Many real-world applications for complex industrial engineering or design problems can be modelled as optimisation problems. These problems often have features such as multi-modality (multiple optimal ...

In reinforcement learning (RL), an agent learns to achieve its goal by interacting with its environment and learning from feedback about its successes and failures. This feedback is typically encoded ...

Abstract: This work investigates the space-limited aircraft assembly scheduling problem (SAASP) based on real-world cases. A computational model, minimizing the makespan, is developed to formulate the ...

Abstract: To solve the computationally heavy inversion of the soil thermal resistivity and the real time ampacity of power cable, a reinforcement learning-based two-modes (Mode 1 and Mode 2) ...

The system integrates a multimodal fall detection framework combining inertial, proprioceptive, and acoustic sensing, along with an improved stance phase detection algorithm.KIMLAB Researchers in the ...

Every year, NeurIPS produces hundreds of impressive papers, and a handful that subtly reset how practitioners think about scaling, evaluation and system design. In 2025, the most consequential works ...

How can a small model learn to solve tasks it currently fails at, without rote imitation or relying on a correct rollout? A team of researchers from Google Cloud AI Research and UCLA have released a ...

Reinforcement learning (RL) is machine learning (ML) in which the learning system adjusts its behavior to maximize the amount of reward and minimize the amount of punishment it receives over time ...

W4S operates in turns. The state contains task instructions, the current workflow program, and feedback from prior executions. An action has 2 components, an analysis of what to change, and new Python ...