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Value and Reward Based Learning in Neurobots

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889194315 Year: Pages: 158 DOI: 10.3389/978-2-88919-431-5 Language: English
Publisher: Frontiers Media SA
Subject: Neurology --- Science (General)
Added to DOAB on : 2016-01-19 14:05:46
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Organisms are equipped with value systems that signal the salience of environmental cues to their nervous system, causing a change in the nervous system that results in modification of their behavior. These systems are necessary for an organism to adapt its behavior when an important environmental event occurs. A value system constitutes a basic assumption of what is good and bad for an agent. These value systems have been effectively used in robotic systems to shape behavior. For example, many robots have used models of the dopaminergic system to reinforce behavior that leads to rewards. Other modulatory systems that shape behavior are acetylcholine’s effect on attention, norepinephrine’s effect on vigilance, and serotonin’s effect on impulsiveness, mood, and risk. Moreover, hormonal systems such as oxytocin and its effect on trust constitute as a value system. This book presents current research involving neurobiologically inspired robots whose behavior is: 1) Shaped by value and reward learning, 2) adapted through interaction with the environment, and 3) shaped by extracting value from the environment.

Intelligent Business Process Optimization for the Service Industry

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ISBN: 9783866444546 Year: Pages: 310 p. DOI: 10.5445/KSP/1000014466 Language: ENGLISH
Publisher: KIT Scientific Publishing
Subject: Business and Management
Added to DOAB on : 2019-07-30 20:01:57
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The company's sustainable competitive advantage derives from its capacity to create value for customers and to adapt the operational practices to changing situations. Business processes are the heart of each company. Therefore process excellence has become a key issue. This book introduces a novel approach focusing on the autonomous optimization of business processes by applying sophisticated machine learning techniques such as Relational Reinforcement Learning and Particle Swarm Optimization.

Intrinsic motivations and open-ended development in animals, humans, and robots

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889193721 Year: Pages: 350 DOI: 10.3389/978-2-88919-372-1 Language: English
Publisher: Frontiers Media SA
Subject: Neurology --- Science (General) --- Psychology
Added to DOAB on : 2015-11-19 16:29:12
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The aim of this Research Topic for Frontiers in Psychology under the section of Cognitive Science and Frontiers in Neurorobotics is to present state-of-the-art research, whether theoretical, empirical, or computational investigations, on open-ended development driven by intrinsic motivations. The topic will address questions such as: How do motivations drive learning? How are complex skills built up from a foundation of simpler competencies? What are the neural and computational bases for intrinsically motivated learning? What is the contribution of intrinsic motivations to wider cognition? Autonomous development and lifelong open-ended learning are hallmarks of intelligence. Higher mammals, and especially humans, engage in activities that do not appear to directly serve the goals of survival, reproduction, or material advantage. Rather, a large part of their activity is intrinsically motivated - behavior driven by curiosity, play, interest in novel stimuli and surprising events, autonomous goal-setting, and the pleasure of acquiring new competencies. This allows the cumulative acquisition of knowledge and skills that can later be used to accomplish fitness-enhancing goals. Intrinsic motivations continue during adulthood, and in humans artistic creativity, scientific discovery, and subjective well-being owe much to them. The study of intrinsically motivated behavior has a long history in psychological and ethological research, which is now being reinvigorated by perspectives from neuroscience, artificial intelligence and computer science. For example, recent neuroscientific research is discovering how neuromodulators like dopamine and noradrenaline relate not only to extrinsic rewards but also to novel and surprising events, how brain areas such as the superior colliculus and the hippocampus are involved in the perception and processing of events, novel stimuli, and novel associations of stimuli, and how violations of predictions and expectations influence learning and motivation. Computational approaches are characterizing the space of possible reinforcement learning algorithms and their augmentation by intrinsic reinforcements of different kinds. Research in robotics and machine learning is yielding systems with increasing autonomy and capacity for self-improvement: artificial systems with motivations that are similar to those of real organisms and support prolonged autonomous learning. Computational research on intrinsic motivation is being complemented by, and closely interacting with, research that aims to build hierarchical architectures capable of acquiring, storing, and exploiting the knowledge and skills acquired through intrinsically motivated learning. Now is an important moment in the study of intrinsically motivated open-ended development, requiring contributions and integration across a large number of fields within the cognitive sciences. This Research Topic aims to contribute to this effort by welcoming papers carried out with ethological, psychological, neuroscientific and computational approaches, as well as research that cuts across disciplines and approaches.

Oxytocin's routes in social behavior: Into the 21st century

Authors: --- ---
Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889196968 Year: Pages: 132 DOI: 10.3389/978-2-88919-696-8 Language: English
Publisher: Frontiers Media SA
Subject: Neurology --- Science (General)
Added to DOAB on : 2016-04-07 11:22:02
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Our brain is endowed with an incredible capacity to be social, to trust, to cooperate, to be altruistic, to feel empathy and love. Nevertheless, the biological underpinnings of such behaviors remain partially hardwired. Seminal research in rodents has provided important insights on the identification of specific genes in modulating social behaviors, in particular, the arginine vasopressin receptor and the oxytocin receptor genes. These genes are involved in regulating a wide range of social behaviors, mother-infant interactions, social recognition, aggression and socio-sexual behavior. Remarkably, we now know that these genes contribute to social behavior in a broad range of species from voles to humans. Indeed, advances in human non-invasive neuroimaging techniques and genetics have enabled scientists to begin to elucidate the neurobiological basis of the complexity of human social behaviors using "pharmacological fMRI" and "imaging genetics". Over the past few years, there has been a strong interest focused on the role of oxytocin in modulating human social behaviors with translational relevance for understanding neuropsychiatric disorders, such as autism, schizophrenia and depression, in which deficits in social perception and social recognition are key phenotypes. The convergence of this interdisciplinary research is beginning to reveal the complex nature of oxytocin’s actions. For instance, the way that oxytocin does influence social functioning is highly related to individual differences in social experiences, but also to the inter-individual variability in the receptor distribution of this molecule in the brain. Remarkably, despite the increasing evidence that oxytocin has a key role in regulating human social behavior, we still lack of knowledge on the core mechanisms of action of this molecule. Understanding its fundamental actions is a crucial need in order to target optimal therapeutic strategies for human social disorders. The originality of this Research Topic stands on its translational focus on bridging the gap between fundamental knowledge acquired from oxytocin research in voles and monkeys and recent clinical investigations in humans. For instance, what are the key animal findings that can import further knowledge on the mechanisms of actions of this molecule in humans? What are the key experiences that can be performed in the animal model in order to answer significant science gaps in the treatment of neuropsychiatric disorders? Hence, within this Research Topic, we will review the current state of the field, identify where the gaps in knowledge are, and propose directions for future research. This issue will begin with a comparative review that examines the role of this peptide in diverse animal models, which highlights the adaptive value of oxytocin’s function across multiple species. Then, a series of reviews will examine the role of oxytocin in voles, primates, and humans with an eye toward revealing commonalities in the underlying brain circuits mediating oxytocin’s effects on social behavior. Next, there will be a translational review highlighting the evidence for oxytocin’s role in clinical applications in psychopathology. Hence, via the continuum of basic to translational research areas, we will try to address the important gaps in our understanding of the neurobiological routes of social cognition and the mechanisms of action of the neuropeptides that guide our behaviors and decisions.

Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources

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Book Series: Karlsruher Forschungsberichte aus dem Institut für Hochleistungsimpuls- und Mikrowellentechnik ISSN: 21922764 ISBN: 9783731504672 Year: Volume: 8 Pages: XIII, 231 p. DOI: 10.5445/KSP/1000051503 Language: ENGLISH
Publisher: KIT Scientific Publishing
Subject: Technology (General)
Added to DOAB on : 2019-07-30 20:02:02
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In this work, an innovative real-time microwave control approach is proposed, to improve the temperature homogeneity under microwave heating. Multiple adaptive or intelligent control structures have been developed, including the model predictive control, neural network control and reinforcement learning control methods. Experimental results prove that these advanced control methods can effectively reduce the final temperature derivations and improve the temperature homogeneity.

Elements of Causal Inference

Authors: --- ---
Book Series: Adaptive Computation and Machine Learning series ISBN: 9780262344296 9780262037310 Year: Pages: 288 Language: English
Publisher: The MIT Press
Subject: Computer Science
Added to DOAB on : 2019-01-17 11:41:31
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A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning.The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data. After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem. The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts.

Autonomous Control of Unmanned Aerial Vehicles

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ISBN: 9783039210305 / 9783039210312 Year: Pages: 270 DOI: 10.3390/books978-3-03921-031-2 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-06-26 08:44:06
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Unmanned aerial vehicles (UAVs) are being increasingly used in different applications in both military and civilian domains. These applications include surveillance, reconnaissance, remote sensing, target acquisition, border patrol, infrastructure monitoring, aerial imaging, industrial inspection, and emergency medical aid. Vehicles that can be considered autonomous must be able to make decisions and react to events without direct intervention by humans. Although some UAVs are able to perform increasingly complex autonomous manoeuvres, most UAVs are not fully autonomous; instead, they are mostly operated remotely by humans. To make UAVs fully autonomous, many technological and algorithmic developments are still required. For instance, UAVs will need to improve their sensing of obstacles and subsequent avoidance. This becomes particularly important as autonomous UAVs start to operate in civilian airspaces that are occupied by other aircraft. The aim of this volume is to bring together the work of leading researchers and practitioners in the field of unmanned aerial vehicles with a common interest in their autonomy. The contributions that are part of this volume present key challenges associated with the autonomous control of unmanned aerial vehicles, and propose solution methodologies to address such challenges, analyse the proposed methodologies, and evaluate their performance.

Plug-in Hybrid Electric Vehicle (PHEV)

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ISBN: 9783039214532 / 9783039214549 Year: Pages: 230 DOI: 10.3390/books978-3-03921-454-9 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-12-09 11:49:15
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Climate change, urban air quality, and dependency on crude oil are important societal challenges. In the transportation sector especially, clean and energy efficient technologies must be developed. Electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs) have gained a growing interest in the vehicle industry. Nowadays, the commercialization of EVs and PHEVs has been possible in different applications (i.e., light duty, medium duty, and heavy duty vehicles) thanks to the advances in energy storage systems, power electronics converters (including DC/DC converters, DC/AC inverters, and battery charging systems), electric machines, and energy efficient power flow control strategies. This book is based on the Special Issue of the journal Applied Sciences on “Plug-In Hybrid Electric Vehicles (PHEVs)”. This collection of research articles includes topics such as novel propulsion systems, emerging power electronics and their control algorithms, emerging electric machines and control techniques, energy storage systems, including BMS, and efficient energy management strategies for hybrid propulsion, vehicle-to-grid (V2G), vehicle-to-home (V2H), grid-to-vehicle (G2V) technologies, and wireless power transfer (WPT) systems.

Keywords

battery power --- convex optimization --- dynamic programming --- engine-on power --- plug-in hybrid electric vehicle --- simulated annealing --- electric vehicle --- open-end winding --- dual inverter --- voltage vector distribution --- power sharing --- energy management --- range-extender --- CO2 --- air quality --- mobility needs --- LCA --- Paris Agreement --- hybrid energy storage system --- lithium-ion battery --- lithium-ion capacitor --- lifetime model --- power distribution --- state of health estimation --- adaptive neuron-fuzzy inference system (ANFIS) --- group method of data handling (GMDH) --- artificial neural network (ANN) --- electric vehicles (EVs) --- capacity degradation --- lithium-ion battery --- time-delay input --- interleaved multiport converte --- multi-objective genetic algorithm --- hybrid electric vehicles --- losses model --- wide bandgap (WBG) technologies --- Energy Storage systems --- LCA --- Well-to-Wheel --- electric vehicle --- plug-in hybrid --- electricity mix --- consequential --- attributional --- marginal --- system modelling --- energy system --- meta-analysis --- parallel hybrid electric vehicle --- regenerative braking --- fuel consumption characteristics --- energy efficiency --- state of charge --- lithium polymer battery --- electric vehicle --- Plugin Hybrid electric vehicle --- Li-ion battery --- modelling --- measurements --- state of charge --- strong track filter --- modified one-state hysteresis model --- Li(Ni1/3Co1/3Mn1/3)O2 battery --- energy management strategy --- Markov decision process (MDP) --- plug-in hybrid electric vehicles (PHEVs) --- Q-learning (QL) --- reinforcement learning (RL) --- novel propulsion systems --- emerging power electronics --- including wide bandgap (WBG) technology --- emerging electric machines --- efficient energy management strategies for hybrid propulsion systems --- energy storage systems --- life-cycle assessment (LCA)

Control and Nonlinear Dynamics on Energy Conversion Systems

Authors: ---
ISBN: 9783039211104 / 9783039211111 Year: Pages: 438 DOI: 10.3390/books978-3-03921-111-1 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-08-28 11:21:27
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The ever-increasing need for higher efficiency, smaller size, and lower cost make the analysis, understanding, and design of energy conversion systems extremely important, interesting, and even imperative. One of the most neglected features in the study of such systems is the effect of the inherent nonlinearities on the stability of the system. Due to these nonlinearities, these devices may exhibit undesirable and complex dynamics, which are the focus of many researchers. Even though a lot of research has taken place in this area during the last 20 years, it is still an active research topic for mainstream power engineers. This research has demonstrated that these systems can become unstable with a direct result in increased losses, extra subharmonics, and even uncontrollability/unobservability. The detailed study of these systems can help in the design of smaller, lighter, and less expensive converters that are particularly important in emerging areas of research like electric vehicles, smart grids, renewable energy sources, and others. The aim of this Special Issue is to cover control and nonlinear aspects of instabilities in different energy conversion systems: theoretical, analysis modelling, and practical solutions for such emerging applications. In this Special Issue, we present novel research works in different areas of the control and nonlinear dynamics of energy conversion systems.

Keywords

data-driven --- prediction --- neural network --- air-handling unit (AHU) --- supply air temperature --- pulverizing system --- soft sensor --- inferential control --- moving horizon estimation --- multi-model predictive control --- micro-grid --- droop control --- virtual impedance --- harmonic suppression --- power quality --- combined heat and power unit --- two-stage bypass --- dynamic model --- coordinated control system --- predictive control --- decoupling control --- power conversion --- model–plant mismatches --- disturbance observer --- performance recovery --- offset-free --- electrical machine --- electromagnetic vibration --- multiphysics --- rotor dynamics --- air gap eccentricity --- calculation method --- magnetic saturation --- corrugated pipe --- whistling noise --- Helmholtz number --- excited modes --- switched reluctance generator --- capacitance current pulse train control --- voltage ripple --- capacitance current --- feedback coefficient --- distributed architecture --- maximum power point tracking --- sliding mode control --- overvoltage --- permanent magnet synchronous motor (PMSM) --- single artificial neuron goal representation heuristic dynamic programming (SAN-GrHDP) --- single artificial neuron (SAN) --- reinforcement learning (RL) --- goal representation heuristic dynamic programming (GrHDP) --- adaptive dynamic programming (ADP) --- sliding mode observer (SMO) --- permanent magnet synchronous motor (PMSM) --- extended back electromotive force (EEMF) --- position sensorless --- bridgeless converter --- discontinuous conduction mode (DCM) --- high step-up voltage gain --- power factor correction (PFC) --- space mechanism --- multi-clearance --- nonlinear dynamic model --- planetary gears --- vibration characteristics --- new step-up converter --- ultrahigh voltage conversion ratio --- small-signal model --- average-current mode control --- slope compensation --- monodromy matrix --- current mode control --- boost-flyback converter --- explosion-magnetic generator --- plasma accelerator --- current-pulse formation --- DC-DC buck converter --- contraction analysis --- global stability --- matrix norm --- DC micro grid --- efficiency optimization --- variable bus voltage MG --- variable switching frequency DC-DC converters --- centralized vs. decentralized control --- local vs. global optimization --- buck converter --- DC motor --- bifurcations in control parameter --- sliding control --- zero average dynamics --- fixed-point inducting control --- DC-DC converters --- quadratic boost --- maximum power point tracking (MPPT) --- nonlinear dynamics --- subharmonic oscillations --- photovoltaic (PV) --- steel catenary riser --- rigid body rotation --- wave --- the load of suspension point in the z direction --- Cable3D

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