Results 11 to 20 of about 36,270,671 (149)
Advanced Intelligent Discovery, EarlyView.This article outlines how artificial intelligence could reshape the design of next‐generation transistors as traditional scaling reaches its limits. It discusses emerging roles of machine learning across materials selection, device modeling, and fabrication processes, and highlights hierarchical reinforcement learning as a promising framework for ...
Shoubhanik Nath +4 more
wiley +1 more source
, 2022 Welcome to AAMAS 2022, the 21st edition of the International Conference on Autonomous Agents and Multiagent Systems! AAMAS gathers researchers and practitioners from around the world to share and discuss the latest advances in the field of autonomous ...
core +1 more source
Advanced Intelligent Discovery, EarlyView.Artificial intelligence is redefining network pharmacology (NP). By integrating knowledge graph engineering, geometric deep learning, multiomics anchoring, and generative reasoning, AI‐driven NP (AI‐NP) transforms static target mapping into dynamic, predictive modeling.
Cong Wang +9 more
wiley +1 more source
Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.This review aims to provide a broad understanding for interdisciplinary researchers in engineering and clinical applications. It addresses the development and control of magnetic actuation systems (MASs) in clinical surgeries and their revolutionary effects in multiple clinical applications.
Yingxin Huo +3 more
wiley +1 more source
Dynamic Obstacle Avoidance of Metamorphic Microrobots Using Concentric Sector Navigator
Advanced Intelligent Systems, EarlyView.Metamorphic magnetic microrobots are navigated using a concentric sector navigator for dynamic obstacle avoidance in both microroller and swarm states. After microroller transport, citrate‐triggered dissolution releases nanoparticles that reassemble into a controllable swarm.
Zhaowen Su +4 more
wiley +1 more source
Multiagent reinforcement learning in Markov games : asymmetric and symmetric approaches [PDF]
, 2004 Modern computing systems are distributed, large, and heterogeneous. Computers, other information processing devices and humans are very tightly connected with each other and therefore it would be preferable to handle these entities more as agents than ...
Könönen, Ville
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Advanced Intelligent Systems, EarlyView.A hybrid Reinforcement Learning–Explainable AI framework integrates SHAP and LIME explanations directly into a Deep Q‐Network inference loop for real‐time ICU decision support. Trained on 18 142 mechanically ventilated stays from the eICU database, the system attains 93.0% decision accuracy, 20% fewer errors than RL alone, and a 91% clinician trust ...
Jannatul Ferdaus Disha +2 more
wiley +1 more source
Do (and say) as I say: Linguistic adaptation in human-computer dialogs [PDF]
, 2014 © Theodora Koulouri, Stanislao Lauria, and Robert D. Macredie. This article has been made available through the Brunel Open Access Publishing Fund.There is strong research evidence showing that people naturally align to each other’s vocabulary, sentence ...
Macredie, R, Kolouri, T, Lauria, S
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The Collective Power of Bacteria as a Blueprint for Emergent Intelligence
Advanced Intelligent Systems, EarlyView.Small cells, powerful collectives. Bacteria demonstrate how sophisticated behaviors can emerge from many simple individuals working together. We explore the remarkable world of bacterial communities and the mechanisms that underpin their complex emergent behaviors, enabling impressive adaptability, robustness, and responsiveness to changing ...
Johanna A. Blee +2 more
wiley +1 more source
Graph Neural Network‐Based Reinforcement Learning for Decentralized Multi‐Robot Manipulation
Advanced Intelligent Systems, EarlyView.Robot arms lifting a large object face a trade‐off: centralized controllers explode in parameters, while decentralized ones cannot coordinate. A GNN resolves this—each arm runs its own network but acts on the full team state, achieving centralized‐level coordination with decentralized execution. Trained across team sizes, a single policy scales to four‐
Tong Chen +3 more
wiley +1 more source