scholarly journals Redefining Safety in Light of Human-Robot Interaction: A Critical Review of Current Standards and Regulations

2021 ◽  
Vol 3 ◽  
Author(s):  
Alberto Martinetti ◽  
Peter K. Chemweno ◽  
Kostas Nizamis ◽  
Eduard Fosch-Villaronga
Keyword(s):  
Machine Learning ◽  
Human Robot Interaction ◽  
Cloud Services ◽  
Machine Learning Techniques ◽  
Physical Interaction ◽  
Robot Interaction ◽  
Learning Techniques ◽  
Standards And Regulations ◽  
Physical Impact ◽  
Definition Of

Policymakers need to consider the impacts that robots and artificial intelligence (AI) technologies have on humans beyond physical safety. Traditionally, the definition of safety has been interpreted to exclusively apply to risks that have a physical impact on persons’ safety, such as, among others, mechanical or chemical risks. However, the current understanding is that the integration of AI in cyber-physical systems such as robots, thus increasing interconnectivity with several devices and cloud services, and influencing the growing human-robot interaction challenges how safety is currently conceptualised rather narrowly. Thus, to address safety comprehensively, AI demands a broader understanding of safety, extending beyond physical interaction, but covering aspects such as cybersecurity, and mental health. Moreover, the expanding use of machine learning techniques will more frequently demand evolving safety mechanisms to safeguard the substantial modifications taking place over time as robots embed more AI features. In this sense, our contribution brings forward the different dimensions of the concept of safety, including interaction (physical and social), psychosocial, cybersecurity, temporal, and societal. These dimensions aim to help policy and standard makers redefine the concept of safety in light of robots and AI’s increasing capabilities, including human-robot interactions, cybersecurity, and machine learning.

scholarly journals A Survey on Emotion Recognition for Human Robot Interaction

2021 ◽  
Vol 28 (2) ◽  
pp. 125-146
Keyword(s):  
Machine Learning ◽  
Emotion Recognition ◽  
Human Robot Interaction ◽  
Machine Learning Techniques ◽  
Emotional States ◽  
Robot Interaction ◽  
Artificial Intelligent ◽  
Learning Techniques ◽  
Human Counterpart ◽  
Recent Developments

With the recent developments of technology and the advances in artificial intelligent and machine learning techniques, it becomes possible for the robot to acquire and show the emotions as a part of Human-Robot Interaction (HRI). An emotional robot can recognize the emotional states of humans so that it will be able to interact more naturally with its human counterpart in different environments. In this article, a survey on emotion recognition for HRI systems has been presented. The survey aims to achieve two objectives. Firstly, it aims to discuss the main challenges that face researchers when building emotional HRI systems. Secondly, it seeks to identify sensing channels that can be used to detect emotions and provides a literature review about recent researches published within each channel, along with the used methodologies and achieved results. Finally, some of the existing emotion recognition issues and recommendations for future works have been outlined.

Improving Automated Planning with Machine Learning

2010 ◽  
pp. 599-620
Author(s):  
Susana Fernández Arregui ◽  
Sergio Jiménez Celorrio ◽  
Tomás de la Rosa Turbides
Keyword(s):  
Machine Learning ◽  
Automated Planning ◽  
Machine Learning Techniques ◽  
Advantages And Disadvantages ◽  
Learning Techniques ◽  
Depth Analysis ◽  
Search Processes ◽  
Action Models ◽  
New Challenges ◽  
Definition Of

This chapter reports the last machine learning techniques for the assistance of automated planning. Recent discoveries in automated planning have opened the scope of planners, from toy problems to real-world applications, making new challenges come into focus. The planning community believes that machine learning can assist to address these new challenges. The chapter collects the last machine learning techniques for assisting automated planners classified in: techniques for the improvement of the planning search processes and techniques for the automatic definition of planning action models. For each technique, the chapter provides an in-depth analysis of their domain, advantages and disadvantages. Finally, the chapter draws the outline of the new promising avenues for research in learning for planning systems.

Improving Automated Planning with Machine Learning

2012 ◽  
pp. 1355-1373
Author(s):  
Susana Fernández Arregui ◽  
Sergio Jiménez Celorrio ◽  
Tomás de la Rosa Turbides
Keyword(s):  
Machine Learning ◽  
Automated Planning ◽  
Machine Learning Techniques ◽  
Advantages And Disadvantages ◽  
Learning Techniques ◽  
Depth Analysis ◽  
Search Processes ◽  
Action Models ◽  
New Challenges ◽  
Definition Of

This chapter reports the last machine learning techniques for the assistance of automated planning. Recent discoveries in automated planning have opened the scope of planners, from toy problems to real-world applications, making new challenges come into focus. The planning community believes that machine learning can assist to address these new challenges. The chapter collects the last machine learning techniques for assisting automated planners classified in: techniques for the improvement of the planning search processes and techniques for the automatic definition of planning action models. For each technique, the chapter provides an in-depth analysis of their domain, advantages and disadvantages. Finally, the chapter draws the outline of the new promising avenues for research in learning for planning systems.

scholarly journals MQTTset, a New Dataset for Machine Learning Techniques on MQTT

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6578
Author(s):  
Ivan Vaccari ◽  
Giovanni Chiola ◽  
Maurizio Aiello ◽  
Maurizio Mongelli ◽  
Enrico Cambiaso
Keyword(s):  
Machine Learning ◽  
Cyber Security ◽  
Detection System ◽  
Critical Issue ◽  
Cyber Attacks ◽  
Machine Learning Techniques ◽  
Detection Systems ◽  
Learning Techniques ◽  
Iot Devices ◽  
Definition Of

IoT networks are increasingly popular nowadays to monitor critical environments of different nature, significantly increasing the amount of data exchanged. Due to the huge number of connected IoT devices, security of such networks and devices is therefore a critical issue. Detection systems assume a crucial role in the cyber-security field: based on innovative algorithms such as machine learning, they are able to identify or predict cyber-attacks, hence to protect the underlying system. Nevertheless, specific datasets are required to train detection models. In this work we present MQTTset, a dataset focused on the MQTT protocol, widely adopted in IoT networks. We present the creation of the dataset, also validating it through the definition of a hypothetical detection system, by combining the legitimate dataset with cyber-attacks against the MQTT network. Obtained results demonstrate how MQTTset can be used to train machine learning models to implement detection systems able to protect IoT contexts.

scholarly journals Machine Learning for the Communication Optimization in Distributed Systems

2018 ◽  
Vol 7 (4.1) ◽  
pp. 47
Author(s):  
Zarina Kazhmaganbetova ◽  
Shnar Imangaliyev ◽  
Altynbek Sharipbay
Keyword(s):  
Machine Learning ◽  
Communication Patterns ◽  
Cloud Services ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Communication Optimization ◽  
Meta Data ◽  
Vast Number ◽  
Distributed Application ◽  
Learning Techniques

The objective of the work that is presented in this paper was the problem of the communication optimization and detection of the issues of computing resources performance degradation [1, 2] with the usage of machine learning techniques. Computer networks transmit payload data and the meta-data from numerous sources towards vast number of destinations, especially in multi-tenant environments [3, 4]. Meta data describes the payload data and could be analyzed for anomalies detection in the communication patterns. Communication patterns depend on the payload itself and technical protocol used. The technical patterns are the research target as their analysis could spotlight the vulnerable behavior, for example: unusual traffic, extra load transported and etc.There was a big data used to train model with a supervised machine learning. Dataset was collected from the network interfaces of the distributed application infrastructure. Machine Learning tools had been retained from the cloud services provider – Amazon Web Services. The stochastic gradient descent technique was utilized for the model training, so that it could represent the communication patterns in the system. The learning target parameter was a packet length, the regression was performed to understand the relationship between packet meta-data (timestamp, protocol, the source server) and its length. The root mean square error calculation was applied to evaluate the learning efficiency. After model was prepared using training dataset, the model was tested with the test dataset and then applied on the target dataset (dataset for prediction) to check whether it was capable to detect anomalies.The experimental part showed the applicability of machine learning for the communication optimization in the distributed application environment. By means of the trained artificial intelligence model, it was possible to predict target parameters of traffic and computing resources usage with purpose to avoid service degradation. Additionally, one could reveal anomalies in the transferred traffic between application components. The application of techniques is envisioned in information security field and in the field of efficient network resources planning.Further research could be in application machine learning techniques for more complicated distributed environments and enlarging the number of protocols to prepare communication patterns.  

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