scholarly journals Mapping Meaning and Purpose in Human-Robot Teams: Anthropomorphic Agents in Military Operations

2021 ◽  
Vol 5 (1) ◽  
pp. 73-94
Author(s):  
Massimiliano L. Cappuccio ◽  
Jai C. Galliott ◽  
Eduardo B. Sandoval
Keyword(s):  
Social Cognition ◽  
Autonomous Agents ◽  
Critical Role ◽  
Autonomous Systems ◽  
Mental States ◽  
Human Robot Interaction ◽  
Military Operations ◽  
Human Machine Interaction ◽  
Robot Teams ◽  
Recent Developments

We spontaneously tend to project animacy and sensitivity to inanimate objects and sometimes we attribute distinctively human features like intelligence, goals, and reasons to certain artificial devices. This phenomenon is called “anthropomorphism” and has been long studied by researchers in human-robot interaction and social-robotics. These studies are particularly important from the perspective of recent developments in military technology, as autonomous systems controlled by AI are expected to play a greater and greater role in the future of warfare. Anthropomorphistic effects can play a critical role in tactical operations involving hybrid human-robot teams, where service members and autonomous agents need to quickly coordinate relying almost exclusively on fast, cognitively parsimonious, natural forms of communication. These forms rely importantly on anthropomorphism to allow human soldiers read the behavior of machines in terms of goals and intentions. Understanding the cognitive mechanisms that underpin anthropomorphistic attributions is hence potentially crucial to increase the accuracy and efficacy of human-machine interaction in military operations. However, this question is largely philosophical, as numerous models compete in the space of social cognition theory to explain behavior reading and mental states attribution. This paper aims to offer an initial exploration of these mechanisms from a perspective of philosophical psychology and cognitive philosophy, reviewing the theories in social cognition that are most promising to explain anthropomorphism and predict how it can enable and improve natural communication between soldiers and autonomous military technologies.

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.

Of Mental States and Machine Learning

2016 ◽  
Vol 60 (1) ◽  
pp. 1362-1366 ◽  
Author(s):  
Andrew Best ◽  
Samantha F. Warta ◽  
Katelynn A. Kapalo ◽  
Stephen M. Fiore
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Social Cognition ◽  
Mental State ◽  
Response Times ◽  
Interdisciplinary Approach ◽  
Mental States ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Machine Learning Classifiers

Using research in social cognition as a foundation, we studied rapid versus reflective mental state attributions and the degree to which machine learning classifiers can be trained to make such judgments. We observed differences in response times between conditions, but did not find significant differences in the accuracy of mental state attributions. We additionally demonstrate how to train machine classifiers to identify mental states. We discuss advantages of using an interdisciplinary approach to understand and improve human-robot interaction and to further the development of social cognition in artificial intelligence.

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.

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 role and relationship of mindreading and social attunement in HRI – position statements of interdisciplinary researchers. Workshop HRI'20

2020 ◽  
Author(s):  
Agnieszka Wykowska ◽  
Jairo Pérez-Osorio ◽  
Stefan Kopp
Keyword(s):  
Mental States ◽  
Human Robot Interaction ◽  
Human Interaction ◽  
Artificial Agents ◽  
The Novel ◽  
Robot Interaction ◽  
Novel Coronavirus ◽  
Relationship Of ◽  
The Relationship

This booklet is a collection of the position statements accepted for the HRI’20 conference workshop “Social Cognition for HRI: Exploring the relationship between mindreading and social attunement in human-robot interaction” (Wykowska, Perez-Osorio & Kopp, 2020). Unfortunately, due to the rapid unfolding of the novel coronavirus at the beginning of the present year, the conference and consequently our workshop, were canceled. On the light of these events, we decided to put together the positions statements accepted for the workshop. The contributions collected in these pages highlight the role of attribution of mental states to artificial agents in human-robot interaction, and precisely the quality and presence of social attunement mechanisms that are known to make human interaction smooth, efficient, and robust. These papers also accentuate the importance of the multidisciplinary approach to advance the understanding of the factors and the consequences of social interactions with artificial agents.

scholarly journals Joint Action without Mutual Beliefs

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Giacomo Figà Talamanca
Keyword(s):  
Social Cognition ◽  
Joint Action ◽  
Common Ground ◽  
Mental States ◽  
Human Beings ◽  
Cooperative Hunting ◽  
Mutual Awareness ◽  
Shared Practice ◽  
State Of Affairs ◽  
Over Time

Abstract Joint action among human beings is characterized by using elaborate cognitive feats, such as representing the mental states of others about a certain state of affairs. It is still debated how these capacities evolved in the hominid lineage. I suggest that the consolidation of a shared practice over time can foster the predictability of other’s behavior. This might facilitate the evolutionary passage from inferring what others might know by simply seeing them and what they are viewing towards a mutual awareness of each other’s beliefs. I will examine the case for cooperative hunting in one chimpanzee community and argue that it is evidence that they have the potential to achieve common ground, suggesting that the consolidation of a practice might have supported the evolution of higher social cognition in the hominid lineage.

scholarly journals From Gaze Perception to Social Cognition: The Shared-Attention System

2021 ◽  
pp. 174569162095377
Author(s):  
Lisa J. Stephenson ◽  
S. Gareth Edwards ◽  
Andrew P. Bayliss
Keyword(s):  
Social Cognition ◽  
Building Blocks ◽  
Mental States ◽  
Evaluation Agency ◽  
Gaze Following ◽  
Gaze Perception ◽  
Shared Attention ◽  
New Findings ◽  
Attention System ◽  
Sas Model

When two people look at the same object in the environment and are aware of each other’s attentional state, they find themselves in a shared-attention episode. This can occur through intentional or incidental signaling and, in either case, causes an exchange of information between the two parties about the environment and each other’s mental states. In this article, we give an overview of what is known about the building blocks of shared attention (gaze perception and joint attention) and focus on bringing to bear new findings on the initiation of shared attention that complement knowledge about gaze following and incorporate new insights from research into the sense of agency. We also present a neurocognitive model, incorporating first-, second-, and third-order social cognitive processes (the shared-attention system, or SAS), building on previous models and approaches. The SAS model aims to encompass perceptual, cognitive, and affective processes that contribute to and follow on from the establishment of shared attention. These processes include fundamental components of social cognition such as reward, affective evaluation, agency, empathy, and theory of mind.

scholarly journals The State of the Art and Prospects for Osteoimmunomodulatory Biomaterials

Materials ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1357
Author(s):  
Andreea-Mariana Negrescu ◽  
Anisoara Cimpean
Keyword(s):  
Foreign Bodies ◽  
Structural Characteristics ◽  
Critical Role ◽  
Fibrous Tissue ◽  
Bioactive Molecules ◽  
New Bone Formation ◽  
Recent Developments

The critical role of the immune system in host defense against foreign bodies and pathogens has been long recognized. With the introduction of a new field of research called osteoimmunology, the crosstalk between the immune and bone-forming cells has been studied more thoroughly, leading to the conclusion that the two systems are intimately connected through various cytokines, signaling molecules, transcription factors and receptors. The host immune reaction triggered by biomaterial implantation determines the in vivo fate of the implant, either in new bone formation or in fibrous tissue encapsulation. The traditional biomaterial design consisted in fabricating inert biomaterials capable of stimulating osteogenesis; however, inconsistencies between the in vitro and in vivo results were reported. This led to a shift in the development of biomaterials towards implants with osteoimmunomodulatory properties. By endowing the orthopedic biomaterials with favorable osteoimmunomodulatory properties, a desired immune response can be triggered in order to obtain a proper bone regeneration process. In this context, various approaches, such as the modification of chemical/structural characteristics or the incorporation of bioactive molecules, have been employed in order to modulate the crosstalk with the immune cells. The current review provides an overview of recent developments in such applied strategies.

scholarly journals Social cognition in anorexia nervosa: Specific difficulties in decoding emotional but not nonemotional mental states

2016 ◽  
Vol 49 (9) ◽  
pp. 883-890 ◽  
Author(s):  
Timo Brockmeyer ◽  
Judith Pellegrino ◽  
Hannah Münch ◽  
Wolfgang Herzog ◽  
Isabell Dziobek ◽  
...  
Keyword(s):  
Anorexia Nervosa ◽  
Social Cognition ◽  
Mental States

scholarly journals Special Issue in Artificial Intelligence

2019 ◽  
Vol 30 (1) ◽  
pp. 7-8
Author(s):  
Dora Maria Ballesteros
Keyword(s):  
Artificial Intelligence ◽  
Communication Networks ◽  
Language Processing ◽  
Autonomous Systems ◽  
Big Data Analytics ◽  
Human Robot Interaction ◽  
Support Vector ◽  
Special Issue ◽  
Science And Engineering ◽  
Robot Interaction

Artificial intelligence (AI) is an interdisciplinary subject in science and engineering that makes it possible for machines to learn from data. Artificial Intelligence applications include prediction, recommendation, classification and recognition, object detection, natural language processing, autonomous systems, among others. The topics of the articles in this special issue include deep learning applied to medicine [1, 3], support vector machine applied to ecosystems [2], human-robot interaction [4], clustering in the identification of anomalous patterns in communication networks [5], expert systems for the simulation of natural disaster scenarios [6], real-time algorithms of artificial intelligence [7] and big data analytics for natural disasters [8].

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