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2020 Index IEEE Robotics and Automation Letters Vol. 5

2020 IEEE Robotics and Automation Letters  
Vanneste, F., +, LRA April 2020 2380-2386 Control of a Silicone Soft Tripod Robot via Uncertainty Compensation.  ...  Wang, H., +, LRA April 2020 1119-1126 Compensation Control of a Silicone Soft Tripod Robot via Uncertainty Compensation.  ... 
doi:10.1109/lra.2020.3032821 fatcat:qrnouccm7jb47ipq6w3erf3cja

Table of Contents

2016 Australian Journal of Primary Health  
A 3D 4-field box treatment with a 5% uncertainty and an IMRT treatment with 11% uncertainty have the same treatment outcome expectation value.  ...  In this work, we introduce A^3P, a risk-aware task-level reinforcement learning algorithm. A^3P represents a task-level state machine as a POMDP.  ... 
doi:10.1071/pyv22n4toc fatcat:hrwhnxdpjreslhuwi7y4edqmo4

2021 Index IEEE Internet of Things Journal Vol. 8

2021 IEEE Internet of Things Journal  
The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination.  ...  ., +, JIoT Nov. 1, 2021 16181-16190 Image fusion Building a Lane Merge Coordination for Connected Vehicles Using Deep Reinforcement Learning.  ...  ., +, JIoT Oct. 15, 2021 15059-15069 FlexSensing: A QoI and Latency-Aware Task Allocation Scheme for Vehicle-Based Visual Crowdsourcing via Deep Q-Network.  ... 
doi:10.1109/jiot.2022.3141840 fatcat:42a2qzt4jnbwxihxp6rzosha3y

Probabilistic Motion Planning for Automated Vehicles

Maximilian Naumann
2020
In motion planning for automated vehicles, a thorough uncertainty consideration is crucial to facilitate safe and convenient driving behavior.  ...  The approaches consider uncertainties from imperfect perception, occlusions and limited sensor range, and also those in the behavior of other traffic participants.  ...  On the other hand, due to the need for real-time capability, a holistic uncertainty treatment in probabilistic approaches often induces a strong limitation of the action space of automated vehicles.  ... 
doi:10.5445/ksp/1000126389 fatcat:p3vwppt2ejc77at3hql775vtpa

Probabilistic Motion Planning for Automated Vehicles

Maximilian Naumann
2020
The actual planning is done via evaluation of the planning-prediction-cycle in a forward simulation and determining the probability of such an evolution via collision risk assessment.  ...  Again, the presented behavior shows the key idea of the probability treatment within All Uncertainties Combined Finally, we present a scenario with all the different types of uncertainties.  ... 
doi:10.5445/ir/1000123725 fatcat:k4iapl23lrc4bhrkq7iexjkrlm

Deep Reinforcement Learning [article]

Yuxi Li
2018 arXiv   pre-print
We start with background of artificial intelligence, machine learning, deep learning, and reinforcement learning (RL), with resources.  ...  We draw a big picture, filled with details. We discuss six core elements, six important mechanisms, and twelve applications, focusing on contemporary work, and in historical contexts.  ...  The authors experiment with grid world coordination, a partially observable game,  ... 
arXiv:1810.06339v1 fatcat:kp7atz5pdbeqta352e6b3nmuhy

Artificial Intellgence – Application in Life Sciences and Beyond. The Upper Rhine Artificial Intelligence Symposium UR-AI 2021 [article]

Karl-Herbert Schäfer
2021 arXiv   pre-print
The TriRhenaTech alliance is a network of universities in the Upper-Rhine Trinational Metropolitan Region comprising of the German universities of applied sciences in Furtwangen, Kaiserslautern, Karlsruhe  ...  Topics of the conference are applications of Artificial Intellgence in life sciences, intelligent systems, industry 4.0, mobility and others.  ...  This work was funded by the Ministry of Science, Research and Arts of Baden-Württemberg (MWK) as part of the project Q-AMeLiA (Quality Assurance of Machine Learning Applications).  ... 
arXiv:2112.05657v1 fatcat:wdjgymicyrfybg5zth2dc2i3ni

A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges [article]

Moloud Abdar, Farhad Pourpanah, Sadiq Hussain, Dana Rezazadegan, Li Liu, Mohammad Ghavamzadeh, Paul Fieguth, Xiaochun Cao, Abbas Khosravi, U Rajendra Acharya, Vladimir Makarenkov, Saeid Nahavandi
2021 arXiv   pre-print
Uncertainty quantification (UQ) plays a pivotal role in reduction of uncertainties during both optimization and decision making processes.  ...  It can be applied to solve a variety of real-world applications in science and engineering.  ...  They devised a technique for VI with implicit priors and denoted DWP in a form of an implicit distribution.  ... 
arXiv:2011.06225v4 fatcat:wwnl7duqwbcqbavat225jkns5u

Robot Controllers for Highly Dynamic Environments with Real-time Constraints

Alexander Ferrein
2010 Künstliche Intelligenz  
A thorough treatment of this can be found in .  ...  Other techniques for decision making concentrate on the uncertainty of sensors, actuators and the environment the robot interacts with.  ... 
doi:10.1007/s13218-010-0041-3 fatcat:ltrvy7pynjbs7joc5exp2khgii

SELECTIVE DETERMINISM FOR AUTONOMOUS NAVIGATION IN MULTI-AGENT SYSTEMS

Jeffrey Johnson, Martha White, Kevin Pilgrim, Christoph Stiller
2017 unpublished
Kris Hauser who introduced me to the field of robotics and whose rigor and insights as a teacher, adviser, and researcher gave me the foundation necessary to pursue new ideas and become a successful researcher  ...  David Crandall, Paul Purdom, Martha White, Kevin Pilgrim, and Christoph Stiller, for their valuable comments, suggestions, and ideas that helped shape and improve the quality of this thesis.  ...  An interacting agent is one whose dynamic state is a function of the dynamic state of the system and an internal policy (for example, pedestrians or animals could be interacting agents).  ... 
fatcat:slsodyny3respilkvssqe4zp4e

Steps Towards a Theory of Visual Information: Active Perception, Signal-to-Symbol Conversion and the Interplay Between Sensing and Control [article]

Stefano Soatto
2017 arXiv   pre-print
Gibson, and a notion of "complete information" that relates to the minimal sufficient statistics of a complete representation.  ...  The concept of Actionable Information is described, that relates to a notion of information championed by J.  ...  determines g as a function of I, g =ĝ(I), via the Implicit Function Theorem.  ... 
arXiv:1110.2053v4 fatcat:utdycuug75drzkm2a4s74ozeg4

Acting under Uncertainty for Information Gathering and Shared Autonomy

Shervin Javdani
2018
As uncertainty is often the culprit of failure, many prior works attempt to reduce the problem to one with a known state.  ...  However, this fails to account for a key property of acting under uncertainty: we can often gain utility while uncertain.  ...  Chung and Huang [CH11] use A* search to predict pedestrian motions, including a model of uncertainty, and plan paths using these predictions. Bandyopadhyay et al.  ... 
doi:10.1184/r1/6714560 fatcat:pr7fhad7svfgjn3ebcjodzhkhi

2013 Jahresbericht Annual Report

Gesellschafter
unpublished
a high degree of uncertainty that is atypical in more conventional systems.  ...  plans for a book on "comprehensive modelling", a step towards a common vision of the research field.  ...  Structure of the Center Schloss Dagstuhl wird als eine gemeinnützige GmbH Schloss Dagstuhl is operated as a non-profit organibetrieben, deren Gesellschafter die Gesellschaft für Infor-zation whose associates  ... 
fatcat:xixyfnmw65ehzlozcfxl55r5uq

Machine Learning and Multiagent Preferences

Ritesh Noothigattu
2021
In classical social choice, each of the n agents presents a ranking over the m candidates, and the goal is to find a winning candidate(or a consensus ranking) that is the most \fair" outcome.  ...  We also consider the setting where agents have utility functions over a given set of outcomes,and our goal is to learn a classifier that is fair with respect to these preferences.  ...  the implicit constraints of society.  ... 
doi:10.1184/r1/14402201 fatcat:npot53a4vbd43gzvjamfljxdgy