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Constrained Sampling and Counting: Universal Hashing Meets SAT Solving
[article]
2015
arXiv
pre-print
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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]
2018
arXiv
pre-print
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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]
2018
Lecture Notes in Computer Science
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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]
2021
arXiv
pre-print
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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]
2017
arXiv
pre-print
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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]
2012
arXiv
pre-print
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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
2022
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
unpublished
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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
2013
Neural Information Processing Systems
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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]
2015
Lecture Notes in Computer Science
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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]
2020
arXiv
pre-print
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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]
2020
arXiv
pre-print
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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
2019
Autonomous Robots
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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]
2020
arXiv
pre-print
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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
2020
Proceedings of the Seventeenth International Conference on Principles of Knowledge Representation and Reasoning
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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
2022
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
unpublished
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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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