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Model-based reasoning using answer set programming
Diagnosis, i.e., the detection and identification of faults, provides the basis for bringing systems back to normal operation in case of a fault....
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Answer Set Programming Made Easy
We take up an idea from the folklore of Answer Set Programming (ASP), namely that choices, integrity constraints along with a restricted rule format...
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Rethinking Answer Set Programming Templates
In imperative programming, the Domain-Driven Design methodology helps in co** with the complexity of software development by materializing in code...
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Marketplace Logistics via Answer Set Programming
Marketplaces aggregate products from several providers and handle orders involving several suppliers automatically. We report on an application of...
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Using answer set programming to deal with boolean networks and attractor computation: application to gene regulatory networks of cells
Deciphering gene regulatory networks’ functioning is an essential step for better understanding of life, as these networks play a fundamental role in...
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Statistical Relational Extension of Answer Set Programming
This tutorial presents a statistical relational extension of the answer set programming language called...
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SPRAG: building and benchmarking a Short Programming-Related Answer Grading dataset
Automated Short Answer Grading (ASAG) is a widely explored application of NLP in the domain of education. While much research focuses on natural...
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Hamiltonian Cycle Reconfiguration with Answer Set Programming
The Hamiltonian cycle reconfiguration problem is defined as determining, for a given Hamiltonian cycle problem and two among its feasible solutions,...
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Recongo: Bounded Combinatorial Reconfiguration with Answer Set Programming
We develop an approach called bounded combinatorial reconfiguration for solving combinatorial reconfiguration problems based on Answer Set...
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Combinatorial Reconfiguration with Answer Set Programming: Algorithms, Encodings, and Empirical Analysis
We propose an approach called bounded combinatorial reconfiguration for solving combinatorial reconfiguration problems based on Answer Set...
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Solving Vehicle Equipment Specification Problems with Answer Set Programming
We develop an approach to solving mono- and multi-objective vehicle equipment specification problems considering the corporate average fuel economy...
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Generative Datalog and Answer Set Programming – Extended Abstract
Generative Datalog is an extension of Datalog that incorporates constructs for referencing parameterized probability distributions. This augmentation...
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Learning Automata-Based Complex Event Patterns in Answer Set Programming
Complex Event Recognition and Forecasting (CER/F) techniques attempt to detect, or even forecast ahead of time, event occurrences in streaming input...
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Comparing Planning Domain Models Using Answer Set Programming
Automated planning is a prominent area of Artificial Intelligence, and an important component for intelligent autonomous agents. A critical aspect of...
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Inference in Probabilistic Answer Set Programming Under the Credal Semantics
Probabilistic Answer Set Programming under the credal semantics (PASP) describes an uncertain domain through an answer set program extended with...
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Approximate Inference in Probabilistic Answer Set Programming for Statistical Probabilities
“Type 1” statements were introduced by Halpern in 1990 with the goal to represent statistical information about a domain of interest. These are of...
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Using Answer Set Programming to Improve Sensor Network Lifetime
Sensor network lifetime maximization can be solved using heuristic methods, but they produce only suboptimal sensor activity schedules. However,...
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Learning the Parameters of Probabilistic Answer Set Programs
Probabilistic Answer Set Programming (PASP) is a powerful formalism that allows to model uncertain scenarios with answer set programs. One of the...
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Supplier Optimization at Bosch with Knowledge Graphs and Answer Set Programming
The automotive industry is constantly facing the challenge of optimizing their suppliers to meet customer demands while kee** costs low. Knowledge...
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Heuristics, Answer Set Programming and Markov Decision Process for Solving a Set of Spatial Puzzles*
Spatial puzzles composed of rigid objects, flexible strings and holes offer interesting challenges for reasoning about spatial entities that are...
