AIMC Topic: Environment

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Environmental influences on evolvable robots.

PloS one
The field of Evolutionary Robotics addresses the challenge of automatically designing robotic systems. Furthermore, the field can also support biological investigations related to evolution. In this paper, we evolve (simulated) modular robots under d...

ToyArchitecture: Unsupervised learning of interpretable models of the environment.

PloS one
Research in Artificial Intelligence (AI) has focused mostly on two extremes: either on small improvements in narrow AI domains, or on universal theoretical frameworks which are often uncomputable, or lack practical implementations. In this paper we a...

Deep Multi-Critic Network for accelerating Policy Learning in multi-agent environments.

Neural networks : the official journal of the International Neural Network Society
Humans live among other humans, not in isolation. Therefore, the ability to learn and behave in multi-agent environments is essential for any autonomous system that intends to interact with people. Due to the presence of multiple simultaneous learner...

Numerosity discrimination in deep neural networks: Initial competence, developmental refinement and experience statistics.

Developmental science
Both humans and non-human animals exhibit sensitivity to the approximate number of items in a visual array, as indexed by their performance in numerosity discrimination tasks, and even neonates can detect changes in numerosity. These findings are oft...

Neural Network Ensembles for Sensor-Based Human Activity Recognition Within Smart Environments.

Sensors (Basel, Switzerland)
In this paper, we focus on data-driven approaches to human activity recognition (HAR). Data-driven approaches rely on good quality data during training, however, a shortage of high quality, large-scale, and accurately annotated HAR datasets exists fo...

Machine Learning Approaches to Improve Three Basic Plant Phenotyping Tasks Using Three-Dimensional Point Clouds.

Plant physiology
Developing automated methods to efficiently process large volumes of point cloud data remains a challenge for three-dimensional (3D) plant phenotyping applications. Here, we describe the development of machine learning methods to tackle three primary...

Fuzzy Model for Quantitative Assessment of Environmental Start-up Projects in Air Transport.

International journal of environmental research and public health
The purpose of this paper is to develop an applied fuzzy model of information technology to obtain quantitative estimates of environmental start-up projects in air transport. The developed model will become a useful tool for venture funds, business a...

A Fuzzy Model of Risk Assessment for Environmental Start-up Projects in the Air Transport Sector.

International journal of environmental research and public health
The purpose of this paper is to develop a fuzzy model of the risk assessment for environmental start-up projects in the air transport sector at the stage of business expansion. The model developed for the following software will be a useful tool for ...

Discrimination of indoor versus outdoor environmental state with machine learning algorithms in myopia observational studies.

Journal of translational medicine
BACKGROUND: Wearable smart watches provide large amount of real-time data on the environmental state of the users and are useful to determine risk factors for onset and progression of myopia. We aim to evaluate the efficacy of machine learning algori...

Recovering Wind-Induced Plant Motion in Dense Field Environments via Deep Learning and Multiple Object Tracking.

Plant physiology
Understanding the relationships between local environmental conditions and plant structure and function is critical for both fundamental science and for improving the performance of crops in field settings. Wind-induced plant motion is important in m...