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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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Predicting weighted unobserved nodes in a regulatory network using answer set programming
BackgroundThe impact of a perturbation, over-expression, or repression of a key node on an organism, can be modelled based on a regulatory and/or...
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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... -
From Felicitous Models to Answer Set Programming
Felicitous models were defined by Kit Fine in 1987 for the purpose of describing the semantics of negation in the programming language Prolog. They... -
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... -
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... -
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... -
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,... -
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... -
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... -
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... -
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... -
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... -
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... -
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... -
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... -
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,... -
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...