Artificial Intelligence in Maritime Navigation: A Human Factors Perspective

2020 ◽  
pp. 429-435
Author(s):  
Scott N. MacKinnon ◽  
Reto Weber ◽  
Fredrik Olindersson ◽  
Monica Lundh
Keyword(s):  
Artificial Intelligence ◽  
Human Factors ◽  
Maritime Navigation

scholarly journals Assessing autonomous ship navigation using bridge simulators enhanced by cycle-consistent adversarial networks

2021 ◽  
pp. 1748006X2110210
Author(s):  
Andreas Brandsæter ◽  
Ottar L Osen
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Decision Support ◽  
Object Detection ◽  
Autonomous Navigation ◽  
Adversarial Networks ◽  
Maritime Navigation ◽  
Ship Navigation ◽  
Autonomous Ship ◽  
Ship Operation

The advent of artificial intelligence and deep learning has provided sophisticated functionality for sensor fusion and object detection and classification which have accelerated the development of highly automated and autonomous ships as well as decision support systems for maritime navigation. It is, however, challenging to assess how the implementation of these systems affects the safety of ship operation. We propose to utilize marine training simulators to conduct controlled, repeated experiments allowing us to compare and assess how functionality for autonomous navigation and decision support affects navigation performance and safety. However, although marine training simulators are realistic to human navigators, it cannot be assumed that the simulators are sufficiently realistic for testing the object detection and classification functionality, and hence this functionality cannot be directly implemented in the simulators. We propose to overcome this challenge by utilizing Cycle-Consistent Adversarial Networks (Cycle-GANs) to transform the simulator data before object detection and classification is performed. Once object detection and classification are completed, the result is transferred back to the simulator environment. Based on this result, decision support functionality with realistic accuracy and robustness can be presented and autonomous ships can make decisions and navigate in the simulator environment.

Human Factors Considerations for Teaming between Construction Workersand Voice-based Intelligent Virtual Agent (VIVA)

2021 ◽  
Vol 65 (1) ◽  
pp. 72-76
Author(s):  
Maryam Rahimi Movassagh ◽  
Nazila Roofigari-Esfahan ◽  
Sang Won Lee ◽  
Carlos Evia ◽  
David Hicks ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Human Factors ◽  
Research Agenda ◽  
Task Environment ◽  
Virtual Agent ◽  
Work Processes ◽  
Construction Sites

Construction sites experience low productivity due to particular characteristics such as unique designs in each project, sporadic arrival of projects, and complexity of the process. Another contributing factor to low productivity is poor communication among workers, supervisors, and a site’s centralized knowledge hub. Research shows that introducing advanced artificial intelligence (AI) technology in construction can tackle these problems. In this paper, we analyzed human factors considerations–user, task, environment, and technology and identified their characteristics and challenges to design an interactive AI system to facilitate communication between workers and other stakeholders. Based on the analysis, we propose a voice-based intelligent virtual agent (VIVA) as a multi-purpose AI system on construction sites with a further research agenda. We hope that this effort can guide the design of construction-specific AI systems and that this worker-AI teaming can improve overall work processes, enhance productivity, and promote safety in construction.

scholarly journals Human factors challenges for the safe use of artificial intelligence in patient care

2019 ◽  
Vol 26 (1) ◽  
pp. e100081 ◽  
Author(s):  
Mark Sujan ◽  
Dominic Furniss ◽  
Kath Grundy ◽  
Howard Grundy ◽  
David Nelson ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Human Factors ◽  
Patient Care ◽  
Situation Awareness ◽  
Human Performance ◽  
Independent Evaluation ◽  
The Impact ◽  
Human Factors Research

The use of artificial intelligence (AI) in patient care can offer significant benefits. However, there is a lack of independent evaluation considering AI in use. The paper argues that consideration should be given to how AI will be incorporated into clinical processes and services. Human factors challenges that are likely to arise at this level include cognitive aspects (automation bias and human performance), handover and communication between clinicians and AI systems, situation awareness and the impact on the interaction with patients. Human factors research should accompany the development of AI from the outset.

scholarly journals The Wisdom of the Market: Using Human Factors to Design Prediction Markets for Collective Intelligence

2019 ◽  
Vol 63 (1) ◽  
pp. 1471-1475
Author(s):  
Lorenzo Barberis Canonico ◽  
Christopher Flathmann ◽  
Nathan McNeese
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Game Theory ◽  
Human Factors ◽  
Cognitive Science ◽  
Collective Intelligence ◽  
Human Intelligence ◽  
Prediction Markets ◽  
New Model ◽  
Large Groups

There is an ever-growing literature on the power of prediction markets to harness “the wisdom of the crowd” from large groups of people. However, traditional prediction markets are not designed in a human-centered way, often restricting their own potential. This creates the opportunity to implement a cognitive science perspective on how to enhance the collective intelligence of the participants. Thus, we propose a new model for prediction markets that integrates human factors, cognitive science, game theory and machine learning to maximize collective intelligence. We do this by first identifying the connections between prediction markets and collective intelligence, to then use human factors techniques to analyze our design, culminating in the practical ways with which our design enables artificial intelligence to complement human intelligence.

scholarly journals Logical Modeling of Emotions for Ambient Intelligence

2011 ◽  
pp. 108-127 ◽  
Author(s):  
Carole Adam ◽  
Benoit Gaudou ◽  
Dominique Login ◽  
Emiliano Lorini
Keyword(s):  
Artificial Intelligence ◽  
Human Factors ◽  
Ambient Intelligence ◽  
Modal Logics ◽  
Human Reasoning ◽  
Counterfactual Reasoning ◽  
Attribution Of Responsibility ◽  
Logical Approach ◽  
Complex Emotions ◽  
Logical Modeling

Ambient Intelligence (AmI) is the art of designing intelligent and user-focused environments. It is thus of great importance to take human factors into account. In this chapter we especially focus on emotions, that have been proved to be essential in human reasoning and interaction. To this end, we assume that we can take advantage of the results obtained in Artificial Intelligence about the formal modeling of emotions. This chapter specifically aims at showing the interest of logic as a tool to design agents endowed with emotional abilities useful for Ambient Intelligence applications. In particular, we show that modal logics allow the representation of the mental attitudes involved in emotions such as beliefs, goals or ideals. Moreover, we illustrate how modal logics can be used to represent complex emotions (also called self-conscious emotions) involving elaborated forms of reasoning, such as self-attribution of responsibility and counterfactual reasoning. Examples of complex emotions are regret and guilt. We illustrate our logical approach by formalizing some case studies concerning an intelligent house taking care of its inhabitants.

scholarly journals A Preliminary Look at Heuristic Analysis for Assessing Artificial Intelligence Explainability

2020 ◽  
Vol 8 ◽  
pp. 61-72
Author(s):  
Kara Combs ◽  
Mary Fendley ◽  
Trevor Bihl
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Tree ◽  
Human Factors ◽  
Black Box ◽  
Decision Processes ◽  
Decision Tree Classifier ◽  
Tree Classifier ◽  
Explainable Ai ◽  
Heuristic Analysis

Artificial Intelligence and Machine Learning (AI/ML) models are increasingly criticized for their “black-box” nature. Therefore, eXplainable AI (XAI) approaches to extract human-interpretable decision processes from algorithms have been explored. However, XAI research lacks understanding of algorithmic explainability from a human factors’ perspective. This paper presents a repeatable human factors heuristic analysis for XAI with a demonstration on four decision tree classifier algorithms.

Human Factors and Artificial Intelligence – How can we achieve AI for social good (Preprint)

2020 ◽  
Author(s):  
Arlene Oetomo ◽  
Sahan Salim ◽  
Tatiana Bevilacqua ◽  
Kirti Sundar Sahu ◽  
Pedro Elkind Velmovitsky ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Human Factors ◽  
Independent Living ◽  
Relevant Literature ◽  
Technological Advancement ◽  
Human Machine Interaction ◽  
Social Good ◽  
Health And Wellbeing ◽  
Future Work ◽  
Machine Interaction

BACKGROUND Advances in technology will impact the field of human factors as new solutions change how we plan, share knowledge, perceive and act on real world problems. Human machine interaction will become more seamless until we are unable to differentiate between the human and the machine introducing issues of trust and privacy. Technological advancement has created big data and now we are able to tackle large problems with data for better evidence-based solutions and policy measures. OBJECTIVE This paper discusses the use of human factors methods when developing solutions that use artificial intelligence, including machine learning and deep learning, to tackle challenges for social good, especially related to health. METHODS We review relevant literature and present areas and example use cases. RESULTS The potential uses for artificial intelligence applied with human factors are discussed in four areas which impact human health and wellbeing: precision medicine, independent living, public health and the environment. CONCLUSIONS We hope to inspire future work in this field with a better understanding of how human factors can be applied to AI-based solutions. We make the case for the inclusion of HF experts on diverse project teams.

Frontiers of Human-Machine Teaming: How Human Factors Is Contributing

2020 ◽  
Vol 64 (1) ◽  
pp. 259-262
Author(s):  
Michael F. Rayo ◽  
Michael F. Rayo ◽  
Emilie M. Roth ◽  
Alexander M. Morison ◽  
Daniel J. Zelik ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Human Factors ◽  
System Performance ◽  
Design Patterns ◽  
Primary Method ◽  
Critical Importance ◽  
High Complexity ◽  
Design And Development ◽  
High Uncertainty

Although the majority of effort in Artificial Intelligence (AI) ideation, design, and development seeks to optimize the AI as the primary method of optimizing overall system performance, the evidence is clear that for risk-critical work in high-complexity, high-uncertainty settings, it is the interactions between human and machines that must be prioritized. Only be effectively coordinating the available machine and human agents can the system be resilient to an increasing set of system demands. This panel will convey the work that they are doing and obstacles they are facing in the following areas: (1) demonstrating the critical importance of human-machine teaming, (2) hardening design patterns that result in successful human- machine teams, (3) designing and evaluating new automation solutions for their ability to team, and (4) ensuring that new automation solutions are implemented and adopted for risk-critical work.

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