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Constrained Sampling and Counting: Universal Hashing Meets SAT Solving [article]

Kuldeep S. Meel, Moshe Vardi, Supratik Chakraborty, Daniel J. Fremont, Sanjit A. Seshia, Dror Fried, Alexander Ivrii, Sharad Malik
2015 arXiv   pre-print
Recently, we proposed a novel approach that combines universal hashing and SAT solving and scales to formulas with hundreds of thousands of variables without giving up correctness guarantees.  ...  Constrained sampling and counting are two fundamental problems in artificial intelligence with a diverse range of applications, spanning probabilistic reasoning and planning to constrained-random verification  ...  two more recent: universal hashing, satisfiability (SAT) solving, and satisfiability modulo theories (SMT) solving.  ... 
arXiv:1512.06633v1 fatcat:fhs6yqqsavcedhmw3frs6mi6pi

Constrained Counting and Sampling: Bridging the Gap between Theory and Practice [article]

Kuldeep S. Meel
2018 arXiv   pre-print
In this thesis, we introduce a novel hashing-based algorithmic framework for constrained sampling and counting that combines the classical algorithmic technique of universal hashing with the dramatic progress  ...  Consequently, constrained counting and sampling have been subject to intense theoretical and empirical investigations over the years.  ...  Motivated by "SAT revolution", this thesis seeks to develop algorithmic foundations for two widely useful extensions of SAT: constrained counting and sampling.  ... 
arXiv:1806.02239v1 fatcat:3tgo2z4tkjbxjfwql6v4725d3i

A New Probabilistic Algorithm for Approximate Model Counting [chapter]

Cunjing Ge, Feifei Ma, Tian Liu, Jian Zhang, Xutong Ma
2018 Lecture Notes in Computer Science  
Constrained counting is important in domains ranging from artificial intelligence to software analysis. There are already a few approaches for counting models over various types of constraints.  ...  Recently, hashing-based approaches achieve success but still rely on solution enumeration.  ...  This improvement of hash functions is also orthogonal to our approach as we use hash functions and SAT solving as black boxes.  ... 
doi:10.1007/978-3-319-94205-6_21 fatcat:7idpv5ufd5dqlitkgzd7id6vhi

Model Counting meets F0 Estimation [article]

A. Pavan and N.V. Vinodchandran and Arnab Bhattacharyya and Kuldeep S. Meel
2021 arXiv   pre-print
We next turn our attention to viewing streaming from the lens of counting and show that framing F_0 estimation as a special case of #DNF counting allows us to obtain a general recipe for a rich class of  ...  To this end, we focus on two foundational problems: model counting for CSP's and computation of zeroth frequency moments (F_0) for data streams.  ...  progress in the SAT solving wherein calls to NP oracles are replaced by invocations of SAT solver in practice.  ... 
arXiv:2105.00639v1 fatcat:4exegyfj35c5fnem7sewzwzime

A New Probabilistic Algorithm for Approximate Model Counting [article]

Cunjing Ge, Feifei Ma, Tian Liu, Jian Zhang
2017 arXiv   pre-print
Constrained counting is important in domains ranging from artificial intelligence to software analysis. There are already a few approaches for counting models over various types of constraints.  ...  Recently, hashing-based approaches achieve both theoretical guarantees and scalability, but still rely on solution enumeration.  ...  The use of universal hash functions in counting problems began in [28, 30] , but the resulting algorithm scaled poorly in practice.  ... 
arXiv:1706.03906v1 fatcat:g6v5qfffefb4lbazjbxfsq3cgm

Solving weighted and counting variants of connectivity problems parameterized by treewidth deterministically in single exponential time [article]

Hans L. Bodlaender, Marek Cygan, Stefan Kratsch, Jesper Nederlof
2012 arXiv   pre-print
Naturally, this raises the question whether randomization is necessary to achieve this runtime; furthermore, it is desirable to also solve counting and weighted versions (the latter without incurring a  ...  For example, in this time we can solve the traveling salesman problem or count the number of Hamiltonian cycles.  ...  Moreover the second author thanks Marcin Pilipczuk and Lukasz Kowalik for helpful discussions at the early stage of the paper.  ... 
arXiv:1211.1505v1 fatcat:zidqnmlmcrcxrcsdk262kqktiq

Counting, Sampling, and Synthesis: The Quest for Scalability

Kuldeep S. Meel
2022 Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence   unpublished
Our work seeks to enable a Beyond SAT revolution via design of scalable techniques for three fundamental problems that lie beyond SAT: constrained counting, constrained sampling, and automated synthesis  ...  From a core technical perspective, our work builds on the SAT revolution, which refers to algorithmic advances in combinatorial solving techniques for the fundamental problem of satisfiability (SAT), i.e  ...  The author would like to give a special shout out to his friend and long-term collaborator, Dr. Mate Soos.  ... 
doi:10.24963/ijcai.2022/813 fatcat:rfun5tt6ijhb5jfpawmdq2abiy

Embed and Project: Discrete Sampling with Universal Hashing

Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman
2013 Neural Information Processing Systems  
We propose a sampling algorithm, called PAWS, based on embedding the set into a higher-dimensional space which is then randomly projected using universal hash functions to a lower-dimensional subspace  ...  We demonstrate that by using state-of-the-art combinatorial search tools, PAWS can efficiently sample from Ising grids with strong interactions and from software verification instances, while MCMC and  ...  The idea is to project by randomly constraining the configuration space using a family of universal hash functions, search for up to P "surviving" configurations, and then, if fewer than P survive, perform  ... 
dblp:conf/nips/ErmonGSS13 fatcat:3sfucsgamzfrjick63y4cyeemy

On Parallel Scalable Uniform SAT Witness Generation [chapter]

Supratik Chakraborty, Daniel J. Fremont, Kuldeep S. Meel, Sanjit A. Seshia, Moshe Y. Vardi
2015 Lecture Notes in Computer Science  
We present a random hashing-based, easily parallelizable algorithm, UniGen2, for sampling solutions of propositional constraints.  ...  Constrained-random verification (CRV) is widely used in industry for validating hardware designs.  ...  Most of the sampling algorithms used for uniform witness generation fail to meet this criterion, and are hence not easily parallelizable.  ... 
doi:10.1007/978-3-662-46681-0_25 fatcat:nrbbch5ok5d4hdlbp74gf5ds4y

Manthan: A Data Driven Approach for Boolean Function Synthesis [article]

Priyanka Golia, Subhajit Roy, Kuldeep S. Meel
2020 arXiv   pre-print
Manthan views functional synthesis as a classification problem, relying on advances in constrained sampling for data generation, and advances in automated reasoning for a novel proof-guided refinement  ...  The significant performance improvements, along with our detailed analysis, highlights several interesting avenues of future work at the intersection of machine learning, constrained sampling, and automated  ...  This work was supported in part by National Research Foundation Singapore under its NRF Fellowship Programme[NRF-NRFFAI1-2019-0004 ] and AI Singapore Programme [AISG-RP-2018-005], and NUS ODPRT Grant [  ... 
arXiv:2005.06922v1 fatcat:mcocntrshzbq3fhys2vfuj2hdu

Building Very Small Test Suites (with Snap) [article]

Jianfeng Chen, Xipeng Shen, Tim Menzies
2020 arXiv   pre-print
Also, SNAP ran orders of magnitude faster and (unlike prior work) generated 100% valid tests.  ...  Software is now so large and complex that additional architecture is needed to guide theorem provers as they try to generate test suites.  ...  [14] list key ingredients of integration of universal hashing and SAT solvers; e.g. guarantee uniform solutions to a constraint model.  ... 
arXiv:1905.05358v3 fatcat:rudyv4276bb27ig2zkcfc2tsde

Gaussian process decentralized data fusion meets transfer learning in large-scale distributed cooperative perception

Ruofei Ouyang, Bryan Kian Hsiang Low
2019 Autonomous Robots  
Formula Forgetting â€" Application to Belief Update and Conservative Extension Liangda Fang*, Hai Wan, Xianqiao Liu, Biqing Fang, Zhaorong Lai DepthLGP: Learning Embeddings of Out-of-Sample Nodes in Dynamic  ...  for Scene Recognition Yang Liu*, Qingchao Chen, Wei Chen, Ian Wassell DID: Distributed Incremental Block Coordinate Descent for Nonnegative Matrix Factorization Tianxiang Gao*, Chris Chu, Iowa State University  ... 
doi:10.1007/s10514-018-09826-z fatcat:67yqhwmgozccxni56rxmuapjgm

On the Approximability of Weighted Model Integration on DNF Structures [article]

Ralph Abboud, İsmail İlkan Ceylan, Radoslav Dimitrov
2020 arXiv   pre-print
In this work, we study weighted model integration, a generalization of weighted model counting which involves real variables in addition to propositional variables, and pose the following question: Does  ...  Building on classical results from approximate volume computation and approximate weighted model counting, we show that weighted model integration on DNF structures can indeed be approximated for a class  ...  Experiments for this work were conducted on servers provided by the Advanced Research Computing (ARC) cluster administered by the University of Oxford.  ... 
arXiv:2002.06726v3 fatcat:xswhmqm5jnarjhisjglljn5zhi

On the Approximability of Weighted Model Integration on DNF Structures

Ralph Abboud, İsmail İlkan Ceylan, Radoslav Dimitrov
2020 Proceedings of the Seventeenth International Conference on Principles of Knowledge Representation and Reasoning  
In this work, we study weighted model integration, a generalization of weighted model counting which involves real variables in addition to propositional variables, and pose the following question: Does  ...  Building on classical results from approximate volume computation and approximate weighted model counting, we show that weighted model integration on DNF structures can indeed be approximated for a class  ...  Looking forward, we aim to investigate alternative approaches for approximate WMI with guarantees, e.g., hashing-based approaches, to minimize the impact of sampling.  ... 
doi:10.24963/kr.2020/85 dblp:conf/kr/AbboudCD20 fatcat:5g52z3cppvaxveehswaa4nukdu

Towards New Optimized Artificial Immune Recognition Systems under the Belief Function Theory

Rihab Abdelkhalek
2022 Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence   unpublished
Artificial Immune Recognition Systems (AIRS) are powerful machine learning techniques, which aim to solve real world problems. A number of AIRS versions have produced successful prediction results.  ...  This issue is considered as a huge obstacle for having accurate and effective classification outputs. Therefore, our main objective is to handle this uncertainty using the belief function theory.  ...  The author would like to give a special shout out to his friend and long-term collaborator, Dr. Mate Soos.  ... 
doi:10.24963/ijcai.2022/817 fatcat:exzsbodemvhjbjekfei2vik4lq
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