Socially Believable Robots

Long-term companionship, emotional attachment and realistic interaction with robots have always been the ultimate sign of technological advancement projected by sci-fi literature and entertainment industry. With the advent of artificial intelligence, we have indeed stepped into an era of socially believable robots or humanoids. Affective computing has enabled the deployment of emotional or social robots to a certain level in social settings like informatics, customer services and health care. Nevertheless, social believability of a robot is communicated through its physical embodiment and natural expressiveness. With each passing year, innovations in chemical and mechanical engineering have facilitated life-like embodiments of robotics; however, still much work is required for developing a “social intelligence” in a robot in order to maintain the illusion of dealing with a real human being. This chapter is a collection of research studies on the modeling of complex autonomous systems. It will further shed light on how different social settings require different levels of social intelligence and what are the implications of integrating a socially and emotionally believable machine in a society driven by behaviors and actions

Humanoid and android robots in the imaginary of adolescents, young adults and seniors

This paper investigates effects of participants’ gender and age (adolescents, young adults, and seniors), robots’ gender (male and female robots) and appearance (humanoid vs android) on robots’ acceptance dimensions. The study involved 6 differently aged groups of participants (two adolescents, two young adults and two seniors’ groups, for a total of 240 participants) requested to express their willingness to interact and their perception of robots’ usefulness, pleasantness, appeal, and engagement for two different sets of females (Pepper, Erica, and Sophia) and male (Romeo, Albert, and Yuri) humanoid and android robots. Participants were also requested to express their preferred and attributed age ranges and occupations they entrusted to robots among healthcare, housework, protection and security and front office. Results show that neither the age nor participants and robots’ gender, nor robots’ human likeness univocally affected robots’ acceptance by these differently aged users. Robots’ acceptance appeared to be a nonlinear combination of all these factors

Virtual Neurorobotics (VNR) to Accelerate Development of Plausible Neuromorphic Brain Architectures

Traditional research in artificial intelligence and machine learning has viewed the brain as a specially adapted information-processing system. More recently the field of social robotics has been advanced to capture the important dynamics of human cognition and interaction. An overarching societal goal of this research is to incorporate the resultant knowledge about intelligence into technology for prosthetic, assistive, security, and decision support applications. However, despite many decades of investment in learning and classification systems, this paradigm has yet to yield truly “intelligent” systems. For this reason, many investigators are now attempting to incorporate more realistic neuromorphic properties into machine learning systems, encouraged by over two decades of neuroscience research that has provided parameters that characterize the brain's interdependent genomic, proteomic, metabolomic, anatomic, and electrophysiological networks. Given the complexity of neural systems, developing tenable models to capture the essence of natural intelligence for real-time application requires that we discriminate features underlying information processing and intrinsic motivation from those reflecting biological constraints (such as maintaining structural integrity and transporting metabolic products). We propose herein a conceptual framework and an iterative method of virtual neurorobotics (VNR) intended to rapidly forward-engineer and test progressively more complex putative neuromorphic brain prototypes for their ability to support intrinsically intelligent, intentional interaction with humans. The VNR system is based on the viewpoint that a truly intelligent system must be driven by emotion rather than programmed tasking, incorporating intrinsic motivation and intentionality. We report pilot results of a closed-loop, real-time interactive VNR system with a spiking neural brain, and provide a video demonstration as online supplemental material

Intelligent Advanced User Interfaces for Monitoring Mental Health Wellbeing

It has become pressing to develop objective and automatic measurements integrated in intelligent diagnostic tools for detecting and monitoring depressive states and enabling an increased precision of diagnoses and clinical decision-makings. The challenge is to exploit behavioral and physiological biomarkers and develop Artificial Intelligent (AI) models able to extract information from a complex combination of signals considered key symptoms. The proposed AI models should be able to help clinicians to rapidly formulate accurate diagnoses and suggest personalized intervention plans ranging from coaching activities (exploiting for example serious games), support networks (via chats, or social networks), and alerts to caregivers, doctors, and care control centers, reducing the considerable burden on national health care institutions in terms of medical, and social costs associated to depression cares

Speech emotion recognition using 2D-convolutional neural network

This research proposes a speech emotion recognition model to predict human emotions using the convolutional neural network (CNN) by learning segmented audio of specific emotions. Speech emotion recognition utilizes the extracted features of audio waves to learn speech emotion characteristics; one of them is mel frequency cepstral coefficient (MFCC). Dataset takes a vital role to obtain valuable results in model learning. Hence this research provides the leverage of dataset combination implementation. The model learns a combined dataset with audio segmentation and zero padding using 2D-CNN. Audio segmentation and zero padding equalize the extracted audio features to learn the characteristics. The model results in 83.69% accuracy to predict seven emotions: neutral, happy, sad, angry, fear, disgust, and surprise from the combined dataset with the segmentation of the audio files

Speech Emotion Recognition Considering Local Dynamic Features

Recently, increasing attention has been directed to the study of the speech emotion recognition, in which global acoustic features of an utterance are mostly used to eliminate the content differences. However, the expression of speech emotion is a dynamic process, which is reflected through dynamic durations, energies, and some other prosodic information when one speaks. In this paper, a novel local dynamic pitch probability distribution feature, which is obtained by drawing the histogram, is proposed to improve the accuracy of speech emotion recognition. Compared with most of the previous works using global features, the proposed method takes advantage of the local dynamic information conveyed by the emotional speech. Several experiments on Berlin Database of Emotional Speech are conducted to verify the effectiveness of the proposed method. The experimental results demonstrate that the local dynamic information obtained with the proposed method is more effective for speech emotion recognition than the traditional global features.Comment: 10 pages, 3 figures, accepted by ISSP 201

Interaction Analysis and Cognitive Infocommunications

Cognitive infocommunications encompasses both scientific and engineering oriented approaches to examining extensions of human cognitive capabilities that may be assimilated within the concept of humanity. Necessary (but not sufficient) conditions for the success of any candidate technology include solving problems within private and public spheres of existence, in thought and communication. Exemplar cognitive infocommunication technologies that have been assimilated in to the concept of humanitiy are examined: emotion, gesture, language. Implications for research programmes conducted within the cognitive infocommunications discipline are outlined