Attribution Errors by People and Intelligent Machines

2021 ◽  
pp. 001872082110363
Author(s):  
P. A. Hancock ◽  
John D. Lee ◽  
John W. Senders
Keyword(s):  
Autonomous Systems ◽  
Human Machine Interaction ◽  
Vehicle Collisions ◽  
Intelligent Machines ◽  
Machine Interaction ◽  
Prospective Analyses

Objective To explore the ramifications of attribution errors (AEs), initially in the context of vehicle collisions and then to extend this understanding into the broader and diverse realms of all forms of human–machine interaction. Background This work focuses upon a particular topic that John Senders was examining at the time of his death. He was using the lens of attribution, and its associated errors, to seek to further understand and explore dyadic forms of driver collision. Method We evaluated the utility of the set of Senders’ final observations on conjoint AE in two-vehicle collisions. We extended this evaluation to errors of attribution generally, as applicable to all human–human, human–technology, and prospectively technology–technology interactions. Results As with Senders and his many other contributions, we find evident value in this perspective on how humans react to each other and how they react to emerging forms of technology, such as autonomous systems. We illustrate this value through contemporary examples and prospective analyses. Applications The comprehension and mitigation of AEs can help improve all interactions between people, between intelligent machines and between humans and the machines they work with.

scholarly journals Turn-Taking and Coordination in Human-Machine Interaction

AI Magazine ◽  
2017 ◽  
Vol 37 (4) ◽  
pp. 5-6 ◽  
Author(s):  
Sean Andrist ◽  
Dan Bohus ◽  
Bilge Mutlu ◽  
David Schlangen
Keyword(s):  
Autonomous Systems ◽  
Research Area ◽  
Human Robot Interaction ◽  
Research Progress ◽  
Virtual Agents ◽  
Human Machine Interaction ◽  
Dialog Systems ◽  
Turn Taking ◽  
Core Concepts ◽  
Machine Interaction

This issue of AI Magazine brings together a collection of articles on challenges, mechanisms, and research progress in turn-taking and coordination between humans and machines. The contributing authors work in interrelated fields of spoken dialog systems, intelligent virtual agents, human-computer interaction, human-robot interaction, and semiautonomous collaborative systems and explore core concepts in coordinating speech and actions with virtual agents, robots, and other autonomous systems. Several of the contributors participated in the AAAI Spring Symposium on Turn-Taking and Coordination in Human-Machine Interaction, held in March 2015, and several articles in this issue are extensions of work presented at that symposium. The articles in the collection address key modeling, methodological, and computational challenges in achieving effective coordination with machines, propose solutions that overcome these challenges under sensory, cognitive, and resource restrictions, and illustrate how such solutions can facilitate coordination across diverse and challenging domains. The contributions highlight turn-taking and coordination in human-machine interaction as an emerging and evolving research area with important implications for future applications of AI.

Achieving an Information System's Capability through C2

2015 ◽  
Vol 7 (1) ◽  
pp. 80-96
Author(s):  
Ana C. Calderon ◽  
Peter Johnson
Keyword(s):  
Autonomous Agents ◽  
Autonomous Systems ◽  
Academic Research ◽  
Command And Control ◽  
Human Machine Interaction ◽  
Military Research ◽  
Task Modeling ◽  
General Aspects ◽  
And Control ◽  
Machine Interaction

The authors present a literature review of command and control, linking sociological elements of academic research to military research in a novel way. They will discuss task modeling literature (seen in human machine interaction studies), general aspects of collectives and military and academic research on command and control, studies of autonomous systems and considerations of interactions between humans and autonomous agents. Based on the survey and associations between aspects from these fields, the authors compose a recommendation list for aspects crucial to building of information systems capable of achieving their true capability, through command and control.

Tomorrow’s Human–Machine Design Tools: From Levels of Automation to Interdependencies

2017 ◽  
Vol 12 (1) ◽  
pp. 77-82 ◽  
Author(s):  
Matthew Johnson ◽  
Jeffrey M. Bradshaw ◽  
Paul J. Feltovich
Keyword(s):  
Sociotechnical Systems ◽  
Viable Alternative ◽  
Machine Design ◽  
Design Tools ◽  
Human Machine Interaction ◽  
Intelligent Machines ◽  
Levels Of Automation ◽  
Machine Systems ◽  
Machine Interaction

The growth of sophistication in machine capabilities must go hand in hand with growth of sophistication in human–machine interaction capabilities. To continue advancement as we build today’s intelligent machines, designers need formative tools for creating sociotechnical systems. In this article, we will briefly assess the appropriateness of “levels of automation” as a tool for designing human–machine systems. Additionally, we present coactive design and interdependence analysis as a viable alternative tool moving forward into more advanced and sophisticated human–machine systems.

scholarly journals Towards the realization of dynamically adaptable manufacturing automation systems

2021 ◽  
Vol 379 (2207) ◽  
pp. 20200365
Author(s):  
Robert Harrison ◽  
Daniel Vera ◽  
Bilal Ahmad
Keyword(s):  
Autonomous Systems ◽  
Smart Manufacturing ◽  
Global Knowledge ◽  
Human Machine Interaction ◽  
Manufacturing Automation ◽  
Automation Systems ◽  
Operational Information ◽  
High Degree ◽  
Machine Interaction

The transition from traditional to truly smart dynamically adaptable manufacturing demands the adoption of a high degree of autonomy within automation systems, with resultant changes in the role of the human, in both the manufacturing and logistics functions within the factory. In the context of smart manufacturing, this paper describes research towards the realization of adaptable autonomous automation systems from both the control and information perspectives. Key facets of the approach taken at WMG are described in relation to human–machine interaction, autonomous approaches to assembly and intra-logistics, integration and dynamic system-wide optimization. The progression from simple distributed behavioural components towards autonomous functional entities is described. Effective systems integration and the importance of interoperability in the realization of more distributed and autonomous automation systems are discussed, so that operational information can propagate seamlessly, eliminating the traditional boundary between operational technology and information technology systems, and as an enabler for global knowledge collection, analysis and optimization. This article is part of the theme issue ‘Towards symbiotic autonomous systems'.

scholarly journals eHMI: Review and Guidelines for Deployment on Autonomous Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2912
Author(s):  
Juan Carmona ◽  
Carlos Guindel ◽  
Fernando Garcia ◽  
Arturo de la Escalera
Keyword(s):  
Autonomous Vehicles ◽  
Rapid Development ◽  
Autonomous Systems ◽  
Current Status ◽  
Future Research ◽  
Human Machine Interaction ◽  
Broad Perspective ◽  
Recent Developments ◽  
Feedback Data ◽  
Machine Interaction

Human–machine interaction is an active area of research due to the rapid development of autonomous systems and the need for communication. This review provides further insight into the specific issue of the information flow between pedestrians and automated vehicles by evaluating recent advances in external human–machine interfaces (eHMI), which enable the transmission of state and intent information from the vehicle to the rest of the traffic participants. Recent developments will be explored and studies analyzing their effectiveness based on pedestrian feedback data will be presented and contextualized. As a result, we aim to draw a broad perspective on the current status and recent techniques for eHMI and some guidelines that will encourage future research and development of these systems.

THE CONCEPT OF AUTONOMOUS SYSTEMS IN INDUSTRY 4.0

2019 ◽  
Vol 12 (1) ◽  
pp. 77-87
Author(s):  
György Kovács ◽  
Rabab Benotsmane ◽  
László Dudás
Keyword(s):  
Industry 4.0 ◽  
Intelligent Systems ◽  
Autonomous Systems ◽  
Essential Element ◽  
Industrial Sector ◽  
Life Cycles ◽  
Multi Agent Systems ◽  
New Paradigm ◽  
Intelligent Machines ◽  
Digital Network

Recent tendencies – such as the life-cycles of products are shorter while consumers require more complex and more unique final products – poses many challenges to the production. The industrial sector is going through a paradigm shift. The traditional centrally controlled production processes will be replaced by decentralized control, which is built on the self-regulating ability of intelligent machines, products and workpieces that communicate with each other continuously. This new paradigm known as Industry 4.0. This conception is the introduction of digital network-linked intelligent systems, in which machines and products will communicate to one another in order to establish smart factories in which self-regulating production will be established. In this article, at first the essence, main goals and basic elements of Industry 4.0 conception is described. After it the autonomous systems are introduced which are based on multi agent systems. These systems include the collaborating robots via artificial intelligence which is an essential element of Industry 4.0.

Concise CNN model for face expression recognition

2021 ◽  
pp. 1-9
Author(s):  
Harshadkumar B. Prajapati ◽  
Ankit S. Vyas ◽  
Vipul K. Dabhi
Keyword(s):  
Network Architecture ◽  
Expression Recognition ◽  
Human Machine Interaction ◽  
Face Expression ◽  
Computational Overhead ◽  
Average Accuracy ◽  
Proposed Model ◽  
Face Expression Recognition ◽  
Expression Classification ◽  
Machine Interaction

Face expression recognition (FER) has gained very much attraction to researchers in the field of computer vision because of its major usefulness in security, robotics, and HMI (Human-Machine Interaction) systems. We propose a CNN (Convolutional Neural Network) architecture to address FER. To show the effectiveness of the proposed model, we evaluate the performance of the model on JAFFE dataset. We derive a concise CNN architecture to address the issue of expression classification. Objective of various experiments is to achieve convincing performance by reducing computational overhead. The proposed CNN model is very compact as compared to other state-of-the-art models. We could achieve highest accuracy of 97.10% and average accuracy of 90.43% for top 10 best runs without any pre-processing methods applied, which justifies the effectiveness of our model. Furthermore, we have also included visualization of CNN layers to observe the learning of CNN.

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