AIMC Topic: Artificial Intelligence

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Curiosity Search: Producing Generalists by Encouraging Individuals to Continually Explore and Acquire Skills throughout Their Lifetime.

PloS one
Natural animals are renowned for their ability to acquire a diverse and general skill set over the course of their lifetime. However, research in artificial intelligence has yet to produce agents that acquire all or even most of the available skills ...

Cognitive bio-radar: The natural evolution of bio-signals measurement.

Journal of medical systems
In this article we discuss a novel approach to Bio-Radar, contactless measurement of bio-signals, called Cognitive Bio-Radar. This new approach implements the Bio-Radar in a Software Defined Radio (SDR) platform in order to obtain awareness of the en...

An Efficient Data Partitioning to Improve Classification Performance While Keeping Parameters Interpretable.

PloS one
Supervised machine learning methods typically require splitting data into multiple chunks for training, validating, and finally testing classifiers. For finding the best parameters of a classifier, training and validation are usually carried out with...

A Novel Flexible Inertia Weight Particle Swarm Optimization Algorithm.

PloS one
Particle swarm optimization (PSO) is an evolutionary computing method based on intelligent collective behavior of some animals. It is easy to implement and there are few parameters to adjust. The performance of PSO algorithm depends greatly on the ap...

Impact of three task demand factors on simulated unmanned system intelligence, surveillance, and reconnaissance operations.

Ergonomics
The present study investigated how three task demand factors influenced performance, subjective workload and stress of novice intelligence, surveillance, and reconnaissance operators within a simulation of an unmanned ground vehicle. Manipulations we...

Model-based reinforcement learning with dimension reduction.

Neural networks : the official journal of the International Neural Network Society
The goal of reinforcement learning is to learn an optimal policy which controls an agent to acquire the maximum cumulative reward. The model-based reinforcement learning approach learns a transition model of the environment from data, and then derive...

Applying Cost-Sensitive Extreme Learning Machine and Dissimilarity Integration to Gene Expression Data Classification.

Computational intelligence and neuroscience
Embedding cost-sensitive factors into the classifiers increases the classification stability and reduces the classification costs for classifying high-scale, redundant, and imbalanced datasets, such as the gene expression data. In this study, we exte...

A Hybrid Short-Term Traffic Flow Prediction Model Based on Singular Spectrum Analysis and Kernel Extreme Learning Machine.

PloS one
Short-term traffic flow prediction is one of the most important issues in the field of intelligent transport system (ITS). Because of the uncertainty and nonlinearity, short-term traffic flow prediction is a challenging task. In order to improve the ...

Modeling and analysis of bio-syncretic micro-swimmers for cardiomyocyte-based actuation.

Bioinspiration & biomimetics
Along with sensation and intelligence, actuation is one of the most important factors in the development of conventional robots. Many novel achievements have been made regarding bio-based actuators to solve the challenges of conventional actuation. H...

The body of knowledge: On the role of the living body in grounding embodied cognition.

Bio Systems
Embodied cognition is a hot topic in both cognitive science and AI, despite the fact that there still is relatively little consensus regarding what exactly constitutes 'embodiment'. While most embodied AI and cognitive robotics research views the bod...