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845
A New Method for Solving Hard Satisfiability Problems
 AAAI
, 1992
"... We introduce a greedy local search procedure called GSAT for solving propositional satisfiability problems. Our experiments show that this procedure can be used to solve hard, randomly generated problems that are an order of magnitude larger than those that can be handled by more traditional approac ..."
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Cited by 730 (21 self)
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approaches such as the DavisPutnam procedure or resolution. We also show that GSAT can solve structured satisfiability problems quickly. In particular, we solve encodings of graph coloring problems, Nqueens, and Boolean induction. General application strategies and limitations of the approach are also
The Use of Explicit Plans to Guide Inductive Proofs
 9TH CONFERENCE ON AUTOMATED DEDUCTION
, 1988
"... We propose the use of explicit proof plans to guide the search for a proof in automatic theorem proving. By representing proof plans as the specifications of LCFlike tactics, [Gordon et al 79], and by recording these specifications in a sorted metalogic, we are able to reason about the conjectures ..."
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Cited by 295 (40 self)
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We propose the use of explicit proof plans to guide the search for a proof in automatic theorem proving. By representing proof plans as the specifications of LCFlike tactics, [Gordon et al 79], and by recording these specifications in a sorted metalogic, we are able to reason about
A Model of Inductive Bias Learning
 Journal of Artificial Intelligence Research
, 2000
"... A major problem in machine learning is that of inductive bias: how to choose a learner's hypothesis space so that it is large enough to contain a solution to the problem being learnt, yet small enough to ensure reliable generalization from reasonablysized training sets. Typically such bias is ..."
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Cited by 193 (0 self)
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A major problem in machine learning is that of inductive bias: how to choose a learner's hypothesis space so that it is large enough to contain a solution to the problem being learnt, yet small enough to ensure reliable generalization from reasonablysized training sets. Typically such bias
Inductive Definitions in the System Coq Rules and Properties
, 1992
"... In the pure Calculus of Constructions, it is possible to represent data structures and predicates using higherorder quantification. However, this representation is not satisfactory, from the point of view of both the efficiency of the underlying programs and the power of the logical system. For ..."
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Cited by 191 (2 self)
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. For these reasons, the calculus was extended with a primitive notion of inductive definitions [8]. This paper describes the rules for inductive definitions in the system Coq. They are general enough to be seen as one formulation of adding inductive definitions to a typed lambdacalculus. We prove strong
Supervised Machine Learning: A Review of Classification Techniques. Informatica 31:249–268
, 2007
"... Supervised machine learning is the search for algorithms that reason from externally supplied instances to produce general hypotheses, which then make predictions about future instances. In other words, the goal of supervised learning is to build a concise model of the distribution of class labels i ..."
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Cited by 188 (0 self)
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Supervised machine learning is the search for algorithms that reason from externally supplied instances to produce general hypotheses, which then make predictions about future instances. In other words, the goal of supervised learning is to build a concise model of the distribution of class labels
Inductive Reasoning
 LANGUAGE COMPUTATIONS. AMERICAN MATHEMATICAL SOCIETY
, 1994
"... Our aim is to explain a general theory of inductive reasoning which is close enough to the concerns of language studies. In this setup, the optimal prediction rate is assigned to the hypothesis considered most likely by a priorfree form of Bayesian inference. In terms of practical applications a ..."
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Cited by 1 (0 self)
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Our aim is to explain a general theory of inductive reasoning which is close enough to the concerns of language studies. In this setup, the optimal prediction rate is assigned to the hypothesis considered most likely by a priorfree form of Bayesian inference. In terms of practical applications
Coil sensitivity encoding for fast MRI. In:
 Proceedings of the ISMRM 6th Annual Meeting,
, 1998
"... New theoretical and practical concepts are presented for considerably enhancing the performance of magnetic resonance imaging (MRI) by means of arrays of multiple receiver coils. Sensitivity encoding (SENSE) is based on the fact that receiver sensitivity generally has an encoding effect complementa ..."
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Cited by 193 (3 self)
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New theoretical and practical concepts are presented for considerably enhancing the performance of magnetic resonance imaging (MRI) by means of arrays of multiple receiver coils. Sensitivity encoding (SENSE) is based on the fact that receiver sensitivity generally has an encoding effect
Superposition as a logical glue
"... The typical mathematical language systematically exploits notational and logical abuses whose resolution requires not just the knowledge of domain specific notation and conventions, but not trivial skills in the given mathematical discipline. A large part of this background knowledge is expressed in ..."
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Cited by 3 (3 self)
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to convince the system that his reasoning step is indeed correct. We also show how this kind of automation, named small scale, can serve as the building block for the more general, large scale, case, allowing a smooth integration of equational reasoning with backwardbased proof searching procedures. 1
Reasoning with inductively defined relations in the HOL theorem prover
, 1992
"... Abstract: Inductively defined relations are among the basic mathematical tools of computer science. Examples include evaluation and computation relations in structural operational semantics, labelled transition relations in process algebra semantics, inductivelydefined typing judgements, and proof ..."
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Cited by 49 (0 self)
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systems in general. This paper describes a set of HOL theoremproving tools for reasoning about such inductively defined relations. We also describe a suite of worked examples using these tools. First printed: August 1992
On handling distinct objects in the superposition calculus
 In Notes 5th IWIL Workshop
, 2005
"... Abstract. Many domains of reasoning include a set of distinct objects. For generalpurpose automated theorem provers, this property has to be specified explicitly, by including distinctness axioms. Since their number grows quadratically with the number of distinct objects, this results in large and ..."
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Cited by 4 (3 self)
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Abstract. Many domains of reasoning include a set of distinct objects. For generalpurpose automated theorem provers, this property has to be specified explicitly, by including distinctness axioms. Since their number grows quadratically with the number of distinct objects, this results in large
Results 1  10
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