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226
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
An analysis of Bayesian classifiers
 IN PROCEEDINGS OF THE TENTH NATIONAL CONFERENCE ON ARTI CIAL INTELLIGENCE
, 1992
"... In this paper we present anaveragecase analysis of the Bayesian classifier, a simple induction algorithm that fares remarkably well on many learning tasks. Our analysis assumes a monotone conjunctive target concept, and independent, noisefree Boolean attributes. We calculate the probability that t ..."
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Cited by 440 (17 self)
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In this paper we present anaveragecase analysis of the Bayesian classifier, a simple induction algorithm that fares remarkably well on many learning tasks. Our analysis assumes a monotone conjunctive target concept, and independent, noisefree Boolean attributes. We calculate the probability
DomainIndependent Extensions to GSAT: Solving Large Structured Satisfiability Problems
 PROC. IJCAI93
, 1993
"... GSAT is a randomized local search procedure for solving propositional satisfiability problems (Selman et al. 1992). GSAT can solve hard, randomly generated problems that are an order of magnitude larger than those that can be handled by more traditional approaches such as the DavisPutnam proc ..."
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Cited by 227 (10 self)
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Putnam procedure. GSAT also efficiently solves encodings of graph coloring problems, Nqueens, and Boolean induction. However, GSAT does not perform as well on handcrafted encodings of blocksworld planning problems and formulas with a high degree of asymmetry. We present three strategies that dramatically
PolySet Theory
 http://www.rbjones.com/rbjpub/pp/doc/t020.pdf. p011.tex; 25/01/2010; 13:13; p.12 13
"... This document is concerned with the specification of an interpretation of the first order language of set theory. The purpose of this is to provide an ontological basis for foundation systems suitable for the formal derivation of mathematics. The ontology is to include the pure wellfounded sets of ..."
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Cited by 259 (2 self)
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of rank up to some arbitrary large cardinal together with the graphs of the polymorphic functions definable mathematical concepts. The interpretation is constructed by defining “names ” or “representatives ” for the sets in the domain of discourse by transfinite inductive definition in the context of a
Decision Procedures For Inductive Boolean . . .
 THEORETICAL COMPUTER SCIENCE
, 2003
"... We show how alternating automata provide decision procedures for theequalVofinductivel de#nedBoolfW functions and presentappltfqqVqW to reasoning about parameterizedfamilet of circuits. We use alWzzzEf7q word automata toformalEf familE oflf##WJq structured circuits and alfzzzIPf7 tree automata tofo ..."
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We show how alternating automata provide decision procedures for theequalVofinductivel de#nedBoolfW functions and presentappltfqqVqW to reasoning about parameterizedfamilet of circuits. We use alWzzzEf7q word automata toformalEf familE oflf##WJq structured circuits and alfzzzIPf7 tree automata toformal#f famil# of tree structured circuits. We providecomplfzJW bounds for deciding theequalE of function (or circuit) familt) and show how our decision procedures can beimpl##f7qI using BDDs. In comparison to previous work, our approach issimpl#f has bettercomplWJ bounds, and, in the case of treestructured families is more general.
Learning Boolean Concepts in the Presence of Many Irrelevant Features
 Artificial Intelligence
, 1994
"... In many domains, an appropriate inductive bias is the MINFEATURES bias, which prefers consistent hypotheses definable over as few features as possible. This paper defines and studies this bias in Boolean domains. First, it is shown that any learning algorithm implementing the MINFEATURES bias requ ..."
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Cited by 127 (0 self)
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In many domains, an appropriate inductive bias is the MINFEATURES bias, which prefers consistent hypotheses definable over as few features as possible. This paper defines and studies this bias in Boolean domains. First, it is shown that any learning algorithm implementing the MINFEATURES bias
Boolean complexes and boolean numbers
"... Abstract. The Bruhat order gives a poset structure to any Coxeter group. The ideal of elements in this poset having boolean principal order ideals forms a simplicial poset. This simplicial poset defines the boolean complex for the group. In a Coxeter system of rank n, we show that the boolean comple ..."
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complex is homotopy equivalent to a wedge of (n−1)dimensional spheres. The number of these spheres is the boolean number, which can be computed inductively from the unlabeled Coxeter system, thus defining a graph invariant. For certain families of graphs, the boolean numbers have intriguing combinatorial
Computational Limitations on Learning from Examples
 Journal of the ACM
, 1988
"... Abstract. The computational complexity of learning Boolean concepts from examples is investigated. It is shown for various classes of concept representations that these cannot be learned feasibly in a distributionfree sense unless R = NP. These classes include (a) disjunctions of two monomials, (b) ..."
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Cited by 214 (10 self)
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Abstract. The computational complexity of learning Boolean concepts from examples is investigated. It is shown for various classes of concept representations that these cannot be learned feasibly in a distributionfree sense unless R = NP. These classes include (a) disjunctions of two monomials, (b
Boolean Regression
 Annals of Operations Research
, 1994
"... We take a regressionbased approach to the problem of induction, which is the problem of inferring general rules from specific instances. Whereas traditional regression analysis fits a numerical formula to data, we fit a logical formula to boolean data. We can, for instance, construct an expert ..."
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Cited by 6 (1 self)
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We take a regressionbased approach to the problem of induction, which is the problem of inferring general rules from specific instances. Whereas traditional regression analysis fits a numerical formula to data, we fit a logical formula to boolean data. We can, for instance, construct an expert
Parametric Circuit Representation Using Inductive Boolean Functions
 In Computer Aided Verification, CAV '93, LNCS 697
, 1993
"... . We have developed a methodology based on symbolic manipulation of inductive Boolean functions (IBFs) for formal verification of inductivelydefined hardware. This methodology combines the techniques of reasoning by induction and symbolic tautologychecking in an automated and potentially efficient ..."
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Cited by 17 (2 self)
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. We have developed a methodology based on symbolic manipulation of inductive Boolean functions (IBFs) for formal verification of inductivelydefined hardware. This methodology combines the techniques of reasoning by induction and symbolic tautologychecking in an automated and potentially efficient
Results 1  10
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226