Results 1  10
of
13
Wellnested context unification
 In CADE 2005, LNCS 3632
"... Abstract. Context unification (CU) is the open problem of solving context equations for trees. We distinguish a new decidable variant of CU– wellnested CU – and present a new unification algorithm that solves wellnested context equations in nondeterministic polynomial time. We show that minimal w ..."
Abstract

Cited by 15 (9 self)
 Add to MetaCart
Abstract. Context unification (CU) is the open problem of solving context equations for trees. We distinguish a new decidable variant of CU– wellnested CU – and present a new unification algorithm that solves wellnested context equations in nondeterministic polynomial time. We show that minimal wellnested solutions of context equations can be composed from the material present in the equation (see Theorem 1). This property is wishful when modeling natural language ellipsis in CU. 1
Context Sequence Matching for XML
, 2005
"... Context and sequence variables allow matching to explore termtrees both in depth and in breadth. It makes context sequence matching a suitable computational mechanism for a rulebased language to query and transform XML, or to specify and verify web sites. Such a language would have advantages of b ..."
Abstract

Cited by 10 (5 self)
 Add to MetaCart
Context and sequence variables allow matching to explore termtrees both in depth and in breadth. It makes context sequence matching a suitable computational mechanism for a rulebased language to query and transform XML, or to specify and verify web sites. Such a language would have advantages of both pathbased and patternbased languages. We develop a context sequence matching algorithm and its extension for regular expression matching, and prove their soundness, termination and completeness properties.
Matching with Regular Constraints
 SUTCLIFFE G., VORONKOV A., Eds., Proceedings of LPAR’05
, 2005
"... We describe a sound, terminating, and complete matching algorithm for terms built over flexible arity function symbols and context, function, sequence, and individual variables. Context and sequence variables allow matching to move in term trees to arbitrary depth and breadth, respectively. The ..."
Abstract

Cited by 9 (8 self)
 Add to MetaCart
We describe a sound, terminating, and complete matching algorithm for terms built over flexible arity function symbols and context, function, sequence, and individual variables. Context and sequence variables allow matching to move in term trees to arbitrary depth and breadth, respectively. The values of variables can be constrained by regular expressions which are not necessarily linear. We describe heuristics for optimization, and discuss applications.
Context unification and traversal equations
 In: Proc. of the 12th International Conference on Rewriting Techniques and Applications (RTA’01
, 2001
"... Abstract. Context unification was originally defined by H. Comon in ICALP’92, as the problem of finding a unifier for a set of equations containing firstorder variables and context variables. These context variables have arguments, and can be instantiated by contexts. In other words, they are secon ..."
Abstract

Cited by 8 (7 self)
 Add to MetaCart
Abstract. Context unification was originally defined by H. Comon in ICALP’92, as the problem of finding a unifier for a set of equations containing firstorder variables and context variables. These context variables have arguments, and can be instantiated by contexts. In other words, they are secondorder variables that are restricted to be instantiated by linear terms (a linear term is a λexpression λx1 ···λxn.t where every xi occurs exactly once in t). In this paper, we prove that, if the so called rankbound conjecture is true, then the context unification problem is decidable. This is done reducing context unification to solvability of traversal equations (a kind of word unification modulo certain permutations) and then, reducing traversal equations to word equations with regular constraints. 1
On the Limits of SecondOrder Unification
"... SecondOrder Unification is a problem that naturally arises when applying automated deduction techniques with variables denoting predicates. The problem is undecidable, but a considerable effort has been made in order to find decidable fragments, and understand the deep reasons of its complexity. Tw ..."
Abstract
 Add to MetaCart
SecondOrder Unification is a problem that naturally arises when applying automated deduction techniques with variables denoting predicates. The problem is undecidable, but a considerable effort has been made in order to find decidable fragments, and understand the deep reasons of its complexity. Two variants of the problem, Bounded SecondOrder Unification and Linear SecondOrder Unification –where the use of bound variables in the instantiations is restricted–, have been extensively studied in the last two decades. In this paper we summarize some decidability/undecidability/complexity results, trying to focus on those that could be more interesting for a wider audience, and involving less technical details. 1
Context Sequence Matching for XML 1,2
"... Context and sequence variables allow matching to explore termtrees both in depth and in breadth. It makes context sequence matching a suitable computational mechanism for a rulebased language to query and transform XML, or to specify and verify web sites. Such a language would have advantages of b ..."
Abstract
 Add to MetaCart
(Show Context)
Context and sequence variables allow matching to explore termtrees both in depth and in breadth. It makes context sequence matching a suitable computational mechanism for a rulebased language to query and transform XML, or to specify and verify web sites. Such a language would have advantages of both pathbased and patternbased languages. We develop a context sequence matching algorithm and its extension for regular expression matching, and prove their soundness, termination and completeness properties.
WWV’05 Preliminary Version Context Sequence Matching for XML
"... Context and sequence variables allow matching to explore termtrees both in depth and in breadth. It makes context sequence matching a suitable computational mechanism for a rulebased language to query and transform XML, or to specify and verify web sites. Such a language would have advantages of b ..."
Abstract
 Add to MetaCart
(Show Context)
Context and sequence variables allow matching to explore termtrees both in depth and in breadth. It makes context sequence matching a suitable computational mechanism for a rulebased language to query and transform XML, or to specify and verify web sites. Such a language would have advantages of both pathbased and patternbased languages. We develop a context sequence matching algorithm and its extension for regular expression matching, and prove their soundness, termination and completeness properties.
WellNested Context Unification. Proc. of the 20th Int. Conf. on Automated Deduction (CADE20). WellNested Context Unification ⋆
"... Abstract. Context unification (CU) is the open problem of solving context equations for trees. We distinguish a new decidable variant of CU – wellnested CU – and present a new unification algorithm that solves wellnested context equations in nondeterministic polynomial time. We show that minimal ..."
Abstract
 Add to MetaCart
Abstract. Context unification (CU) is the open problem of solving context equations for trees. We distinguish a new decidable variant of CU – wellnested CU – and present a new unification algorithm that solves wellnested context equations in nondeterministic polynomial time. We show that minimal wellnested solutions of context equations can be composed from the material present in the equation (see Theorem 1). This property is wishful when modeling natural language ellipsis in CU. 1
Contributions To Artificial Intelligence: The IIIA Perspective
, 2002
"... Artificial intelligence is a relatively new scientific and technological field which studies the nature of intelligence by using computers to produce intelligent behaviour. Initially, the main goal was a purely scientific one, understanding human intelligence, and this remains the aim of cognitive s ..."
Abstract
 Add to MetaCart
(Show Context)
Artificial intelligence is a relatively new scientific and technological field which studies the nature of intelligence by using computers to produce intelligent behaviour. Initially, the main goal was a purely scientific one, understanding human intelligence, and this remains the aim of cognitive scientists. Unfortunately, such an ambitious and fascinating goal is not only far from being achieved but has yet to be satisfactorily approached. Fortunately, however, artificial intelligence also has an engineering goal: building systems that are useful to people even if the intelligence of such systems has no relation whatsoever with human intelligence, and therefore being able to build them does not necessarily provide any insight into the nature of human intelligence. This engineering goal has become the predominant one among artificial intelligence researchers and has produced impressive results, ranging from knowledgebased systems to autonomous robots, that have been applied to many different domains. Furthermore, artificial intelligence products and services today represent an annual market of tens of billions of dollars worldwide.