reinforcement learning

A context-aware multimodal multi-agent deep reinforcement learning framework for autonomous personalized education

This paper introduces the Context-Aware Multi-Agent Deep Reinforcement Learning (CA-MA-DRL) framework for personalised digital education, shifting from passive analytics to autonomous decision-making agents. The framework integrates Multimodal Learning Analytics with advanced coordination mechanisms, fusing heterogeneo...

Toward zero-shot generalization of deep reinforcement learning for hybrid energy system dispatch

Motivational Strategies Used by Filipino English as a Foreign Language Teachers in Taiwanese Elementary

This descriptive qualitative study explored the motivational strategies used by Filipino English as a Foreign Language (EFL) teachers in Taiwanese public elementary schools. Situated within Taiwan’s Bilingual 2030 policy context, the inquiry examined the strategies teachers used to engage young learners, the perceived...

Reinforcement learning–coupled neural ode modelling reveals ferroptotic resolution failure as a shared dynamical axis linking periodontitis and coronary atherogenesis

The molecular axis underlying the epidemiologically established association between periodontitis and coronary heart disease (CHD) remains undefined at mechanistic, systems-level resolution. Classical comparative transcriptomics captures correlational gene lists but cannot reconstruct directional disease-state dynamics...

Emergency Evacuation in Classrooms: A Review

Classroom evacuation is constrained by relatively fixed occupant positions, dense desk-and-chair arrangements, restricted aisles, and limited exits, and is jointly influenced by occupant behavior, spatial conditions, and emergency scenarios. This review proposes a four-stage framework comprising scenario and data, expe...

Biased processing of multiple outcomes in human reinforcement learning: evidence from computational modeling and eye-tracking

In many circumstances, choices result in multiple simultaneous outcomes, all of which should be integrated to optimally update reward expectation. Yet, to date, empirical investigations of reinforcement learning have mostly focused on situations where choices deliver only one outcome at a time. To understand how humans...

Human-Centric AI-Driven Sustainable and Resilient Design AI for Energy-efficient and Carbon-neutral Buildings

This chapter examines how Human-Centric Artificial Intelligence is transforming sustainable and resilient design in the built environment. With buildings responsible for nearly 40% of global energy use and 30% of greenhouse gas emissions, the chapter underscores the need for energy-efficient and carbon-neutral architec...

AI- Driven Adaptive Resource Allocation For Next- Generation IoT Communication Networks

The emergence of Internet of Things (IoT) devices in next-generation communication networks has brought about new and complex challenges related to resource management, which include massive connectivity, heterogeneous traffic loads, and strict energy considerations. Static and heuristic resource allocation algorithms...

RACINES (Robotic Acquisition for Cardiac Intelligent Navigation Echography Systems)

The RACINES dataset was collected to support the development of a simulation environment based on generative adversarial networks and the training of a deep reinforcement learning policy for robotic cardiac ultrasound scanning. The data were collected using a robotic arm equipped with an ultrasound probe to perform car...

AI-Based Logic Synthesis: Transforming Electronic Design Automation Worflows

Abstract Artificial intelligence (AI) is increasingly being applied to electronic design automation (EDA), particularly in logic synthesis, where designers must choose optimization operations that affect circuit area, timing, power, and quality of results (QoR). This study reviews recent AI-based approaches that suppor...

Instructional Leadership Styles of Master Teachers and Classroom Management Delivery in Public Secondary Schools in Oriental Mindoro: Basis for a Strategic Model

This descriptive-correlational study examined the instructional leadership styles of master teachers and the classroom management delivery of regular teachers in public secondary schools in the Division of Oriental Mindoro. The respondents were 200 educators, comprising 50 master teachers and 150 regular teachers selec...

Improving performance in SNN-based deep reinforcement learning via transition-aware embeddings

Deep Reinforcement Learning (DRL), which integrates reinforcement learning with deep neural networks (DNNs), has been extensively researched across diverse domains. Robotics, in particular, has seen significant advancements, as DRL enables agents to extract meaningful features from high-dimensional observations and mak...