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36
Learning Acyclic Firstorder Horn Sentences From Entailment
, 1997
"... This paper consider the problem of learning an unknown firstorder Horn sentence H 3 from examples of Horn clauses that H 3 implies and does not imply. Particularly, we deal with a subclass of firstorder Horn sentences ACH(k), called acyclic constrained Horn programs of constant arity k. ACH(k) al ..."
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Cited by 29 (4 self)
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This paper consider the problem of learning an unknown firstorder Horn sentence H 3 from examples of Horn clauses that H 3 implies and does not imply. Particularly, we deal with a subclass of firstorder Horn sentences ACH(k), called acyclic constrained Horn programs of constant arity k. ACH
Learning FirstOrder Acyclic Horn Programs from Entailment
 in Proceedings of the 15th International Conference on Machine Learning; (and Proceedings of the 8th International Conference on Inductive Logic Programming
, 1998
"... . In this paper, we consider learning firstorder Horn programs from entailment. In particular, we show that any subclass of firstorder acyclic Horn programs with constant arity is exactly learnable from equivalence and entailment membership queries provided it allows a polynomialtime subsumption ..."
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Cited by 14 (3 self)
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. In this paper, we consider learning firstorder Horn programs from entailment. In particular, we show that any subclass of firstorder acyclic Horn programs with constant arity is exactly learnable from equivalence and entailment membership queries provided it allows a polynomialtime subsumption
Learning Weakly Acyclic Horn Programs
, 1998
"... We consider a general class of "weakly acyclic Horn programs" where the literals implied by the examples and the target clauses form an acyclic dependency graph. A Horn clause is transparent if all the terms in all its derivations from the target program are contained in the clause itself. ..."
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. A Horn program is transparent if all its clauses are transparent. We show that any subclass of firstorder weakly acyclic, transparent, and unambiguous Horn programs with constant arity is exactly learnable from equivalence, membership, and derivation order queries, provided the class has a
Learning Horn Definitions: Theory and an Application to Planning
 NEW GENERATION COMPUTING
, 1998
"... A Horn definition is a set of Horn clauses with the same head literal. In this paper, we consider learning nonrecursive, firstorder Horn definitions from entailment. We show that this class is exactly learnable from equivalence and membership queries. It follows then that this class is PAC learnab ..."
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Cited by 18 (5 self)
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A Horn definition is a set of Horn clauses with the same head literal. In this paper, we consider learning nonrecursive, firstorder Horn definitions from entailment. We show that this class is exactly learnable from equivalence and membership queries. It follows then that this class is PAC
Learning FunctionFree Horn Expressions
, 1998
"... The problem of learning universally quantified function free first order Horn expressions is studied. Several models of learning from equivalence and membership queries are considered, including the model where interpretations are examples (Learning from Interpretations), the model where clauses are ..."
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Cited by 20 (1 self)
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The problem of learning universally quantified function free first order Horn expressions is studied. Several models of learning from equivalence and membership queries are considered, including the model where interpretations are examples (Learning from Interpretations), the model where clauses
Learning Range Restricted Horn Expressions
, 1999
"... . We study the learnability of first order Horn expressions from equivalence and membership queries. We show that the class of range restricted Horn expressions, where every term in the consequent of every clause appears also in the antecedent of the clause, is learnable. The result holds both for t ..."
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Cited by 5 (4 self)
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. We study the learnability of first order Horn expressions from equivalence and membership queries. We show that the class of range restricted Horn expressions, where every term in the consequent of every clause appears also in the antecedent of the clause, is learnable. The result holds both
Learning Inequated Range Restricted Horn Expressions
, 2000
"... . A learning algorithm for the class of inequated range restricted Horn expressions is presented and proved correct. The main property of this class is that all the terms in the conclusion of a clause appear in the antecedent of the clause, possibly as subterms of more complex terms. And every claus ..."
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Cited by 3 (3 self)
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Introduction This paper considers the problem of learning an unknown first order expression 1 T from examples of clauses that T entails or does not entail. This type of learning framework is known as learning from entailment. A great deal of work has been done in this learning setting. For example, [FP93
A Most General Refinement Operator for Reduced Sentences
, 1992
"... Many learning systems use the space of logic formulas as the search space of hypotheses. To build efficient systems, the set of first order logic formulas can be reduced in many ways. Most systems restrict themselves to (subsets of) Horn clauses. In this paper we investigate the space of reduced f ..."
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Cited by 1 (1 self)
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Many learning systems use the space of logic formulas as the search space of hypotheses. To build efficient systems, the set of first order logic formulas can be reduced in many ways. Most systems restrict themselves to (subsets of) Horn clauses. In this paper we investigate the space of reduced
Effective Rule Induction from Molecular Structures Represented by Labeled Graphs
, 2003
"... Acyclic conjunctive queries form a polynomially evaluable fragment of definite nonrecursive firstorder Horn clauses. Labeled graphs, a special class of relational structures, provide a natural way for representing chemical compounds. We propose an algorithm specific to learning acyclic conjunctive ..."
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Acyclic conjunctive queries form a polynomially evaluable fragment of definite nonrecursive firstorder Horn clauses. Labeled graphs, a special class of relational structures, provide a natural way for representing chemical compounds. We propose an algorithm specific to learning acyclic conjunctive
New revision algorithms
 In Algorithmic Learning Theory, 15th International Conference, ALT 2004
"... Abstract. A revision algorithm is a learning algorithm that identifies the target concept, starting from an initial concept. Such an algorithm is considered efficient if its complexity (in terms of the resource one is interested in) is polynomial in the syntactic distance between the initial and the ..."
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Cited by 2 (0 self)
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are allowed. In this model one of the main open problems is the efficient revision of Horn sentences. Two revision algorithms are presented for special cases of this problem: for depth1 acyclic Horn sentences, and for definite Horn sentences with unique heads. We also present an efficient revision algorithm
Results 1  10
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