Polynomial Time Learning of Some Multiple Context-Free Languages with a Minimally Adequate Teacher

Yoshinaka, Ryo; Clark, Alexander

doi:10.1007/978-3-642-32024-8_13

Cited by 8 publications

(6 citation statements)

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“…A final development coming out of the MCS convergence is new attention to learning methods for substantial subsets of these languages (Yoshinaka and Clark, 2010;Yoshinaka, 2010). This preliminary and more recent ongoing work is very promising.…”

Section: Syntactic Structure: Revealing the Hidden Consensusmentioning

confidence: 99%

The epicenter of linguistic behavior

Stabler¹

2013

Language Down the Garden Path

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Speaking, listening, reading, and reasoning all depend on common, fundamental aspects of linguistic structure. This common structure is defined by the grammar, and so Bever (1970) calls grammar 'the epicenter of all language behavior' in his pioneering study of how grammar and the structures it defines may relate to other cognitive abilities. Computational models with this perspective have become more sophisticated since 1970. While many fundamental questions remain open, a substantial, non-trivial consensus about the nature of linguistic structure has emerged, and a wide range of methods for computing that structure are well understood. Much less is established about how the structure or content of grammar may be grounded in other cognitive abilities, but here again there is a much better understanding of how these could be related, and some proposals are getting sophisticated enough for preliminary empirical assessments.

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Section: Syntactic Structure: Revealing the Hidden Consensusmentioning

confidence: 99%

The epicenter of linguistic behavior

Stabler¹

2013

Language Down the Garden Path

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“…Yoshinaka and Clark [28] present a polynomial-time algorithm for learning congruential mcfls, which properly include multidimensionally substitutable mcfls, with a minimally adequate teacher. Yoshinaka [27] also presents an algorithm that efficiently learns an even richer class of mcfls from positive data and membership queries.…”

Section: Discussionmentioning

confidence: 99%

Efficient learning of multiple context-free languages with multidimensional substitutability from positive data

Yoshinaka

2011

Theoretical Computer Science

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“…The task of SynSkeleton is more difficult than the regular language learning task of L * because the unknown language is a set of graphs, not strings. However, there is work towards L * -like algorithms for richer classes of languages such as learners of context-free grammars [3,13,54] and multiple context-free (MCF) grammars [55]. A single non-terminal symbol in an MCF grammar is not a hole in a single string, but a vector of r holes, where the rank r is bounded.…”

Section: Discussionmentioning

confidence: 99%

Proofs as Relational Invariants of Synthesized Execution Grammars

Voss,

Heath,

Harris

2017

Preprint

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The automatic verification of programs that maintain unbounded low-level data structures is a critical and open problem. Analyzers and verifiers developed in previous work can synthesize invariants that only describe data structures of heavily restricted forms, or require an analyst to provide predicates over program data and structure that are used in a synthesized proof of correctness.In this work, we introduce a novel automatic safety verifier of programs that maintain low-level data structures, named Lttp. Lttp synthesizes proofs of program safety represented as a grammar of a given program's control paths, annotated with invariants that relate program state at distinct points within its path of execution. Lttp synthesizes such proofs completely automatically, using a novel inductive-synthesis algorithm.We have implemented Lttp as a verifier for JVM bytecode and applied it to verify the safety of a collection of verification benchmarks. Our results demonstrate that Lttp can be applied to automatically verify the safety of programs that are beyond the scope of previously-developed verifiers. INTRODUCTIONAutomatically verifying that a given program satisfies a desired safety property is a fundamental problems of program verification. Recent work has seen the development of powerful program verifiers that operate automatically [9,[22][23][24][25]38]. Such verifiers can often determine if practical programs satisfy properties concerning their control flow and facts over a bounded collection of data values [5].However, verifying the safety of programs that maintain unbounded low-level data structures remains an open problem. A significant body of previous work has developed shape analyzers [17,26] that, given a program P, synthesize invariants of the reachable heaps of P represented in a particular shape domain, such as three-valued logical structures [35,41,45] or separation-logic formulas [12,15,42,53]; the invariants synthesized by such analyzers can potentially imply facts about program states that establish that P is safe. Another body of work has developed automatic program

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Polynomial Time Learning of Some Multiple Context-Free Languages with a Minimally Adequate Teacher

Cited by 8 publications

References 20 publications

The epicenter of linguistic behavior

The epicenter of linguistic behavior

Efficient learning of multiple context-free languages with multidimensional substitutability from positive data

Proofs as Relational Invariants of Synthesized Execution Grammars

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