Results 51 to 60 of about 6,708,930 (288)
On constraint satisfaction problems below P [PDF]
Journal of Logic and Computation, 2014 Symmetric Datalog, a fragment of the logic programming language Datalog, is conjectured to capture all constraint satisfaction problems (CSP) in L. Therefore developing tools that help us understand whether or not a CSP can be defined in symmetric Datalog is an important task.
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Advanced Engineering Materials, EarlyView.A unified research data management framework for heterogeneous materials data is presented. The system integrates multimodal datasets using ontologies and knowledge graphs, enabling interoperability and FAIR (findable, accessible, interoperable, reusable) data principles. By linking data across scales and workflows, it supports reproducible, Artifitial
Doaa Mohamed +6 more
wiley +1 more source
Improved ant colony optimization algorithm for solving constraint satisfaction problem
Tongxin xuebao, 2015 The traditional backtracking algorithm was less efficient on solving large-scale constraint satisfaction problem,and more difficult to be solved within a reasonable time.In order to overcome this problem,many incompleteness algo-rithms based on heuristic
HANGYong-gang Z +2 more
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Modeling and Simulation of Mission Planning Problem for Remote Sensing Satellite Imaging
MATEC Web of Conferences, 2018 Mission planning problem for remote sensing satellite imaging is studied. Firstly, the time constraint satisfaction problem model is presented after analyzing the characteristic of time constraint.
Pan Yao +4 more
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A Hybrid Autonomic Computing-Based Approach to Distributed Constraint Satisfaction Problems
Computers, 2015 Distributed constraint satisfaction problems (DisCSPs) are among the widely endeavored problems using agent-based simulation. Fernandez et al. formulated sensor and mobile tracking problem as a DisCSP, known as SensorDCSP In this paper, we adopt a ...
Abhishek Bhatia +2 more
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Robust satisfiability of constraint satisfaction problems [PDF]
Proceedings of the forty-fourth annual ACM symposium on Theory of computing, 2012 An algorithm for a constraint satisfaction problem is called robust if it outputs an assignment satisfying at least (1-g(e))-fraction of the constraints given a (1-e)-satisfiable instance, where g(e) -> 0 as e -> 0, $g(0)=0. Guruswami and Zhou conjectured a characterization of constraint languages for which the corresponding constraint satisfaction ...
Libor Barto, Marcin Kozik
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Advanced Engineering Materials, EarlyView.We develop a data‐driven method to derive the mathematical expressions of the Flory–Huggins interaction parameter χ for the swelling behavior of temperature–responsive hydrogels. Starting from initial assumptions of χ, our workflow combines Bayesian optimization, Flory–Rehner theory, and symbolic regression to generate candidate χ expressions.
Yawen Wang +2 more
wiley +1 more source
Promise constraint satisfaction problems
, 2022 The promise constraint satisfaction problem (PCSP) is a recently introduced vast generalisation of the constraint satisfaction problem (CSP) that captures approximability of satisfiable instances. A PCSP instance comes with two forms of each constraint: a strict one and a weak one. Given the promise that a solution exists under the strict constraints,
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A Lightweight Procedural Layer for Hybrid Experimental–Computational Workflows in Materials Science
Advanced Engineering Materials, EarlyView.We unveil a prototype hybrid‐workflow framework that fuses automatedcomputation with hands‐on experiments. Built atop pyiron, a lightweight, parameterized layer translates procedure descriptions into executable manual steps, syncing instrument settings, human interventions, and data capture in real‐time today.
Steffen Brinckmann +8 more
wiley +1 more source
An Extensive Evaluation of Portfolio Approaches for Constraint Satisfaction Problems
International Journal of Interactive Multimedia and Artificial Intelligence, 2016 In the context of Constraint Programming, a portfolio approach exploits the complementary strengths of a portfolio of different constraint solvers. The goal is to predict and run the best solver(s) of the portfolio for solving a new, unseen problem.
Roberto Amadini +2 more
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