
Train Scheduling with Hybrid Answer Set Programming
We present a solution to realworld train scheduling problems, involving...
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Grounding Bound Founded Answer Set Programs
To appear in Theory and Practice of Logic Programming (TPLP) Bound Fou...
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The Power of NonGround Rules in Answer Set Programming
Answer set programming (ASP) is a wellestablished logic programming lan...
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ASP(AC): Answer Set Programming with Algebraic Constraints
Weighted Logic is a powerful tool for the specification of calculations ...
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Efficiency Analysis of ASP Encodings for Sequential Pattern Mining Tasks
This article presents the use of Answer Set Programming (ASP) to mine se...
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Logic Programming approaches for routing faultfree and maximallyparallel Wavelength Routed Optical Networks on Chip (Application paper)
One promising trend in digital system integration consists of boosting o...
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The DLVHEX System for Knowledge Representation: Recent Advances (System Description)
The DLVHEX system implements the HEXsemantics, which integrates answer ...
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Boosting Answer Set Optimization with Weighted Comparator Networks
Answer set programming (ASP) is a paradigm for modeling knowledge intensive domains and solving challenging reasoning problems. In ASP solving, a typical strategy is to preprocess problem instances by rewriting complex rules into simpler ones. Normalization is a rewriting process that removes extended rule types altogether in favor of normal rules. Recently, such techniques led to optimization rewriting in ASP, where the goal is to boost answer set optimization by refactoring the optimization criteria of interest. In this paper, we present a novel, general, and effective technique for optimization rewriting based on comparator networks, which are specific kinds of circuits for reordering the elements of vectors. The idea is to connect an ASP encoding of a comparator network to the literals being optimized and to redistribute the weights of these literals over the structure of the network. The encoding captures information about the weight of an answer set in auxiliary atoms in a structured way that is proven to yield exponential improvements during branchandbound optimization on an infinite family of example programs. The used comparator network can be tuned freely, e.g., to find the best size for a given benchmark class. Experiments show accelerated optimization performance on several benchmark problems. Under consideration in Theory and Practice of Logic Programming (TPLP).
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