Affective Communication for Socially Assistive Robots (SARs) for Children with Autism Spectrum Disorder: A Systematic Review

Research on affective communication for socially assistive robots has been conducted to enable physical robots to perceive, express, and respond emotionally. However, the use of affective computing in social robots has been limited, especially when social robots are designed for children, and especially those with autism spectrum disorder (ASD). Social robots are based on cognitiveaffective models, which allow them to communicate with people following social behaviors and rules. However, interactions between a child and a robot may change or be different compared to those with an adult or when the child has an emotional deficit. In this study, we systematically reviewed studies related to computational models of emotions for children with ASD. We used the Scopus, WoS, Springer, and IEEE-Xplore databases to answer different research questions related to the definition, interaction, and design of computational models supported by theoretical psychology approaches from 1997 to 2021. Our review found 46 articles; not all the studies considered children or those with ASD.This research was funded by VRIEA-PUCV, grant number 039.358/202

Test moment determination design in active robot learning

A thesis submitted to the University of Bedfordshire, in fulfilment of the requirements for the degree of Master of Science by researchIn recent years, service robots have been increasingly used in people's daily live. These robots are autonomous or semiautonomous and are able to cooperate with their human users. Active robot learning (ARL) is an approach to the development of beliefs for the robots on their users' intention and preference, which is needed by the robots to facilitate the seamless cooperation with humans. This approach allows a robot to perform tests on its users and to build up the high-order beliefs according to the users' responses. This study carried out primary research on designing the test moment determination component in ARL framework. The test moment determination component is used to decide right moment of taking a test action. In this study, an action plan theory was suggested to synthesis actions into a sequence, that is, an action plan, for a given task. All actions are defined in a special format of precondition, action, post-condition and testing time. Forward chaining reasoning was introduced to establish connection between the actions and to synthesis individual actions into an action plan, corresponding to the given task. A simulation environment was set up where a human user and a service robot were modelled using MATLAB. Fuzzy control was employed for controlling the robot to carry out the cooperative action. In order to examine the effect of test moment determination component, simulations were performed to execute a scenario where a robot passes on an object to a human user. The simulation results show that an action plan can be formed according to provided conditions and executed by simulated models properly. Test actions were taken at the moment determined by the test moment determination component to find the human user's intention

Humans and Representations of AI in (Science) Fiction

In this dissertation, I am interested in the concept of SF and the representation of AI in two classical SF novels and two contemporary texts which will be referred to as rather-SF. The primary text used are Isaac Asimov’s The Naked Sun, Philip K. Dick’s Do Androids Dream of Electric Sheep?, Ian McEwan’s Machines Like Me and Kazuo Ishiguro’s Klara and the Sun. SF is seen as having a material and a metaphorical component, while the umbrella-term AI is seen to be embodied as robot and android, or more generally as human-like artificial entity. Depending on whether the AI figures appear in a classical SF text or in a rather-SF text, the meaning of the novum shifts. In the first case, the AI figure is a material embodiment of a technology-driven future, while in the second case, it is an artificial companion for humans, living in the same space and having its own understanding of language, emotion and art. In both cases nevertheless, a relationship between human and AI is constructed, and, even if it is the human who creates the AI, it is the AI who plays a central role in the reshaping of human character and behaviour, even of the human self

Artificial Intelligence and Ambient Intelligence

This book includes a series of scientific papers published in the Special Issue on Artificial Intelligence and Ambient Intelligence at the journal Electronics MDPI. The book starts with an opinion paper on “Relations between Electronics, Artificial Intelligence and Information Society through Information Society Rules”, presenting relations between information society, electronics and artificial intelligence mainly through twenty-four IS laws. After that, the book continues with a series of technical papers that present applications of Artificial Intelligence and Ambient Intelligence in a variety of fields including affective computing, privacy and security in smart environments, and robotics. More specifically, the first part presents usage of Artificial Intelligence (AI) methods in combination with wearable devices (e.g., smartphones and wristbands) for recognizing human psychological states (e.g., emotions and cognitive load). The second part presents usage of AI methods in combination with laser sensors or Wi-Fi signals for improving security in smart buildings by identifying and counting the number of visitors. The last part presents usage of AI methods in robotics for improving robots’ ability for object gripping manipulation and perception. The language of the book is rather technical, thus the intended audience are scientists and researchers who have at least some basic knowledge in computer science

Autonomous Decision-Making based on Biological Adaptive Processes for Intelligent Social Robots

Mención Internacional en el título de doctorThe unceasing development of autonomous robots in many different scenarios drives a new revolution to improve our quality of life. Recent advances in human-robot interaction and machine learning extend robots to social scenarios, where these systems pretend to assist humans in diverse tasks. Thus, social robots are nowadays becoming real in many applications like education, healthcare, entertainment, or assistance. Complex environments demand that social robots present adaptive mechanisms to overcome different situations and successfully execute their tasks. Thus, considering the previous ideas, making autonomous and appropriate decisions is essential to exhibit reasonable behaviour and operate well in dynamic scenarios. Decision-making systems provide artificial agents with the capacity of making decisions about how to behave depending on input information from the environment. In the last decades, human decision-making has served researchers as an inspiration to endow robots with similar deliberation. Especially in social robotics, where people expect to interact with machines with human-like capabilities, biologically inspired decisionmaking systems have demonstrated great potential and interest. Thereby, it is expected that these systems will continue providing a solid biological background and improve the naturalness of the human-robot interaction, usability, and the acceptance of social robots in the following years. This thesis presents a decision-making system for social robots acting in healthcare, entertainment, and assistance with autonomous behaviour. The system’s goal is to provide robots with natural and fluid human-robot interaction during the realisation of their tasks. The decision-making system integrates into an already existing software architecture with different modules that manage human-robot interaction, perception, or expressiveness. Inside this architecture, the decision-making system decides which behaviour the robot has to execute after evaluating information received from different modules in the architecture. These modules provide structured data about planned activities, perceptions, and artificial biological processes that evolve with time that are the basis for natural behaviour. The natural behaviour of the robot comes from the evolution of biological variables that emulate biological processes occurring in humans. We also propose a Motivational model, a module that emulates biological processes in humans for generating an artificial physiological and psychological state that influences the robot’s decision-making. These processes emulate the natural biological rhythms of the human organism to produce biologically inspired decisions that improve the naturalness exhibited by the robot during human-robot interactions. The robot’s decisions also depend on what the robot perceives from the environment, planned events listed in the robot’s agenda, and the unique features of the user interacting with the robot. The robot’s decisions depend on many internal and external factors that influence how the robot behaves. Users are the most critical stimuli the robot perceives since they are the cornerstone of interaction. Social robots have to focus on assisting people in their daily tasks, considering that each person has different features and preferences. Thus, a robot devised for social interaction has to adapt its decisions to people that aim at interacting with it. The first step towards adapting to different users is identifying the user it interacts with. Then, it has to gather as much information as possible and personalise the interaction. The information about each user has to be actively updated if necessary since outdated information may lead the user to refuse the robot. Considering these facts, this work tackles the user adaptation in three different ways. • The robot incorporates user profiling methods to continuously gather information from the user using direct and indirect feedback methods. • The robot has a Preference Learning System that predicts and adjusts the user’s preferences to the robot’s activities during the interaction. • An Action-based Learning System grounded on Reinforcement Learning is introduced as the origin of motivated behaviour. The functionalities mentioned above define the inputs received by the decisionmaking system for adapting its behaviour. Our decision-making system has been designed for being integrated into different robotic platforms due to its flexibility and modularity. Finally, we carried out several experiments to evaluate the architecture’s functionalities during real human-robot interaction scenarios. In these experiments, we assessed: • How to endow social robots with adaptive affective mechanisms to overcome interaction limitations. • Active user profiling using face recognition and human-robot interaction. • A Preference Learning System we designed to predict and adapt the user preferences towards the robot’s entertainment activities for adapting the interaction. • A Behaviour-based Reinforcement Learning System that allows the robot to learn the effects of its actions to behave appropriately in each situation. • The biologically inspired robot behaviour using emulated biological processes and how the robot creates social bonds with each user. • The robot’s expressiveness in affect (emotion and mood) and autonomic functions such as heart rate or blinking frequency.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Richard J. Duro Fernández.- Secretaria: Concepción Alicia Monje Micharet.- Vocal: Silvia Ross

Artificial Intelligence & Popular Music: SKYGGE, Flow Machines, and the Audio Uncanny Valley

This article presents an overview of the first AI-human collaborated album, Hello World, by SKYGGE, which utilizes Sony’s Flow Machines technologies. This case study is situated within a review of current and emerging uses of AI in popular music production, and connects those uses with myths and fears that have circulated in discourses concerning the use of AI in general, and how these fears connect to the idea of an audio uncanny valley. By proposing the concept of an audio uncanny valley in relation to AIPM (artificial intelligence popular music), this article offers a lens through which to examine the more novel and unusual melodies and harmonization made possible through AI music generation, and questions how this content relates to wider speculations about posthumanism, sincerity, and authenticity in both popular music, and broader assumptions of anthropocentric creativity. In its documentation of the emergence of a new era of popular music, the AI era, this article surveys: (1) The current landscape of artificial intelligence popular music focusing on the use of Markov models for generative purposes; (2) posthumanist creativity and the potential for an audio uncanny valley; and (3) issues of perceived authenticity in the technologically mediated “voice”