Rethinking Intelligent Behavior as Competitive Games for Handling Adversarial Challenges to Machine Learning

2020 ◽  
pp. 3-16
Author(s):  
Joseph B. Collins ◽  
Prithviraj Dasgupta
Keyword(s):  
Machine Learning ◽  
Intelligent Behavior ◽  
Competitive Games

The Classification of Skateboarding Tricks : A Transfer Learning and Machine Learning Approach

Mekatronika ◽  
2020 ◽  
Vol 2 (2) ◽  
pp. 1-12
Author(s):  
Muhammad Nur Aiman Shapiee ◽  
Muhammad Ar Rahim Ibrahim ◽  
Muhammad Amirul Abdullah ◽  
Rabiu Muazu Musa ◽  
Noor Azuan Abu Osman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Classification Accuracy ◽  
Nearest Neighbor ◽  
Olympic Games ◽  
Learning Approach ◽  
K Nearest Neighbor ◽  
Test Dataset ◽  
Machine Learning Approach ◽  
Competitive Games

The skateboarding scene has arrived at new statures, particularly with its first appearance at the now delayed Tokyo Summer Olympic Games. Hence, attributable to the size of the game in such competitive games, progressed creative appraisal approaches have progressively increased due consideration by pertinent partners, particularly with the enthusiasm of a more goal-based assessment. This study purposes for classifying skateboarding tricks, specifically Frontside 180, Kickflip, Ollie, Nollie Front Shove-it, and Pop Shove-it over the integration of image processing, Trasnfer Learning (TL) to feature extraction enhanced with tradisional Machine Learning (ML) classifier. A male skateboarder performed five tricks every sort of trick consistently and the YI Action camera captured the movement by a range of 1.26 m. Then, the image dataset were features built and extricated by means of  three TL models, and afterward in this manner arranged to utilize by k-Nearest Neighbor (k-NN) classifier. The perception via the initial experiments showed, the MobileNet, NASNetMobile, and NASNetLarge coupled with optimized k-NN classifiers attain a classification accuracy (CA) of 95%, 92% and 90%, respectively on the test dataset. Besides, the result evident from the robustness evaluation showed the MobileNet+k-NN pipeline is more robust as it could provide a decent average CA than other pipelines. It would be demonstrated that the suggested study could characterize the skateboard tricks sufficiently and could, over the long haul, uphold judges decided for giving progressively objective-based decision.

scholarly journals Machine learning in occupational safety and health: protocol for a systematic review

2021 ◽  
Vol 5 (1) ◽  
pp. 32-38
Author(s):  
Sara Maheronnaghsh ◽  
H. Zolfagharnasab ◽  
M. Gorgich ◽  
J. Duarte
Keyword(s):  
Machine Learning ◽  
Systematic Reviews ◽  
Occupational Safety ◽  
Occupational Safety And Health ◽  
Massive Data ◽  
Inclusion Criteria ◽  
Safety And Health ◽  
Intelligent Behavior ◽  
Methodological Practice ◽  
Meta Analyses

Industry 4.0 has shaped the way people look at the world and interact with it, especially concerning the work environment with respect to occupational safety and health (OSH). Machine learning (ML), as a branch of Artificial Intelligence (AI), can be effectively used to create expert systems to exhibit intelligent behavior to provide solutions to complicated problems and finally process massive data. Therefore, a study is proposed to provide the best methodological practice in the light of ML. Alongside the review of previous investigations, the following research aims to determine the ML approaches appropriate to OSH issues. In other words, highlighting specific ML methodologies, which have been employed successfully in others areas. Bearing this objective in mind, one can identify an appropriate ML technique to solve a problem in the OSH domain. Accordingly, several questions were designed to conduct the research. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension for Protocols and Systematic Reviews were used to draw the research outline. The chosen databases were SCOPUS, PubMed, Science Direct, Inspect, and Web of Science. A set of keywords related to the topic were defined, and both exclusion and inclusion criteria were determined. All of the eligible papers will be analyzed, and the extracted information will be included in an Excel form sheet. The results will be presented in a narrative-based form. Additionally, all tables summarizing the most important findings will be offered.

Enhancing Agent Intelligence with Behavior Duplication

2011 ◽  
Vol 403-408 ◽  
pp. 1266-1269 ◽  
Author(s):  
Wei Tang ◽  
Jun Lai
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Human Action ◽  
Machine Learning Algorithm ◽  
Agent Behavior ◽  
Input Action ◽  
Sample Data ◽  
Intelligent Behavior ◽  
Agent Intelligence

The traditional agent intelligence designing always lead to a fixed behavior manner. In this way, the NPC(Non-Player Character) in the game will act in a fixed and expectable way. It has greatly weakened the long-term attraction of single-played game. Extracting the human action patterns using a statistical-based machine learning algorithm can provide an easily-understanding way to implement the agent behavior intelligence. A daemon program records and sample the human player’s input action and related properties of character and virtual environment, and then apply certain statistical-based machine learning algorithm on the sample data. As a result, a human-similar intelligent behavior model was obtained. It can be used to help agent making an action decision. Repeating the learning process can give the agent a variety of intelligent behavior.

scholarly journals Identify Intelligent Behavior in Machines and Similarity Network of Modeling Methods

2019 ◽  
Vol 8 (11) ◽  
pp. 2065-2068
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Semisupervised Learning ◽  
Humanitarian Law ◽  
Similarity Network ◽  
Modeling Methods ◽  
Spam Filters ◽  
Intelligent Behavior ◽  
E Mail

The ICRC additionally endeavours to prevent suffering through marketing and boosting humanitarian law as well as universal altruistic guidelines. Each time Facebook is actually made use of as well as it identifies buddies' pictures, that is actually additionally machine learning. Spam filters in email spares the user coming from having to wade through lots of spam e-mail, that's likewise a learning formula. Data scientists are actually anticipated to become aware of the variations between monitored artificial intelligence as well as without supervision machine learning-- along with ensemble choices in, which utilizes a mixture of strategies techniques, and semisupervised learning, which incorporates administered as well as without supervision methods

scholarly journals Machine Learning Techniques of Contact Aware Communication System: A Comprehensive Overview

2021 ◽  
Author(s):  
Mohsin Shahzad ◽  
Kashif Hussain ◽  
Muhammad Ali Qureshi ◽  
Fareeha Zahoor
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Communication System ◽  
Machine Learning Techniques ◽  
Attractive Alternative ◽  
Comprehensive Overview ◽  
Intelligent Behavior ◽  
Potential Applications ◽  
Almost All ◽  
Universal Communication

Machine Learning (ML) and Artificial Intelligence(AI) have revolutionized almost all fields that are linked to the acquisition of intelligent behavior in the real world. It is an attractive alternative for a researcher of artificial intelligence. Contrary to rule-based programming, ML is an algorithmic approach in which learning comes from existing data. The more data we have these computer systems look at, we say we’re ‘training’ the computer system, and as the computers begin to identify patterns in the data, identify abnormalities in the data from these abnormalities we improve the system architect according to the requirement. This article introduces the use of comprehensive concepts of machine learning, in general, particular, and their potential applications in communications. Furthermore, the current state and futuristic potentials of enabling universal communication with implications of machine learning methods have been explained. In this review paper, we offer a comprehensive talk on distinctive methods/techniques of information analytics, artificial intelligence (AI), and machine learning (ML) moved forward the contact aware communication system.

Machine learning techniques in internet of UAVs for smart cities applications

2021 ◽  
pp. 1-24
Author(s):  
Fahad A. Alqurashi ◽  
F. Alsolami ◽  
S. Abdel-Khalek ◽  
Elmustafa Sayed Ali ◽  
Rashid A. Saeed
Keyword(s):  
Machine Learning ◽  
Data Processing ◽  
Smart Cities ◽  
Machine Learning Techniques ◽  
Unmanned Systems ◽  
Network Efficiency ◽  
Public Network ◽  
Intelligent Behavior ◽  
Network Big Data ◽  
Cloud Network

Recently, there were much interest in technology which has emerged greatly to the development of smart unmanned systems. Internet of UAV (IoUAV) enables an unmanned aerial vehicle (UAV) to connect with public network, and cooperate with the neighboring environment. It also enables UAV to argument information and gather data about others UAV and infrastructures. Applications related to smart UAV and IoUAV systems are facing many impairments issues. The challenges are related to UAV cloud network, big data processing, energy efficiency in IoUAV, and efficient communication between a large amount of different UAV types, in addition to optimum decisions for intelligence. Artificial Intelligence (AI) technologies such as Machine Learning (ML) mechanisms enable to archives intelligent behavior for unmanned systems. Moreover, it provides a smart solution to enhance IoUAV network efficiency. Decisions in data processing are considered one of the most problematic issues related to UAV especially for the operations related to cloud and fog based network levels. ML enables to resolve some of these issues and optimize the Quality of UAV network experience (QoE). The paper provides theoretical fundamentals for ML models and algorithms for IoUAV applications and recently related works, in addition to future trends.

scholarly journals Biological Correlates Of Machine Learning

2021 ◽  
Author(s):  
Walter Gordon Kruberg
Keyword(s):  
Machine Learning ◽  
Back Propagation ◽  
Neural Nets ◽  
Training Algorithms ◽  
Memory Research ◽  
The Public ◽  
Intelligent Machines ◽  
Intelligent Behavior ◽  
Network Operations ◽  
Error Terms

The greatest successes of artificial intelligence are intelligent machines founded on models of how neurons interact with each other. In creating these models, machine-learning modelers divide intelligent behavior into separate learning and operating experiences: training and inference. We, the public, see machine-learning engines while they are operating, in inference mode, as they interpret our requests and images. To make them operational, their neural nets are trained using algorithms that are widely tested, optimized, and which even compete against each other regularly. The most successful training algorithms use back-propagation to train their neural networks.What has been learned about intelligence from these models is largely absent in biological models: that creating the memories underlying intelligent behavior occurs independently of network operations and requires network-level functions. This paper recasts memory research in the context of those two requirements and outlines novel biological correlates for training and inference modes, vector spaces and error terms. Specific biological machinery is identified as holding the key to understanding memory creation: the operation of tripartite synapses and how astrocytes act as normalization operators to manage synaptic plasticity.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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