reinforcement learning

Secure and trust-aware pricing mechanisms for flexible energy markets in electric vehicle charging station integrated microgrids: A multi-agent reinforcement learning approach

Construction and Real Estate and Artificial Intelligence: The Slowest Adopter, Thin Evidence, and Two Applications Where the Results Are Already Visible

Purpose. Construction is a large industry with a reputation for slow technological change, and real estate is a market in which valuation is a prediction problem. This article asks where artificial intelligence stands in construction and real estate in 2026 and where they may be by about 2035. Design and methods. A rev...

Optimal scheduling of hydrogen-integrated hybrid energy storage systems via multi-agent coordinated reinforcement learning

Decision intelligence for electric bus charging scheduling through LLM-driven reward shaping and reinforcement learning

Proceedings of the 3rd Conference on AI in and for Space (SPAICE 2026)

We are excited to release the preliminary proceedings of SPAICE 2026, the third edition of the European Space Agency’s conference on AI in and for Space, held at ESTEC in Noordwijk, the Netherlands, from 21 to 23 October 2026. When SPAICE began in 2024, the question in the room was still largely whether AI methods coul...

AI-Driven Software Testing for a Test Center of Excellence: A Systematic Mapping and Evidence Synthesis

Artificial intelligence (AI) is increasingly used to automate and optimize software testing, but the evidence is distributed across heterogeneous testing activities, AI techniques, and evaluation measures. This study maps AI-driven software-testing research and translates the evidence into capabilities relevant to a Te...

MutateRL: Reinforcement learning-based test input generation with dynamic mutation selection for deep neural networks

Hierarchical graph reinforcement learning for energy-efficient dual-resource distributed flexible job shop scheduling

Safe and energy-efficient wide-range gas turbine control via condition-adaptive reinforcement learning

Proactive service migration in vehicular edge computing via map-aware mobility prediction and dynamic action-space deep reinforcement learning

Beyond accuracy in crop classification: calibration, selective prediction, and robustness

Introduction: Near-perfect crop-label accuracy can conceal uncertainty, perturbation sensitivity, and weak explanations. This study evaluates these reliability dimensions without treating benchmark classification as an agronomic recommendation. Materials and methods: Ten classifiers were evaluated using five matched st...

Data Quality, Feature Engineering, and Model Reliability in Large-Scale AI Multi-Agentic Systems

A conceptual framework linking data quality assurance, feature engineering, model training and reliability testing, and deployment to an agent population is proposed, closed by a cross-agent monitoring and feedback loop.