Personalized learning in AI-driven music education still faces challenges such as the difficulty of integrating multimodal learning evidence, the complexity of modeling music skill dependencies, and the lack of long-term optimization of learning pathways. To address these issues, this paper proposes a framework for opt...
Meng Lin· International Journal of Art...· 0 citations
This dataset contains the simulation code and results for the research paper "Deep Reinforcement Learning for Supply Chain Resilience: The Tengiz–Sileno Ethane Corridor in Kazakhstan." Contents: 1. Python source code for the physics-based simulator of the Tengiz → GSK → Sileno ethane supply chain (sileno_env.py, config...
Background: Elastic and cognitive optical networks vary modulation format, symbol rate, launch power and route while a service is carrying traffic, so the physical layer has to be measured continuously rather than engineered once at commissioning. Optical performance monitoring (OPM) and modulation format identificatio...
G Lakshmi, S. Brindha· Indian Journal of Science an...· 0 citations
A deep reinforcement learning framework for intelligent cloud resource allocation that jointly optimizes resource utilization, Service Level Agreement compliance, infrastructure cost, and energy efficiency, and adapts to workload distribution shifts within 200 episodes without manual retuning is presented.
Msr Prasad· International Journal of Tec...· 0 citations
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This review critically examines the integration of AI, IoT, and HCI in smart healthcare monitoring and identifies unresolved challenges involving dataset bias, sensor reliability, model drift, human over-reliance, alarm fatigue, interoperability, regulatory compliance, and equitable access.
Swamy Tn· International Journal of Res...· 0 citations
Project TALOS is an autonomous research intelligence platform powered by deep reinforcement learning (DDDQN), multi-tier LLM orchestration, and the Grey Wolf Optimizer (GWO). It conducts end-to-end scientific literature discovery and evaluation across 16 academic APIs.
Christos Smarlamakis, Efstratios Georgopoulos· Zenodo (CERN European Organi...· 0 citations
Order allocation in steel manufacturing constitutes a large-scale NP-hard combinatorial optimization problem. The task assigns heterogeneous subplates to motherplates to minimize material waste, directly influencing production cost. Existing approaches rely on simplified assumptions or predefined cutting patterns, whic...
An AI-Based Smart Load Balancing Electric Vehicle Charging Hub (AI-SLBECH), which offers an effective method for integrating all three service modalities (dispatch mode, predictive scheduling using LSTM models and optimal policies through PPO-RL algorithms), as well as real-time monitoring via Internet of Things (IoT)...
R. Santhoshkumar, S. Suresh· Discover Electronics· 0 citations
(c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. Two literatures make claims about what happens when a language model trains on data that resembles its own distribution. One asks whether reinforcement learning forgets a model's prior capabilities less than supervised fine-tuning does, and answers yes: on-policy R...
Pranay M. Mahendrakar· Zenodo (CERN European Organi...· 0 citations
(c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. Two literatures make claims about what happens when a language model trains on data that resembles its own distribution. One asks whether reinforcement learning forgets a model's prior capabilities less than supervised fine-tuning does, and answers yes: on-policy R...
Pranay M. Mahendrakar· Zenodo (CERN European Organi...· 0 citations
Industry 5.0 signifies the emotional intelligence and human creativity which carries the exactness of intelligent machines. There are two core visions like bioeconomy and human–robot collaboration to focusing the technological advancement, sources of efficiency, and sustainability development. Therefore, forward Automa...
This chapter explores hybrid fuzzy–AI control architectures that combine interpretable rule-based control with learning-based adaptation. It begins by reviewing the motivation for hybrid approaches, particularly in situations where fuzzy controllers provide interpretable baseline behavior but may have limitations under...
R. Pasko· Practical AI Control Methods· 0 citations
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 7, 2026
Students in MIT’s Concourse program delve deeply into the human condition, debate challenging questions, and learn to develop judgment about issues that can’t be quantified.
Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 6, 2026
