An Overview on Evaluation of E-Learning/Training Response Time Considering Artificial Neural Networks Modeling

The objective of this piece of research is to interpret and investigate systematically an observed brain functional phenomenon which associated with proceeding of e-learning processes. More specifically, this work addresses an interesting and challenging educational issue concerned with dynamical evaluation of e-learning performance considering convergence (response) time. That's based on an interdisciplinary recent approach named as Artificial Neural Networks (ANNs) modeling. Which incorporate Nero-physiology, educational psychology, cognitive, and learning sciences. Herein, adopted application of neural modeling results in realistic dynamical measurements of e-learners' response time performance parameter. Initially, it considers time evolution of learners' experienced acquired intelligence level during proceeding of learning / training process. In the context of neurobiological details, the state of synaptic connectivity pattern (weight vector) inside e-learner's brain-at any time instant-supposed to be presented as timely varying dependent parameter. The varying modified synaptic state expected to lead to obtain stored experience spontaneously as learner's output (answer). Obviously, obtained responsive learner's output is a resulting action to any arbitrary external input stimulus (question). So, as the initial brain state of synaptic connectivity pattern (vector) considered as pre-intelligence level measured parameter. Actually, obtained e-learner’s answer is compatibly consistent with modified state of internal / stored experienced level of intelligence. In other words, dynamical changes of brain synaptic pattern (weight vector) modify adaptively convergence time of learning processes, so as to reach desired answer. Additionally, introduced research work is motivated by some obtained results for performance evaluation of some neural system models concerned with convergence time of learning process. Moreover, this paper considers interpretation of interrelations among some other interesting results obtained by a set of previously published educational models. The interpretational evaluation and analysis for introduced models results in some applicable studies at educational field as well as medically promising treatment of learning disabilities. Finally, an interesting comparative analogy between performances of ANNs modeling versus Ant Colony System (ACS) optimization presented at the end of this paper

Dynamics of embodied dissociated cortical cultures for the control of hybrid biological robots.

The thesis presents a new paradigm for studying the importance of interactions between an organism and its environment using a combination of biology and technology: embodying cultured cortical neurons via robotics. From this platform, explanations of the emergent neural network properties leading to cognition are sought through detailed electrical observation of neural activity. By growing the networks of neurons and glia over multi-electrode arrays (MEA), which can be used to both stimulate and record the activity of multiple neurons in parallel over months, a long-term real-time 2-way communication with the neural network becomes possible. A better understanding of the processes leading to biological cognition can, in turn, facilitate progress in understanding neural pathologies, designing neural prosthetics, and creating fundamentally different types of artificial cognition. Here, methods were first developed to reliably induce and detect neural plasticity using MEAs. This knowledge was then applied to construct sensory-motor mappings and training algorithms that produced adaptive goal-directed behavior. To paraphrase the results, most any stimulation could induce neural plasticity, while the inclusion of temporal and/or spatial information about neural activity was needed to identify plasticity. Interestingly, the plasticity of action potential propagation in axons was observed. This is a notion counter to the dominant theories of neural plasticity that focus on synaptic efficacies and is suggestive of a vast and novel computational mechanism for learning and memory in the brain. Adaptive goal-directed behavior was achieved by using patterned training stimuli, contingent on behavioral performance, to sculpt the network into behaviorally appropriate functional states: network plasticity was not only induced, but could be customized. Clinically, understanding the relationships between electrical stimulation, neural activity, and the functional expression of neural plasticity could assist neuro-rehabilitation and the design of neuroprosthetics. In a broader context, the networks were also embodied with a robotic drawing machine exhibited in galleries throughout the world. This provided a forum to educate the public and critically discuss neuroscience, robotics, neural interfaces, cybernetics, bio-art, and the ethics of biotechnology.Ph.D.Committee Chair: Steve M. Potter; Committee Member: Eric Schumacher; Committee Member: Robert J. Butera; Committee Member: Stephan P. DeWeerth; Committee Member: Thomas D. DeMars

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118 pages, 8 figures, 1 tabl

Computational and Psycho-Physiological Investigations of Musical Emotions

The ability of music to stir human emotions is a well known fact (Gabrielsson & Lindstrom. 2001). However, the manner in which music contributes to those experiences remains obscured. One of the main reasons is the large number of syndromes that characterise emotional experiences. Another is their subjective nature: musical emotions can be affected by memories, individual preferences and attitudes, among other factors (Scherer & Zentner, 2001). But can the same music induce similar affective experiences in all listeners, somehow independently of acculturation or personal bias? A considerable corpus of literature has consistently reported that listeners agree rather strongly about what type of emotion is expressed in a particular piece or even in particular moments or sections (Juslin & Sloboda, 2001). Those studies suggest that music features encode important characteristics of affective experiences, by suggesting the influence of various structural factors of music on emotional expression. Unfortunately, the nature of these relationships is complex, and it is common to find rather vague and contradictory descriptions. This thesis presents a novel methodology to analyse the dynamics of emotional responses to music. It consists of a computational investigation, based on spatiotemporal neural networks sensitive to structural aspects of music, which "mimic" human affective responses to music and permit to predict new ones. The dynamics of emotional responses to music are investigated as computational representations of perceptual processes (psychoacoustic features) and self-perception of physiological activation (peripheral feedback). Modelling and experimental results provide evidence suggesting that spatiotemporal patterns of sound resonate with affective features underlying judgements of subjective feelings. A significant part of the listener's affective response is predicted from the a set of six psychoacoustic features of sound - tempo, loudness, multiplicity (texture), power spectrum centroid (mean pitch), sharpness (timbre) and mean STFT flux (pitch variation) - and one physiological variable - heart rate. This work contributes to new evidence and insights to the study of musical emotions, with particular relevance to the music perception and emotion research communities

Quality Diversity: Harnessing Evolution to Generate a Diversity of High-Performing Solutions

Evolution in nature has designed countless solutions to innumerable interconnected problems, giving birth to the impressive array of complex modern life observed today. Inspired by this success, the practice of evolutionary computation (EC) abstracts evolution artificially as a search operator to find solutions to problems of interest primarily through the adaptive mechanism of survival of the fittest, where stronger candidates are pursued at the expense of weaker ones until a solution of satisfying quality emerges. At the same time, research in open-ended evolution (OEE) draws different lessons from nature, seeking to identify and recreate processes that lead to the type of perpetual innovation and indefinitely increasing complexity observed in natural evolution. New algorithms in EC such as MAP-Elites and Novelty Search with Local Competition harness the toolkit of evolution for a related purpose: finding as many types of good solutions as possible (rather than merely the single best solution). With the field in its infancy, no empirical studies previously existed comparing these so-called quality diversity (QD) algorithms. This dissertation (1) contains the first extensive and methodical effort to compare different approaches to QD (including both existing published approaches as well as some new methods presented for the first time here) and to understand how they operate to help inform better approaches in the future. It also (2) introduces a new technique for encoding neural networks for evolution with indirect encoding that contain multiple sensory or output modalities. Further, it (3) explores the idea that QD can act as an engine of open-ended discovery by introducing an expressive platform called Voxelbuild where QD algorithms continually evolve robots that stack blocks in new ways. A culminating experiment (4) is presented that investigates evolution in Voxelbuild over a very long timescale. This research thus stands to advance the OEE community\u27s desire to create and understand open-ended systems while also laying the groundwork for QD to realize its potential within EC as a means to automatically generate an endless progression of new content in real-world applications

The Survey, Taxonomy, and Future Directions of Trustworthy AI: A Meta Decision of Strategic Decisions

When making strategic decisions, we are often confronted with overwhelming information to process. The situation can be further complicated when some pieces of evidence are contradicted each other or paradoxical. The challenge then becomes how to determine which information is useful and which ones should be eliminated. This process is known as meta-decision. Likewise, when it comes to using Artificial Intelligence (AI) systems for strategic decision-making, placing trust in the AI itself becomes a meta-decision, given that many AI systems are viewed as opaque "black boxes" that process large amounts of data. Trusting an opaque system involves deciding on the level of Trustworthy AI (TAI). We propose a new approach to address this issue by introducing a novel taxonomy or framework of TAI, which encompasses three crucial domains: articulate, authentic, and basic for different levels of trust. To underpin these domains, we create ten dimensions to measure trust: explainability/transparency, fairness/diversity, generalizability, privacy, data governance, safety/robustness, accountability, reproducibility, reliability, and sustainability. We aim to use this taxonomy to conduct a comprehensive survey and explore different TAI approaches from a strategic decision-making perspective

Necessary Conditions for Open-Ended Evolution

Evolution on Earth is widely considered to be an effectively endless process. Though this phenomenon of open-ended evolution (OEE) has been a topic of interest in the artificial life community since its beginnings, the field still lacks an empirically validated theory of what exactly is necessary to reproduce the phenomenon in general (including in domains quite unlike Earth). This dissertation (1) enumerates a set of conditions hypothesized to be necessary for OEE in addition to (2) introducing an artificial life world called Chromaria that incorporates each of the hypothesized necessary conditions. It then (3) describes a set of experiments with Chromaria designed to empirically validate the hypothesized necessary conditions. Thus, this dissertation describes the first scientific endeavor to systematically test an OEE framework in an alife world and thereby make progress towards solving an open question not just for evolutionary computation and artificial life, but for science in general