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Learning From your mistakes, or By thinking

Learning From your mistakes, or By thinking

Learning From your mistakes, or By thinking. St Andrews. … for constraint satisfaction problems. Why learn?. Improve performance stop making the same mistakes! Provide explanations why is there no solution? why can’t I have a chocolate tea pot? React to change

By aric
(179 views)

Proofs from SAT Solvers

Proofs from SAT Solvers

Proofs from SAT Solvers. Yeting Ge ACSys NYU Nov 20 2007. SAT solvers and proofs. SAT problem and solvers Given a propositional logic formula, a SAT solver outputs sat or unsat Proofs from SAT solvers are needed A certificate to show the solver is correct

By brinda
(284 views)

GRASP: A Search Algorithm for Propositional Satisfiability

GRASP: A Search Algorithm for Propositional Satisfiability

GRASP: A Search Algorithm for Propositional Satisfiability. Sat in a Nutshell. Given a Boolean formula, find a variable assignment such that the formula evaluates to 1, or prove that no such assignment exists. For n variable,s there are 2 n possible truth assignments to be checked.

By sargent
(83 views)

Proof translation and SMT LIB certification

Proof translation and SMT LIB certification

Proof translation and SMT LIB certification. Yeting Ge Clark Barrett SMT 2008 July 7 Princeton. SMT solvers are more complicated. CVC3 contains over 100,000 lines of code Are SMT solvers correct?. Quest for correct SMT solvers?. To verify a SMT solver is correct?

By bjorn
(132 views)

SAT and CSP/CP Solvers [complete search]

SAT and CSP/CP Solvers [complete search]

A General Nogood -Learning Framework for Pseudo-Boolean Multi-Valued SAT* Siddhartha Jain Brown University Ashish Sabharwal IBM Watson Meinolf Sellmann IBM Watson * to appear at AAAI-2011. SAT and CSP/CP Solvers [complete search]. CP Solvers :

By portia
(95 views)

Interpolants from Z3 proofs

Interpolants from Z3 proofs

Interpolants from Z3 proofs. Ken McMillan Microsoft Research. TexPoint fonts used in EMF: A A A A A. Interpolating Z3. Deriving Craig interpolants from proofs (feasible interpolation) has a variety of applications in verification: Abstraction refinement

By clodia
(121 views)

Sat solving maximization and minimization problems

Sat solving maximization and minimization problems

Sat solving maximization and minimization problems. Student: Victoria Kravchenko Supervisors: Prof. Yoram Moses Liat Atsmon. The Project Goal Study and evaluate the approach of solving optimization problems through SAT reduction, and using SAT solvers to solve the reduced problems.

By lavonn
(99 views)

A signment

A signment

A signment. tack. SAT’10 Conference Edinburgh, Scotland, UK July 11, 2010 . hrinking. Alexander Nadel, Intel, Israel Vadim Ryvchin, Intel&Technion , Israel. Agenda. Introduction Algorithm Description Heuristics Experimental Results Conclusions. What is Shrinking?.

By istas
(81 views)

Boolean Satisfiability Present and Future

Boolean Satisfiability Present and Future

Boolean Satisfiability Present and Future. Lintao Zhang Microsoft Research SVC. SAT: Introduction . Deciding the satisfiability of Boolean formulas SAT: Propositional ( usually in Conjunctive Normal Form (CNF) ) QBF: With quantifiers Many driving forces Verification

By hali
(99 views)

Relaxed DPLL Search for MaxSAT (short paper)

Relaxed DPLL Search for MaxSAT (short paper)

Relaxed DPLL Search for MaxSAT (short paper). Lukas Kroc, Ashish Sabharwal , Bart Selman Cornell University SAT-09 Conference Swansea, U.K. July 3, 2009. SAT and MaxSAT. SAT : Given a Boolean formula such as find, if possible, a truth assignment such that F is satisfied.

By bluma
(127 views)

An Efficient SMT Solver

An Efficient SMT Solver

An Efficient SMT Solver . Lecturer: Qinsi Wang May 2, 2012. Z3. high-performance theorem prover being developed at Microsoft Research. mainly by Leonardo de Moura and Nikolaj Bjørner . Free (online interface, APIs, …)  but Not open source . Why Z3? .

By dextra
(216 views)

Presenter : Ching -Hua Huang

Presenter : Ching -Hua Huang

National Sun Yat-sen University Embedded System Laboratory. Finding Reset Nondeterminism in RTL Designs – Scalable X-Analysis Methodology and Case Study Cited count : 4 Hong- Zu Chou ; Haiqian Yu ; Kai- Hui Chang ;  Dobbyn Dobbyn and Sy -Yen Kuo

By keely
(104 views)

Proofs from SAT Solvers

Proofs from SAT Solvers

Proofs from SAT Solvers. Yeting Ge ACSys NYU Nov 20 2007. SAT solvers and proofs. SAT problem and solvers Given a propositional logic formula, a SAT solver outputs sat or unsat Proofs from SAT solvers are needed A certificate to show the solver is correct

By kieve
(232 views)

[published at AAAI-2013]

[published at AAAI-2013]

[published at AAAI-2013]. Resolution and Parallelizability: Barriers to the Efficient Parallelization of SAT Solvers George Katsirelos MIAT, INRA, Toulouse, France Ashish Sabharwal IBM Watson, USA Horst Samulowitz IBM Watson, USA Laurent Simon Univ. Paris- Sud , LRI/CNRS, Orsay , France.

By adler
(174 views)

Presented by Yunho Kim Provable Software Lab, KAIST

Presented by Yunho Kim Provable Software Lab, KAIST

Heavy-Tailed Phenomena in Satisfiability and Constraint Satisfaction Problems by Carla P. Gomes, Bart Selman, Nuno Crato and henry Kautz. Presented by Yunho Kim Provable Software Lab, KAIST. Introduction Search procedures and problem domains Cost distributions of backtrack search

By nan
(112 views)

Lecture 21 State-Space Search vs. Constraint-Based Planning

Lecture 21 State-Space Search vs. Constraint-Based Planning

Lecture 21 State-Space Search vs. Constraint-Based Planning. CSE 573 Artificial Intelligence I Henry Kautz Fall 2001. Road Map. Today Plan graphs Planning as state space search Comparison of the two approaches. Graphplan. Planning as graph search (Blum & Furst 1995)

By yakov
(133 views)

Workshop on the Verification Grand Challenge

Workshop on the Verification Grand Challenge

Workshop on the Verification Grand Challenge. Panagiotis (Pete) Manolios Georgia Institute of Technology. Challenge: A Verified Microprocessor. International Technology Roadmap for Semiconductors, 2003 Edition

By palti
(128 views)

Time-Space Tradeoffs in Resolution: Lower Bounds for Superlinear Space

Time-Space Tradeoffs in Resolution: Lower Bounds for Superlinear Space

Time-Space Tradeoffs in Resolution: Lower Bounds for Superlinear Space. Chris Beck Princeton University Joint work with Paul Beame & Russell Impagliazzo. Resolution. Lines are clauses, one simple proof step Three basic flavors: Tree-like , Regular , and General Resolution.

By carson
(152 views)

Knowledge Compilation

Knowledge Compilation

Knowledge Compilation. Jinbo Huang NICTA and ANU. S l ides made by Adnan Darwiche and Jinbo Huang. B. C. A. Y. X. A  okX   B  A  okX  B B  okY   C  B  okY  C. KB =. Propositional Logic. Is A,  C a normal device behavior?. A  okX   B

By dahlia
(145 views)

DLL Algorithm

DLL Algorithm

DLL Algorithm. How to realize search in hardware. We need to realize hardware stack Tree search is organized with hardware stack. The node of the tree is variable. The item on the stack is a sum of three literals (in 3SAT, can be more in general POS SAT).

By liesel
(73 views)

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