English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We propose Differentiable Satisfiability and Differentiable Answer SetProgramming (Differentiable SAT/ASP) for multi-model optimization. Models(answer sets or satisfying truth assignments) are sampled using a novel SAT/ASPsolving approach which uses a gradient descent-based branching mechanism.Sampling proceeds until the value of a user-defined multi-model cost functionreaches a given threshold. As major use cases for our approach we proposedistribution-aware model sampling and expressive yet scalable probabilisticlogic programming. As our main algorithmic approach to Differentiable SAT/ASP,we introduce an enhancement of the state-of-the-art CDNL/CDCL algorithm forSAT/ASP solving. Additionally, we present alternative algorithms which use anunmodified ASP solver (Clingo/clasp) and map the optimization task toconventional answer set optimization or use so-called propagators. We alsoreport on the open source software DelSAT, a recent prototype implementation ofour main algorithm, and on initial experimental results which indicate thatDelSATs performance is, when applied to the use case of probabilistic logicinference, on par with Markov Logic Network (MLN) inference performance,despite having advantageous properties compared to MLNs, such as the ability toexpress inductive definitions and to work with probabilities as weightsdirectly in all cases. Our experiments also indicate that our main algorithm isstrongly superior in terms of performance compared to the presented alternativeapproaches which reduce a common instance of the general problem to regularSAT/ASP.

Differentiable Satisfiability and Differentiable Answer Set Programming for Sampling-Based Multi-Model Optimization