmann06a.html},
        issn = {1533-7928},
	description = {Inductive Synthesis of Functional Programs: An Explanation Based Generalization Approach},
	abstract = {We describe an approach to the inductive synthesis of recursive equations from input/output-examples which is based on the classical two-step approach to induction of functional Lisp programs of Summers (1977). In a first step, I/O-examples are rewritten to traces which explain the outputs given the respective inputs based on a datatype theory. These traces can be integrated into one conditional expression which represents a non-recursive program. In a second step, this initial program term is generalized into recursive equations by searching for syntactical regularities in the term. Our approach extends the classical work in several aspects. The most important extensions are that we are able to induce a set of recursive equations in one synthesizing step, the equations may contain more than one recursive call, and additionally needed parameters are automatically introduced.},
	biburl = {http://www.bibsonomy.org/bibtex/241c72a03ba80767af6f77376acdbeb02/emanuel},
	keywords = {analytical_ip article ebg ifp igor1 induction inductive_programming program_synthesis recursive_program_schemes}
}

@article{Muggleton94Inductive,
        title = {Inductive Logic Programming: Derivations, Successes and Shortcomings},
        author = {Stephen Muggleton},
        journal = {SIGART Bulletin},
        number = 1,
        pages = {5--11},
        volume = 5,
        year = 1994,
        url = {http://www.doc.ic.ac.uk/~shm/Papers/sigart.pdf},
        timestamp = {2007.12.19},
	owner = {martin},
	biburl = {http://www.bibsonomy.org/bibtex/254bb1b24ae39e4e3d408ba21a397dd7f/mh},
	keywords = {1994 article ilp induction inductive_inference inductive_learning machine_learning}
}

@article{Gold67,
        title = {Language identification in the limit},
        author = {E. Mark Gold},
        journal = {Information and Control},
        number = 5,
        pages = {447--474},
        volume = 10,
        year = 1967,
        url = {http://www.isrl.uiuc.edu/~amag/langev/paper/gold67limit.html},
        description = {golds seminal paper on inductive inference},
	abstract = {Language learnability has been investigated. This refers to the following situation: A class of possible languages is specified, together with a method of presenting information to the learner about an unknown language, which is to be chosen from the class. The question is now asked, ``Is the information sufficient to determine which of the possible languages is the unknown language?'' Many definitions of learnability are possible, but only the following is considered here: Time is quantized and has a finite starting time. At each time the learner receives a unit of information and is to make a guess as to the identity of the unknown language on the basis of the information received so far. This process continues forever. The class of languages will be considered learnable with respect to the specified method of information presentation if there is an algorithm that the learner can use to make his guesses, the algorithm having the following property: Given any language of the class, there is some finite time after which the guesses will all be the same and they will be correct.

In this preliminary investigation, a language is taken to be a set of strings on some finite alphabet. The alphabet, is the same for all languages of the class. Several variations of each of the following two basic methods of information presentation are investigated: A text for a language generates the strings of the language in any order such that every string of the language occurs at. least once. An informant for a language tells whether a string is in the language, and chooses the strings in some order such that every string occurs at least once. It was found that the class of context-sensitive languages is learnable from an informant, but that, not even the class of regular languages is learnable from a text. },
	biburl = {http://www.bibsonomy.org/bibtex/2eb8c409fe23b597a953b9981698158cf/emanuel},
	keywords = {article identification_in_the_limit induction machine_learning seminal_paper}
}

@article{Summers77,
        title = {A Methodology for {LISP} Program Construction from Examples},
        address = {New York, NY, USA},
        author = {Phillip D. Summers},
        journal = {Journal of the ACM},
        number = 1,
        pages = {161--175},
        publisher = {ACM},
        volume = 24,
        year = 1977,
        url = {http://doi.acm.org/10.1145/321992.322002},
        description = {The inductive programming seminal paper from Summers. Constructing a linear recursive program by generalising regularities in a finite set of traces and predicates.},
	abstract = {An automatic programming system, THESYS, for constructing recursive LISP programs from examples of what they do is described. The construction methodology is illustrated as a series of transformations from the set of examples to a program satisfying the examples. The transformations consist of (1) deriving the specific computation associated with a specific example, (2) deriving control flow predicates, and (3) deriving an equivalent program specification in the form of recurrence relations. Equivalence between certain recurrence relations and various program schemata is proved. A detailed description of the construction of four programs is presented to illustrate the application of the methodology.},
	biburl = {http://www.bibsonomy.org/bibtex/25126eafebbe355dc1c9425514bb51f3d/emanuel},
	keywords = {analytical_ip article ifp induction inductive_programming program_synthesis seminal_paper thesys}
}

@article{HamfeltFO01,
        title = {Logic Program Synthesis as Problem Reduction Using Combining Forms},
        author = {Andreas Hamfelt and Jørgen Fischer Nilsson and Nikolaj Oldager},
        journal = {Automated Software Engineering},
        number = 2,
        pages = {167--193},
        volume = 8,
        year = 2001,
        url = {http://dx.doi.org/10.1023/A:1008741507024},
        description = {SpringerLink - Zeitschriftenbeitrag},
	abstract = {This paper presents an approach to inductive synthesis of logic programs from examples using problem decomposition and problem reduction principles. This is in contrast to the prevailing logic program induction paradigm, which relies on generalization of programs from examples. The problem reduction is accomplished as a constrained top-down search process, which eventually is to reach trivial problems.},
	biburl = {http://www.bibsonomy.org/bibtex/251fe030fb520cc593f98a147c54e5af3/emanuel},
	keywords = {article ase combilog combinduce enumerative_ip ilp induction inductive_programming program_synthesis recursion_schemes}
}