The Return of xorro

Everardo, Flavio; Janhunen, Tomi; Kaminski, Roland; Schaub, Torsten

doi:10.1007/978-3-030-20528-7_21

Cited by 3 publications

(5 citation statements)

References 21 publications

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“…We selected four programs that allow for counting answer sets, namely, DynASP v2.0 (Fichte et al 2017), clingo v5.4.0 (Gebser et al 2007), Ganak (Sharma et al 2019), ApproxMC4 (Soos, Gocht, and Meel 2020). We compared the ASP+XOR solver of ApproxASP with xorro (Everardo et al 2019). clingo can count answer sets by enumeration.…”

Section: Methodsmentioning

confidence: 99%

“…Unfortunately, such a construction is quite impractical for two reasons. First, it would require us to add far too many auxiliary variables for each new parity constraint, and second, we would suffer from the known inadequacy to scale due to grounding (Everardo et al 2019). Hence, we aim for an incremental "propagator"-based implementation when solving parity constraints (Gebser et al 2016).…”

Section: Partitioning and Sampling Countsmentioning

confidence: 99%

“…First, we formally define parity constraints and show basic properties needed for partitioning the search space. The meaning of parity constraints follows previous works on the topic (Everardo et al 2019). For atoms a 1 and a 2 , we denote the exclusive-or between these two atoms by a 1 ⊕ a 2 .…”

Section: Partitioning and Sampling Countsmentioning

confidence: 99%

“…The ASP solver addresses the answer set computation while invoking the XOR solving subroutine. Everardo et al (2019) discussed the impact of eagerly or lazily translating XOR constraints into ASP encodings. An eager translation is practically infeasible due to an exponential blowup in the number of constraints.…”

Section: Implementation Detailsmentioning

confidence: 99%

“…Nonetheless, the compilation can be used to transform an input instance and use the existing model counter, e.g., (Chakraborty, Meel, and Vardi 2013). A recent extension to the ASP solver clingo (Everardo et al 2019), called xorro, introduces a variety of parity constraints into ASP solving, relying on ASP encodings of parity constraints or theory propagators (Gebser et al 2016). In contrast, our implementation relies on a dedicated high performant implementation of Gauss-Jordan elimination (GJE).…”

Section: Introductionmentioning

confidence: 99%

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ApproxASP – a Scalable Approximate Answer Set Counter

Kabir

Everardo

Shukla³

et al. 2022

AAAI

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Answer Set Programming (ASP) is a framework in artificial intelligence and knowledge representation for declarative modeling and problem solving. Modern ASP solvers focus on the computation or enumeration of answer sets. However, a variety of probabilistic applications in reasoning or logic programming require counting answer sets. While counting can be done by enumeration, simple enumeration becomes immediately infeasible if the number of solutions is high. On the other hand, approaches to exact counting are of high worst-case complexity. In fact, in propositional model counting, exact counting becomes impractical. In this work, we present a scalable approach to approximate counting for answer set programming. Our approach is based on systematically adding XOR constraints to ASP programs, which divide the search space. We prove that adding random XOR constraints partitions the answer sets of an ASP program. In practice, we use a Gaussian elimination-based approach by lifting ideas from SAT to ASP and integrating it into a state of the art ASP solver, which we call ApproxASP. Finally, our experimental evaluation shows the scalability of our approach over the existing ASP systems.

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Section: Methodsmentioning

confidence: 99%

Section: Partitioning and Sampling Countsmentioning

confidence: 99%

Section: Partitioning and Sampling Countsmentioning

confidence: 99%

Section: Implementation Detailsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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ApproxASP – a Scalable Approximate Answer Set Counter

Kabir

Everardo

Shukla³

et al. 2022

AAAI

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Fast Error Propagation Probability Estimates by Answer Set Programming and Approximate Model Counting

et al. 2022

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A Scalable Reasoning and Learning Approach for Neural-Symbolic Stream Fusion

Phuoc

Eiter

Le-Tuan

2021

AAAI

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Driven by deep neural networks (DNN), the recent development of computer vision makes vision sensors such as stereo cameras and Lidars ubiquitous in autonomous cars, robotics and traffic monitoring. However, a traditional DNN-based data fusion pipeline like object tracking has to hard-wire an engineered set of DNN models to a fixed processing logic, which makes it difficult to infuse new models to that pipeline. To overcome this, we propose a novel neural-symbolic stream reasoning approach realised by semantic stream reasoning programs which specify DNN-based data fusion pipelines via logic rules with learnable probabilistic degrees as weights. The reasoning task over this program is governed by a novel incremental reasoning algorithm, which lends itself also as a core building block for a scalable and parallel algorithm to learn the weights for such program. Extensive experiments with our first prototype on multi-object tracking benchmarks for autonomous driving and traffic monitoring show that our flexible approach can considerably improve both accuracy and processing throughput compared to the DNN-based counterparts.

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The Return of xorro

Cited by 3 publications

References 21 publications

ApproxASP – a Scalable Approximate Answer Set Counter

ApproxASP – a Scalable Approximate Answer Set Counter

Fast Error Propagation Probability Estimates by Answer Set Programming and Approximate Model Counting

A Scalable Reasoning and Learning Approach for Neural-Symbolic Stream Fusion

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