Abstract: Spiking neural networks (SNNs) offer an effective approach to solving constraint satisfaction problems (CSPs) by leveraging their temporal, event-driven dynamics. Moreover, neuromorphic ...
We study some nonlinear optimal control problems under state constraint. We construct extremal flows by differential-algebraic equations to solve an optimal control problem subject to mixed ...
We present ‘NeuralConstraints,’ a suite of computer-assisted composition tools that integrates a feedforward neural network as a rule within a constraint-based composition framework.
OKLAHOMA CITY & DALLAS--(BUSINESS WIRE)--From slow-moving inventory to capacity constraints, the furniture industry faces unique supply chain challenges that are difficult to manage at scale via ...
Logical reasoning remains a crucial area where AI systems struggle despite advances in processing language and knowledge. Understanding logical reasoning in AI is essential for improving automated ...
Integer Linear Programming (ILP) is the foundation of combinatorial optimization, which is extensively applied across numerous industries to resolve challenging decision-making issues. Under a set of ...
Abstract: Multi-Niche Constraint Satisfaction problems are common in applications of evolutionary computation for industrial design, where the optimization algorithm is used as a preparatory step for ...
Since the open-pit precedence-constrained production scheduling problem is an NP-hard problem, solving it is always a challenging task, especially from a long-term perspective because a mineral ...
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