We address the challenge of multi-agent cooperation, where agents achieve a common goal by cooperating with decentralized agents under complex partial observations. Existing cooperative agent systems often struggle with efficiently processing continuously accumulating information, managing globally suboptimal planning due to lack of consideration of collaborators, and addressing false planning caused by environmental changes introduced by other collaborators. To overcome these challenges, we propose the RElevance, Proximity, and Validation-Enhanced Cooperative Language Agent (REVECA), a novel cognitive architecture powered by GPT-4o-mini. REVECA enables efficient memory management, optimal planning, and cost-effective prevention of false planning by leveraging Relevance Estimation, Adaptive Planning, and Trajectory-based Validation. Extensive experimental results demonstrate REVECA's superiority over existing methods across various benchmarks, while a user study reveals its potential for achieving trustworthy human-AI cooperation.

Marker-based optical motion capture (mocap) systems are increasingly utilized for acquiring 3D human motion, offering advantages in capturing the subtle nuances of human movement, style consistency, and ease of obtaining desired motion. Motion data acquisition via mocap typically requires laborious marker labeling and motion reconstruction, recent deep-learning solutions have aimed to automate the process. However, such solutions generally presuppose a fixed marker configuration to reduce learning complexity, thereby limiting flexibility. To overcome the limitation, we introduce DAMO, an end-to-end deep solver, proficiently inferring arbitrary marker configurations and optimizing pose reconstruction. DAMO outperforms state-of-the-art like SOMA and MoCap-Solver in scenarios with significant noise and unknown marker configurations. We expect that DAMO will meet various practical demands such as facilitating dynamic marker configuration adjustments during capture sessions, processing marker clouds irrespective of whether they employ mixed or entirely unknown marker configurations, and allowing custom marker configurations to suit distinct capture scenarios. DAMO code and pretrained models are available at https://github.com/CritBear/damo .

Since the frequency and intensity of heatwaves are expected to increase due to global warming, it is crucial to establish a simplified approach to preventing personal heat-related illnesses as occupational hazard under extremely hot environments, by taking into account individual differences in heat strain. In light of this, this study proposed a simplified forecast model for personal heat strain under extremely hot environments by utilizing feature importance in machine learning, focused on enhancing its field applicability. Using the 1417 records gathered from the experiments under extremely hot conditions, the simplified model was developed by utilizing four types of machine learning algorithms (i.e., random forest, extreme gradient boosting, support vector regression, and multi-layer perceptron). As a result, the proposed simplified models with four principal features (i.e., dry-bulb temperature, radiant temperature, relative humidity, and percentage body fat) were found to be the most reliable, resulting in a small difference of 0.047 °C compared to the reference model. Since the principal biometric features can be easily measured by a simple and low-cost body composition test (but not with the physical measurement of personal core body temperature) before they are put into the field, the proposed approach will aid in providing more systematic and efficient management system for proactively preventing personal heat-related illnesses. Furthermore, it is expected that the field applicability of the proposed approach can be continuously enhanced by utilizing the accumulated big data gathered from the field-based living-lab projects over a lengthy period.

To improve construction safety through proactive prevention strategies, it is essential to leverage virtual reality (VR) training programs that foster active learning through dynamic interaction between VR systems and learners. Addressing this need, this study proposed an interactive immersive VR (IVR)-based construction safety training framework, incorporating four interactive learning elements (ILEs): immediate feedback, basic interaction with objects, assembling objects, and knowledge testing. This study divided sixty trainees into two groups: one experienced the proposed novel interactive IVR-based training approach, while the other underwent traditional, non-interactive IVR-based training. To accurately evaluate the impact of the four ILEs on individual learning performance, this study conducted t-test analyses and utilized machine learning-based SHAP (Shapley Additive exPlanations) analysis to compare the results between both groups. The findings indicated that the proposed method significantly enhanced active learning, positively influencing the sensory and knowledge domains of trainees more effectively than the existing methods. Notably, 'ILE-(1). Immediate feedback' and 'ILE-(2). Basic interaction with objects' were identified as key factors in improving personal learning outcomes during training. This study contributes significantly to the field of management in engineering, demonstrating that interactive IVR-based training can offer a superior learning experience and enhance safety performance, thereby reducing accident rates and creating safer construction sites.