In this paper we show that the critical part of a correctness proof for implementations of higher-order functional languages is amenable to machine-assisted proof. An extended version of the lambda-calculus is conside...
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(纸本)3540600175
In this paper we show that the critical part of a correctness proof for implementations of higher-order functional languages is amenable to machine-assisted proof. An extended version of the lambda-calculus is considered, and the congruence between its direct and continuation semantics is proved. The proof has been constructed with the help of a generic theorem prover - Isabelle. The major part of the problem lies in establishing the existence of predicates which describe the congruence. This has been solved using Milne's inclusive predicate strategy [5]. The most important intermediate results and the main theorem as derived by Isabelle are quoted in the paper.
The growing complexity of modern engineering tasks necessitates improved tool support for modelling, in particular tools allowing early detection of modelling errors. Broadly, there are two classes of modelling errors...
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We show how several novel tools in logic programming for AI (namely, continuation based linear and timeless assumptions, and datalog grammars) can assist us in producing terse treatments of difficult language processi...
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We show how several novel tools in logic programming for AI (namely, continuation based linear and timeless assumptions, and datalog grammars) can assist us in producing terse treatments of difficult language processing phenomena. As a proof of concept, we present a concise parser for datalog grammars (logic grammars where strings are represented with numbered word boundaries rather than as lists of words), that uses assumptions and a combination of left-corner parsing and charting. We then study two test cases of this parser's application: complete constituent coordination, and error diagnosis and correction.
We present a logic programming parsing methodology which we believe especially interesting for understanding implicit human-language structures. It records parsing state constituents through linear assumptions to be c...
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We show how two novel tools in logic programming for AI (namely, continuation-based linear and timeless assumptions, and Datalog grammars) can assist us in producing terse treatments of difficult language processing p...
详细信息
We show how two novel tools in logic programming for AI (namely, continuation-based linear and timeless assumptions, and Datalog grammars) can assist us in producing terse treatments of difficult language processing phenomena. As a proof of concept, we present a concise parser for Datalog grammars (logic grammars where strings are represented with numbered word boundaries rather than as lists of words) that uses assumptions and a combination of left-corner parsing and charting. We then study two test cases of this parser's application: complete constituent coordination, and error diagnosis and correction.
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