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A New Algorithm to Automate Inductive Learning of Default Theories [article]

Farhad Shakerin, Elmer Salazar, Gopal Gupta
2017 arXiv   pre-print
Therefore, learned default theories are better understood by humans. In this paper, we present new algorithms to learn default theories in the form of non-monotonic logic programs.  ...  In inductive learning of a broad concept, an algorithm should be able to distinguish concept examples from exceptions and noisy data.  ...  FOLD Algorithm The idea of our FOLD algorithm is to learn a concept as a default theory and possibly multiple exceptions.  ... 
arXiv:1707.02693v1 fatcat:hnc7rjafdzei3jzdknqfdofxg4

The 2005 AAAI Classic Paper Awards

Tom M. Mitchell, Hector J. Levesque
2005 The AI Magazine  
During the mid-1980s the new theory of PAC learning was being developed, which allowed deriving quantitative bounds on the probability of successful learning as a function of the number of training examples  ...  measure of the complexity of the learner's hypothesis space, characterizing this key aspect of the learner's inductive bias.Haussler's paper was therefore important in linking the new PAC learning theory  ... 
doi:10.1609/aimag.v26i4.1853 dblp:journals/aim/MitchellL05 fatcat:vpxbjogznzghxnf7lbsuptl6ry

FOLD-R++: A Scalable Toolset for Automated Inductive Learning of Default Theories from Mixed Data [article]

Huaduo Wang, Gopal Gupta
2022 arXiv   pre-print
FOLD-R is an automated inductive learning algorithm for learning default rules for mixed (numerical and categorical) data.  ...  We also create a powerful tool-set by combining FOLD-R++ with s(CASP)-a goal-directed ASP execution engine-to make predictions on new data samples using the answer set program generated by FOLD-R++.  ...  We are grateful to Joaquin Arias and the s(CASP) team for their work on providing facilities for generating the justification tree and English encoding of rules in s(CASP).  ... 
arXiv:2110.07843v3 fatcat:7euthhtkmrgzvbx6nyez3647a4

Induction of Non-monotonic Logic Programs To Explain Statistical Learning Models

Farhad Shakerin
2019 Electronic Proceedings in Theoretical Computer Science  
We present a fast and scalable algorithm to induce non-monotonic logic programs from statistical learning models.  ...  Our experiments with UCI standard benchmarks suggest a significant improvement in terms of classification evaluation metrics and running time of the training algorithm compared to ALEPH, a state-of-the-art  ...  Other reasons that underscore the importance of inductive learning of default theories can be found in Sakama [15] who also surveys other attempts in this direction.  ... 
doi:10.4204/eptcs.306.51 fatcat:4xypxl3di5cfvgxpa3fnnvliou

Saturation-based theorem proving: Past successes and future potential [chapter]

Harald Ganzinger
1996 Lecture Notes in Computer Science  
Proof-search in intuitionistic logic with equality, or back to simultaneous rigid E-Unification p. 32 Extensions to a generalization critic for inductive proof p. 47 Learning domain knowledge to improve  ...  semantics for production rule systems: theory and applications p. 508 Experiments in the heuristic use of past proof experience p. 523 Lemma discovery in automating induction p. 538 Advanced indexing  ... 
doi:10.1007/3-540-61511-3_64 fatcat:iizfu32trnfadmj4z7xcvwds5y

Cumulative Scoring-Based Induction of Default Theories

Farhad Shakerin, Gopal Gupta, Michael Wagner
2018 International Conference on Logic Programming  
Our original FOLD algorithm automates the inductive learning of default theories. The enhancements presented here preserve the greedy nature of hypothesis search during clause specialization.  ...  Significant research has been conducted in recent years to extend Inductive Logic Programming (ILP) methods to induce a more expressive class of logic programs such as answer set programs.  ...  F I C L P 2 0 1 8 2:4 Cumulative Scoring-Based Induction of Default Theories Algorithm 1 Overview of the FOIL algorithm.  ... 
doi:10.4230/oasics.iclp.2018.2 dblp:conf/iclp/ShakerinG18 fatcat:cfxjfyr4andldc5tjbsatlknra

A Survey of Automated Deduction [chapter]

Alan Bundy
1999 Lecture Notes in Computer Science  
Abstract We survey research in the automation of deductive inference, from its beginnings in the early history of computing to the present d a y.  ...  These four were: a description of a theorem prover for propositional logic, called the Logic Theory Machine, written by Newell, Shaw and Simon, Newell et al., 1957 ; two papers on Gelernter's Geometry  ...  For instance, in Inductive Logic Programming, Muggleton, 1991 , it is used to learn a general logic program from instances of it. 6 Higher-Order Logic and Type Theory Many problems in both mathematics  ... 
doi:10.1007/3-540-48317-9_6 fatcat:r7kti6fn2bcjbodyatqdq64wu4

Forthcoming papers

1998 Artificial Intelligence  
A belief network is constructed to work parallel to a deterministic model, and it is used to update conditional probabilities associated with different components of that model.  ...  Varis, A belief network approach to optimization and parameter estimation: application to resource and environmental management An approach to use Bayesian belief networks in optimization is presented,  ...  An inductive approach does not require a complete and tractable domain theory and has the potential to create more effective rules by learning from more than one example at a time.  ... 
doi:10.1016/s0004-3702(98)00038-1 fatcat:x4ydqgpzrfdxnepjsvgfi5mv4a

Page 7911 of Mathematical Reviews Vol. , Issue 99k [page]

1999 Mathematical Reviews  
a useful way to integrate learning with reasoning.  ...  This view yields efficient algorithms for default reasoning.  ... 

Page 1258 of Mathematical Reviews Vol. , Issue 2001B [page]

2001 Mathematical Reviews  
Didier Dubois, Michel Grabisch and Henri Prade, Assessing the value of a candidate: a qualitative possibilistic approach (137- 147); Béatrice Duval and Pascal Nicolas, Learning default theories (148-159  ...  causal explanations (55-67); Christian Borgelt and Rudolf Kruse, A critique of inductive causa- tion (68-79); Rachel A.  ... 

Learning to Reason with HOL4 tactics [article]

Thibault Gauthier, Cezary Kaliszyk, Josef Urban
2018 arXiv   pre-print
It implements a modified A*-algorithm directly in HOL4 that explores different tactic-level proof paths, guiding their selection by learning from a large number of previous tactic-level proofs.  ...  Techniques combining machine learning with translation to automated reasoning have recently become an important component of formal proof assistants.  ...  Conclusion We proposed a new proof assistant automation technique which combines tactic-based proof search, with machine learning tactic prediction and a "small hammer" approach.  ... 
arXiv:1804.00595v1 fatcat:p6oehxklwfdalokzeez3v6prga

Page 4913 of Mathematical Reviews Vol. , Issue 95h [page]

1995 Mathematical Reviews  
(English summary) Algorithmic learning theory (Tokyo, 1993), 59-72, Lecture Notes in Comput. Sci., 744, Springer, Berlin, 1993.  ...  Summary: “Our goal through several years has been the devel- opment of an efficient search algorithm for inductive inference of expressions using only input/output examples.  ... 

Heuristic Based Induction of Answer Set Programs: From Default theories to combinatorial problems [article]

Farhad Shakerin, Gopal Gupta
2018 arXiv   pre-print
Our extended algorithm is a greedy FOIL-like algorithm, capable of inducing non-monotonic logic programs, examples of which includes programs for combinatorial problems such as graph-coloring and N-queens  ...  In this paper we extend our previous work on learning stratified answer set programs that have a single stable model to learning arbitrary (i.e., non-stratified) ones with multiple stable models.  ...  Recently we developed an algorithm called FOLD [18] to automate inductive learning of default theories represented as stratified answer set programs.  ... 
arXiv:1802.06462v1 fatcat:iw6bn3i2zrgkzp5aniiwr53csq

Page 2597 of Mathematical Reviews Vol. , Issue 97D [page]

1997 Mathematical Reviews  
a tractable tableau representation of extensions to the idea of compiling default theories.”  ...  “Our learning method has been applied to reconstruct a non- singly connected network of 22 nodes and 22 arcs without the need of any a priori supplied node ordering.”  ... 

Priorities on defaults with prerequisites, and their application in treating specificity in terminological default logic

Franz Baader, Bernhard Hollunder
1995 Journal of automated reasoning  
We shall exhibit some interesting properties of the new formalism, compare it with existing approaches, and describe an algorithm for computing extensions.  ...  In a recent paper we have proposed terminological default logic as a formalism that combines means both for structured representation of classes and objects and for default inheritance of properties.  ...  Acknowledgements We thank Peter Patel-Schneider for interesting discussions on specificity of defaults and Bernhard Nebel and the referees for helpful comments on previous versions of this paper.  ... 
doi:10.1007/bf00881830 fatcat:psdiqq62qjarfjbzgwvezzctye
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