Affective social anthropomorphic intelligent system

Human conversational styles are measured by the sense of humor, personality, and tone of voice. These characteristics have become essential for conversational intelligent virtual assistants. However, most of the state-of-the-art intelligent virtual assistants (IVAs) are failed to interpret the affective semantics of human voices. This research proposes an anthropomorphic intelligent system that can hold a proper human-like conversation with emotion and personality. A voice style transfer method is also proposed to map the attributes of a specific emotion. Initially, the frequency domain data (Mel-Spectrogram) is created by converting the temporal audio wave data, which comprises discrete patterns for audio features such as notes, pitch, rhythm, and melody. A collateral CNN-Transformer-Encoder is used to predict seven different affective states from voice. The voice is also fed parallelly to the deep-speech, an RNN model that generates the text transcription from the spectrogram. Then the transcripted text is transferred to the multi-domain conversation agent using blended skill talk, transformer-based retrieve-and-generate generation strategy, and beam-search decoding, and an appropriate textual response is generated. The system learns an invertible mapping of data to a latent space that can be manipulated and generates a Mel-spectrogram frame based on previous Mel-spectrogram frames to voice synthesize and style transfer. Finally, the waveform is generated using WaveGlow from the spectrogram. The outcomes of the studies we conducted on individual models were auspicious. Furthermore, users who interacted with the system provided positive feedback, demonstrating the system's effectiveness.Comment: Multimedia Tools and Applications (2023

Considerations in designing a cybernetic simple 'learning' model; and an overview of the problem of modelling learning

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Learning is viewed as a central feature of living systems and must be manifested in any artifact that claims to exhibit general intelligence. The central aims of the thesis are twofold: (1) - To review and critically assess the empirical and theoretical aspects of learning as have been addressed in a multitude of disciplines, with the aim of extracting fundamental features and elements. (2) - To develop a more systematic approach to the cybernetic modelling of learning than has been achieved hitherto. In pursuit of aim (1) above the following discussions are included: Historical and Philosophical backgrounds; Natural learning, both physiological and psychological aspects; Hierarchies of learning identified in the evolutionary, functional and developmental senses; An extensive section on the general problem of modelling of learning and the formal tools, is included as a link between aims (1) and (2). Following this a systematic and historically oriented study of cybernetic and other related approaches to the problem of modelling of learning is presented. This then leads to the development of a state-of-the-art general purpose experimental cybernetic learning model. The programming and use of this model is also fully described, including an elaborate scheme for the manifestation of simple learning

Robotic Smart Prosthesis Arm with BCI and Kansei / Kawaii / Affective Engineering Approach. Pt I: Quantum Soft Computing Supremacy

A description of the design stage and results of the development of the conceptual structure of a robotic prosthesis arm is given. As a result, a prototype of manmade prosthesis on a 3D printer as well as a foundation for computational intelligence presented. The application of soft computing technology (the first step of IT) allows to extract knowledge directly from the physical signal of the electroencephalogram, as well as to form knowledge-based intelligent robust control of the lower performing level taking into account the assessment of the patient’s emotional state. The possibilities of applying quantum soft computing technologies (the second step of IT) in the processes of robust filtering of electroencephalogram signals for the formation of mental commands and quantum supremacy simulation of robotic prosthetic arm discussed

Fuzzy Computational Model for Emotion Regulation Based on Affect Control Theory

Emotion modeling is a multi-disciplinary problem that has managed to attract a great deal of research work spanned to a wide spectrum of scholarly areas starting at humanistic science fields passing through applied sciences and engineering and arriving at health care and wellbeing. Emotion research under the umbrella of IT and Computer Science was extensively successful with a handful of achievements especially in the last two decades. Affective Computing is an IT originated systematic research area that strives to best model emotions in a way that fits the needs for computer applications enriched with affective component. A comprehensive Affective Computing based system is made of three major components: a component for emotion detection, a component for emotion modeling, and finally a component to generating affective responses in artificial agents. The major focus of this dissertation is on developing efficient computational models for emotions. In fact most of the research works presented in this dissertation were focused on a sub problem of emotion modeling known as emotion regulation at which we strive to model the dynamics of changes in the emotional response levels of individuals as a result of the overt or covert situational changes. In this dissertation, several emotion related problems were addressed. Modeling the dynamics for emotion elicitation from a pure appraisal approach, investigating individualistic differences in emotional processes, and modeling emotion contagion as a type of social contagion phenomena are a few to name from those conducted research works. The main contribution of this dissertation was to propose a new computational model for the problem of emotion regulation that is based on Affect Control Theory. The new approach utilized a hybrid appraisal-dimensional architecture. By using a fuzzy modeling approach, the natural fuzziness in perceiving, representing and expressing emotions was effectively and efficiently addressed. Furthermore, the combination of automata framework with the concept of bipolar emotional channels used at the heart of the modeling processes of the proposed model has further contributed to promote the behavior of the model in order to exhibit an accepted degree of human-like affective behavior

On certain areas of human factors a literature search

Annotated bibliography on human factors and engineerin

Reinforcement Learning

Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field

Design of Multi Agent Based Crowd Injury Model

A major concern of many government agencies is to predict and control the behavior of crowds in different situations. Many times such gatherings are legal, legitimate, and peaceful. But there are times when they can turn violent, run out of control, result in material damages and even casualties. It then becomes the duty of governments to bring them under control using a variety of techniques, including non-lethal and lethal weapons, if necessary. In order to aid decision makers on the course of action in crowd control, there are modeling and simulation tools that can provide guidelines by giving programmed rules to computer animated characters and to observe behaviors over time in appropriate scenarios. A crowd is a group of people attending a public gathering, with some joint purpose, such as protesting government or celebrating an event. In some countries these kinds of activities are the only way to express public\u27s displeasure with their governments. The governments\u27 reactions to such activities may or may not be tolerant. For these reasons, such situations must be eliminated by recognizing when and how they occur and then providing guidelines to mitigate them. Police or military forces use non-lethal weapons (NLWs), such as plastic bullets or clubs, to accomplish their job. In order to simulate the results of such actions in a computer, there is a need to determine the physical effects of NLWs over the individuals in the crowd. In this dissertation, a fuzzy logic based crowd injury model for determining the physical effects of NLWs is proposed. Fuzzy logic concepts can be applied to a problem by using linguistic rules, which are determined by problem domain experts. In this case, a group of police and military officers were consulted for a set of injury model rules and those rules were then included in the simulation platform. As a proof of concept, a prototype system was implemented using the Repast Simphony agent based simulation toolkit. Simulation results illustrated the effectiveness of the simulation framework