Spatial Assembly: Generative Architecture With Reinforcement Learning, Self Play and Tree Search

With this work, we investigate the use of Reinforcement Learning (RL) for the generation of spatial assemblies, by combining ideas from Procedural Generation algorithms (Wave Function Collapse algorithm (WFC)) and RL for Game Solving. WFC is a Generative Design algorithm, inspired by Constraint Solving. In WFC, one defines a set of tiles/blocks and constraints and the algorithm generates an assembly that satisfies these constraints. Casting the problem of generation of spatial assemblies as a Markov Decision Process whose states transitions are defined by WFC, we propose an algorithm that uses Reinforcement Learning and Self-Play to learn a policy that generates assemblies that maximize objectives set by the designer. Finally, we demonstrate the use of our Spatial Assembly algorithm in Architecture Design.Comment: Workshop on Machine Learning for Creativity and Design at the 34rd Conference on Neural Information Processing Systems (NeurIPS 2020

Integrated Reconfigurable Autonomous Architecture System

Advances in state-of-the-art architectural robotics and artificially intelligent design algorithms have the potential not only to transform how we design and build architecture, but to fundamentally change our relationship to the built environment. This system is situated within a larger body of research related to embedding autonomous agency directly into the built environment through the linkage of AI, computation, and robotics. It challenges the traditional separation between digital design and physical construction through the development of an autonomous architecture with an adaptive lifecycle. Integrated Reconfigurable Autonomous Architecture System (IRAAS) is composed of three components: 1) an interactive platform for user and environmental data input, 2) an agent-based generative space planning algorithm with deep reinforcement learning for continuous spatial adaptation, 3) a distributed robotic material system with bi-directional cyber-physical control protocols for simultaneous state alignment. The generative algorithm is a multi-agent system trained using deep reinforcement learning to learn adaptive policies for adjusting the scales, shapes, and relational organization of spatial volumes by processing changes in the environment and user requirements. The robotic material system was designed with a symbiotic relationship between active and passive modular components. Distributed robots slide their bodies on tracks built into passive blocks that enable their locomotion while utilizing a locking and unlocking system to reconfigure the assemblages they move across. The three subsystems have been developed in relation to each other to consider both the constraints of the AI-driven design algorithm and the robotic material system, enabling intelligent spatial adaptation with a continuous feedback chain

Tesseract: Integrated Reconfigurable Autonomous Architecture System

TESSERACT is an autonomous architecture developed through a voxel-based robotic material system that continuously reshapes communities through a socio-economic model with shifting fractional ownership. This incentivizes users to trade and share portions of physical space in realtime (Figure 1). Based on the Integrated Reconfigurable Autonomous Architecture System, TESSERACT buildings have a continuously adaptive lifecycle enabling the shifting spatial needs of communities to be negotiated through an Observe, Generate, [re]Assemble feedback loop (Figure 2). TESSERACT is implemented with three integrated components: an interactive platform, a space planning algorithm, and a distributed robotic material system

Expectancy-Value Models for the STEM Persistence Plans of Ninth-Grade, High-Ability Students: A Comparison Between Black, Hispanic, and White Students

Group differences in the effects of the expectancies and values that high-ability students have for science and mathematics on plans to persist in science, technology, engineering, and mathematics (STEM) were investigated. A nationally representative sample of ninth-grade students, the High School Longitudinal Study of 2009 (HSLS: 2009; n = 21,444) was used. The analytic sample was 1,757 (48% female, 52% male) Black (13.8%), Hispanic (26.7%), and White (59.6%) students who scored in the top 10% of their race group on the mathematics achievement test. Hierarchical logistic regression models were developed for each race/ethnicity group to examine the relationships of demographic and expectancy-value variables with STEM persistence status. Science attainment value, science intrinsic value, and STEM utility value were predictive of STEM persistence, but these variables operated differently in groups of Black, Hispanic, and White students. Implications for educators include the need for ways to improve perceptions of science identity and awareness of the utility of science and mathematics courses. (C) 2013 Wiley Periodicals, Inc

MindSpaces:Art-driven Adaptive Outdoors and Indoors Design

MindSpaces provides solutions for creating functionally and emotionally appealing architectural designs in urban spaces. Social media services, physiological sensing devices and video cameras provide data from sensing environments. State-of-the-Art technology including VR, 3D design tools, emotion extraction, visual behaviour analysis, and textual analysis will be incorporated in MindSpaces platform for analysing data and adapting the design of spaces.</p

Injury rates and injury risk factors among federal bureau of investigation new agent trainees

<p>Abstract</p> <p>Background</p> <p>A one-year prospective examination of injury rates and injury risk factors was conducted in Federal Bureau of Investigation (FBI) new agent training.</p> <p>Methods</p> <p>Injury incidents were obtained from medical records and injury compensation forms. Potential injury risk factors were acquired from a lifestyle questionnaire and existing data at the FBI Academy.</p> <p>Results</p> <p>A total of 426 men and 105 women participated in the project. Thirty-five percent of men and 42% of women experienced one or more injuries during training. The injury incidence rate was 2.5 and 3.2 injuries/1,000 person-days for men and women, respectively (risk ratio (women/men) = 1.3, 95% confidence interval = 0.9-1.7). The activities most commonly associated with injuries (% of total) were defensive tactics training (58%), physical fitness training (20%), physical fitness testing (5%), and firearms training (3%). Among the men, higher injury risk was associated with older age, slower 300-meter sprint time, slower 1.5-mile run time, lower total points on the physical fitness test (PFT), lower self-rated physical activity, lower frequency of aerobic exercise, a prior upper or lower limb injury, and prior foot or knee pain that limited activity. Among the women higher injury risk was associated with slower 300-meter sprint time, slower 1.5-mile run time, lower total points on the PFT, and prior back pain that limited activity.</p> <p>Conclusion</p> <p>The results of this investigation supported those of a previous retrospective investigation emphasizing that lower fitness and self-reported pain limiting activity were associated with higher injury risk among FBI new agents.</p