30,550 research outputs found

    Strict General Setting for Building Decision Procedures into Theorem Provers

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    The efficient and flexible incorporating of decision procedures into theorem provers is very important for their successful use. There are several approaches for combining and augmenting of decision procedures; some of them support handling uninterpreted functions, congruence closure, lemma invoking etc. In this paper we present a variant of one general setting for building decision procedures into theorem provers (gs framework [18]). That setting is based on macro inference rules motivated by techniques used in different approaches. The general setting enables a simple describing of different combination/augmentation schemes. In this paper, we further develop and extend this setting by an imposed ordering on the macro inference rules. That ordering leads to a ”strict setting”. It makes implementing and using variants of well-known or new schemes within this framework a very easy task even for a non-expert user. Also, this setting enables easy comparison of different combination/augmentation schemes and combination of their ideas

    A General Setting for Flexibly Combining and Augmenting Decision Procedures

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    Galois stratification and ACFA

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    Cirencester College (FEFC inspection report; 04/94 and 68/98)

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    Comprises two Further Education Funding Council (FEFC) inspection reports for the periods 1993-94 (04/94), and 1997-98 (68/98). The FEFC has a legal duty to make sure further education in England is properly assessed. Inspections and reports on each college of further education are conducted according to a four-year cycle. Cirencester College in Gloucestershire is a major provider of further education courses for the south-east region of the Cotswolds with a rapidly developing portfolio of courses for school leavers and adults

    Rule-based Machine Learning Methods for Functional Prediction

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    We describe a machine learning method for predicting the value of a real-valued function, given the values of multiple input variables. The method induces solutions from samples in the form of ordered disjunctive normal form (DNF) decision rules. A central objective of the method and representation is the induction of compact, easily interpretable solutions. This rule-based decision model can be extended to search efficiently for similar cases prior to approximating function values. Experimental results on real-world data demonstrate that the new techniques are competitive with existing machine learning and statistical methods and can sometimes yield superior regression performance.Comment: See http://www.jair.org/ for any accompanying file

    Institutional audit : University of Ulster

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    Daventry Tertiary College (FEFC Inspection Report; 37/94 and 69/98)

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    Comprises two Further Education Funding Council (FEFC) inspection reports for the periods 1994 and 1997-9

    Kendal College: report from the Inspectorate (FEFC inspection report; 12/96 and 05/00)

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    Comprises two Further Education Funding Council (FEFC) inspection reports for the periods 1995-96 and 1999-2000

    Equality and diversity: an aspect report on provision in Scotland's colleges

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