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A control-plane analysis distinguishing three concepts routinely collapsed in contemporary AI discourse: event verifiability, proxy validity, and governance sufficiency. Taking Reinforcement Learning with Self-Verifiable Rewards and its SpyRL instantiation as its case, it accepts the engineering contribution — an envir...
This chapter explores the transformative role of artificial intelligence (AI) and machine learning (ML) in modern healthcare and medical training. We first outline the rapid progress of AI, fueled by the digitization of health data and advances in computing, which has led to significant shifts in how clinical data and...
This paper investigates the output-feedback inverse reinforcement learning (IRL) problem and proposes a data-driven output-feedback off-policy inverse Q-learning algorithm that relies solely on input–output trajectory data from an expert system to address the problem of an unknown performance index in discrete-time lin...
This work states that there has been an upsurge of interest and development of RL theory and applications in the communities of stochastic control, stochastic analysis and unsupervised learning.
Heating, Ventilation, and Air Conditioning (HVAC) systems account for 60–70% of residential electricity consumption in Oman, where extreme desert climate, with temperatures regularly exceeding 45 °C create substantial cooling demands. Unlike general reviews of smart HVAC controls, this study specifically evaluates the...
A comprehensive extraction grid covering the 21 selected primary studies. Synthesizes structural data including author, publication venue, process mining domain, reinforcement learning approach, and explainability (XAI) focus.
A comprehensive extraction grid covering the 21 selected primary studies. Synthesizes structural data including author, publication venue, process mining domain, reinforcement learning approach, and explainability (XAI) focus.
This study is intended to serve as a structured reference for the development of scalable, trustworthy, and intelligent IoV systems by highlighting state-of-the-art techniques, open research gaps, and future directions.
A new machine-learning approach broke records for weather forecasting for an important time frame in emergency planning.
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
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.
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.
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