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Advantages of decision lists and implicit negatives in Inductive Logic Programming

Mary Elaine Califf, Raymond J. Mooney
1998 New generation computing  
doi:10.1007/bf03037482 fatcat:ajl4kolpljao5fwohhzynxridm

Learning the past tense of English verbs using inductive logic programming [chapter]

Raymond J. Mooney, Mary Elaine Califf
1996 Lecture Notes in Computer Science  
We h a ve developed a technique for learning a special type of Prolog program called a rst-order decision list, de ned as an ordered list of clauses each ending in a cut.  ...  This paper presents results on using a new inductive logic programming method called Foidl to learn the past tense of English verbs.  ...  A fuller discussion of this research appears in 13 .  ... 
doi:10.1007/3-540-60925-3_60 fatcat:otoxrhk5hbggzntxeqhtm5ebpe

Induction of First-Order Decision Lists: Results on Learning the Past Tense of English Verbs [article]

R. J. Mooney, M. E. Califf
1995 arXiv   pre-print
This paper presents a method for inducing logic programs from examples that learns a new class of concepts called first-order decision lists, defined as ordered lists of clauses each ending in a cut.  ...  The method, called FOIDL, is based on FOIL (Quinlan, 1990) but employs intensional background knowledge and avoids the need for explicit negative examples.  ...  Thanks also to Ross for aiding us in running the Foil experiments. Discussions with John Zelle and Cindi Thompson at the University of Texas also in uenced this work.  ... 
arXiv:cs/9506102v1 fatcat:3cx5x4tasjhhxfeuy4ogo7kqhi

Induction of First-Order Decision Lists: Results on Learning the Past Tense of English Verbs

R. J. Mooney, M. E. Califf
1995 The Journal of Artificial Intelligence Research  
This paper presents a method for inducing logic programs from examples that learns a new class of concepts called first-order decision lists, defined as ordered lists of clauses each ending in a cut.  ...  The method, called FOIDL, is based on FOIL (Quinlan, 1990) but employs intensional background knowledge and avoids the need for explicit negative examples.  ...  Thanks also to Ross for aiding us in running the Foil experiments. Discussions with John Zelle and Cindi Thompson at the University o f T exas also in uenced this work.  ... 
doi:10.1613/jair.148 fatcat:l36ggei7jnbd3g6fb7b4l5g5zy

Comparative results on using inductive logic programming for corpus-based parser construction [chapter]

John M. Zelle, Raymond J. Mooney
1996 Lecture Notes in Computer Science  
Chill treats grammar acquisition as the learning of search-control rules within a logic program.  ...  Unlike many current corpus-based approaches that use propositional or probabilistic learning algorithms, Chill uses techniques from inductive logic programming ILP to learn relational representations.  ...  Here Chill has an overwhelming advantage, achieving 84 accuracy compared to the 20 accuracy of induction with implicit negatives.  ... 
doi:10.1007/3-540-60925-3_59 fatcat:vbulx6gnpvenjia2ksh34hlqr4

Top-down induction of first-order logical decision trees

Hendrik Blockeel, Luc De Raedt
1998 Artificial Intelligence  
The expressivity of these trees is shown to be larger than that of the flat logic programs which are typically induced by classical ILP systems, and equal to that of first-order decision lists.  ...  A first-order framework for top-down induction of logical decision trees is introduced.  ...  This work is also part of the European Community Esprit project no. 20237, Inductive Logic Programming 2.  ... 
doi:10.1016/s0004-3702(98)00034-4 fatcat:mkt3eava3fhyndnrbkwaqycxxi

Logic Programming for Describing and Solving Planning Problems [article]

Maurice Bruynooghe
2000 arXiv   pre-print
In this sense abductive logic programming is a smooth extension of the standard paradigm of logic programming, not a radical departure.  ...  In this paradigm, all program rules are considered as constraints and solutions are stable models of the rule set. This is a rather radical departure from the standard paradigm of logic programming.  ...  Acknowledgements I am grateful to the reviewers for their useful comments on the draft; also to Marc Denecker, Nikolay Pelov and Bob Kowalski for various discussions and useful comments.  ... 
arXiv:cs/0003025v1 fatcat:7ssfzs6udffsji6iukrrgc35me

A Brief Overview of Rule Learning [chapter]

Johannes Fürnkranz, Tomáš Kliegr
2015 Lecture Notes in Computer Science  
In this paper, we provide a brief summary of elementary research in rule learning.  ...  The two main research directions are descriptive rule learning, with the goal of discovering regularities that hold in parts of the given dataset, and predictive rule learning, which aims at generalizing  ...  Acknowledgment Tomáš Kliegr was partly supported by the Faculty of Informatics and Statistics, University of Economics, Prague within "long term institutional support for research activities" scheme and  ... 
doi:10.1007/978-3-319-21542-6_4 fatcat:eiyersvgjjakpjpany7db7bcxi

Bayesian Inductive Logic Programming [chapter]

Stephen Muggleton
1994 Machine Learning Proceedings 1994  
Inductive Logic Programming (ILP) involves the construction of first-order definite clause theories from examples and background knowledge.  ...  The strong applications in ILP can be contrasted with relatively weak PAC-learning results (even highlyrestricted forms of logic programs are known to be prediction-hard).  ...  Acknowledgements The author would like to thank David Page and Ashwin Srinivasan of Oxford University Computing Laboratory and Michael Sternberg and Ross King of Imperial Cancer Research Fund for discussions  ... 
doi:10.1016/b978-1-55860-335-6.50052-0 dblp:conf/icml/Muggleton94 fatcat:55iza3henjff7bdeqvprl7txue

A Survey of Automated Deduction [chapter]

Alan Bundy
1999 Lecture Notes in Computer Science  
A Potted History of Early Automated Deduction Deduction is the branch of reasoning formalised by and studied in mathematical logic.  ...  For instance, four of the 19 papers in one of the earliest and seminal AI texts, Feigenbaum and Feldman's Computers and Thought", described implementations of deductive reasoning.  ...  Although explicit and implicit induction sound very di erent, close analysis of the rewriting process in implicit induction reveals proof steps which are very similar to the base and step cases of explicit  ... 
doi:10.1007/3-540-48317-9_6 fatcat:r7kti6fn2bcjbodyatqdq64wu4

Inductive logic programming at 30 [article]

Andrew Cropper, Sebastijan Dumančić, Richard Evans, Stephen H. Muggleton
2021 arXiv   pre-print
Inductive logic programming (ILP) is a form of logic-based machine learning. The goal is to induce a hypothesis (a logic program) that generalises given training examples.  ...  We focus on (i) new meta-level search methods, (ii) techniques for learning recursive programs, (iii) new approaches for predicate invention, and (iv) the use of different technologies.  ...  Blockeel H, De Raedt L ( ) Top-down induction of first-order logical decision trees. Artif Intell ( -): -.  ... 
arXiv:2102.10556v2 fatcat:kv7ktjbajng6jjfae3sq3ubbmu

Efficient and Effective Induction of First Order Decision Lists [chapter]

Mary Elaine Califf
2003 Lecture Notes in Computer Science  
We present BUFOIDL, a new bottom-up algorithm for learning first order decision lists.  ...  to overcome in BUFOIDL.  ...  FOIDL takes this approach, and we follow it as well. The FOIDL Algorithm FOIDL is a top-down inductive logic programming algorithm based largely on FOIL [11] .  ... 
doi:10.1007/3-540-36468-4_2 fatcat:6p64puklqndwxg2kqtop4ki57q

A First-Order Logic with Frames [chapter]

Adithya Murali, Lucas Peña, Christof Löding, P. Madhusudan
2020 Lecture Notes in Computer Science  
This program logic consists of both localized proof rules as well as rules that derive the weakest tightest preconditions in FL.  ...  Finally, we design a program logic based on frame logic for reasoning with programs that dynamically update heaps that facilitates local specifications and frame reasoning.  ...  As usual, it would be sufficient to forbid negative occurrences of inductive predicates in mutual recursion.  ... 
doi:10.1007/978-3-030-44914-8_19 fatcat:xmdzlbfxzrb3bprfxmnvw3o54m

CoALP-Ty'16 [article]

Ekaterina Komendantskaya, František Farka
2016 arXiv   pre-print
This volume consists of extended abstracts describing current research in the following areas: Semantics: Lawvere theories and Coalgebra in Logic and Functional Programming Programming languages: Horn  ...  This volume constitutes the pre-proceedings of the Workshop on Coalgebra, Horn Clause Logic Programming and Types (CoALP-Ty'16), held on 28--29 November 2016 in Edinburgh as a mark of the end of the EPSRC  ...  This in turn gives us a universe of discourse for exploring other semantics and proof systems for mixed inductive-coinductive logic programs.  ... 
arXiv:1612.03032v1 fatcat:d6gug5imufgwbcslntnyts4nim

Bayesian inductive logic programming

Stephen Muggleton
1994 Proceedings of the seventh annual conference on Computational learning theory - COLT '94  
Acknowledgements The author would like to thank David Page and Ashwin Srinivasan of Oxford University Computing Laboratory and Michael Sternberg and Ross King of Imperial Cancer Research Fund for discussions  ...  and input to this paper.  ...  This apparently flies in the face of negative PAClearnability results for logic programs.  ... 
doi:10.1145/180139.178095 dblp:conf/colt/Muggleton94 fatcat:6667u33l7fazfkdl3w76v3ot2i
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