Proceedings of the Workshop on Multimodal Analyses enabling Artificial Agents in Human-Machine Interaction

2016 ◽  
Keyword(s):  
Artificial Agents ◽  
Human Machine Interaction ◽  
Machine Interaction

Attention and Consciousness in Intentional Action: Steps Toward Rich Artificial Agency

2020 ◽  
Vol 07 (01) ◽  
pp. 15-24
Author(s):  
Paul Bello ◽  
Will Bridewell
Keyword(s):  
Cognitive Architecture ◽  
Critical Role ◽  
Intentional Action ◽  
Lessons Learned ◽  
Psychological Explanation ◽  
Cognitive Systems ◽  
Artificial Agents ◽  
Human Machine Interaction ◽  
Machine Interaction ◽  
Insight Into

If artificial agents are to be created such that they occupy space in our social and cultural milieu, then we should expect them to be targets of folk psychological explanation. That is to say, their behavior ought to be explicable in terms of beliefs, desires, obligations, and especially intentions. Herein, we focus on the concept of intentional action, and especially its relationship to consciousness. After outlining some lessons learned from philosophy and psychology that give insight into the structure of intentional action, we find that attention plays a critical role in agency, and indeed, in the production of intentional action. We argue that the insights offered by the literature on agency and intentional action motivate a particular kind of computational cognitive architecture, and one that hasn’t been well-explicated or computationally fleshed out among the community of AI researchers and computational cognitive scientists who work on cognitive systems. To give a sense of what such a system might look like, we present the ARCADIA attention-driven cognitive system as first steps toward an architecture to support the type of agency that rich human–machine interaction will undoubtedly demand.

Designing Artificial Agents as Social Companions

2017 ◽  
Vol 61 (1) ◽  
pp. 1604-1608 ◽  
Author(s):  
Eva Wiese ◽  
Tyler Shaw ◽  
Daniel Lofaro ◽  
Carryl Baldwin
Keyword(s):  
Social Robotics ◽  
Social Connection ◽  
Artificial Agents ◽  
Human Machine Interaction ◽  
Mind Perception ◽  
Social Partners ◽  
Internal States ◽  
And Performance ◽  
Machine Interaction ◽  
Do So

When we interact with others, we make inferences about their internal states (i.e., intentions, emotions) and use this information to understand and predict their behavior. Reasoning about the internal states of others is referred to as mentalizing, and presupposes that our social partners are believed to have a mind. Seeing mind in others increases trust, prosocial behaviors and feelings of social connection, and leads to improved joint performance. However, while human agents trigger mind perception by default, artificial agents are not automatically treated as intentional entities but need to be designed to do so. The panel addresses this issue by discussing how mind attribution to robots and other automated agents can be elicited by design, what the effects of mind perception are on attitudes and performance in human-robot and human-machine interaction and what behavioral and neuroscientific paradigms can be used to investigate these questions. Application areas covered include social robotics, automation, driver-vehicle interfaces, and others.

scholarly journals The Ethical Significance of Human Likeness in Robotics and AI

2019 ◽  
Vol 10 (2) ◽  
pp. 52-67 ◽  
Author(s):  
Peter Remmers
Keyword(s):  
Decision Making ◽  
Ethical Issues ◽  
Human Action ◽  
Artificial Agents ◽  
Human Machine Interaction ◽  
Human Likeness ◽  
Machine Interaction ◽  
Ethical Significance ◽  
And Robotics ◽  
Turing's Test

A defining goal of research in AI and robotics is to build technical artefacts as substitutes, assistants or enhancements of human action and decision-making. But both in reflection on these technologies and in interaction with the respective technical artefacts, we sometimes encounter certain kinds of human likenesses. To clarify their significance, three aspects are highlighted. First, I will broadly investigate some relations between humans and artificial agents by recalling certain points from the debates on Strong AI, on Turing’s Test, on the concept of autonomy and on anthropomorphism in human-machine interaction. Second, I will argue for the claim that there are no serious ethical issues involved in the theoretical aspects of technological human likeness. Third, I will suggest that although human likeness may not be ethically significant on the philosophical and conceptual levels, strategies to use anthropomorphism in the technological design of human-machine collaborations are ethically significant, because artificial agents are specifically designed to be treated in ways we usually treat humans.

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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