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arXiv 2018年

作者： Tallamraju, Rahul Rajappa, Sujit Black, Michael Karlapalem, Kamalakar Ahmad, Aamir Perceiving Systems Department Max Planck Institute for Intelligent Systems Tübingen Germany Chair of Cognitive Systems Department of Computer Science University of Tubingen Tübingen Germany Agents and Applied Robotics Group International Institute for Information Technology Hyderabad India

In this work, we consider the problem of decentralized multi-robot target tracking and obstacle avoidance in dynamic environments. Each robot executes a local motion planning algorithm which is based on model predictive control (MPC). The planner is designed as a quadratic program, subject to constraints on robot dynamics and obstacle avoidance. Repulsive potential field functions are employed to avoid obstacles. The novelty of our approach lies in embedding these nonlinear potential field functions as constraints within a convex optimization framework. Our method convexifies non-convex constraints and dependencies, by replacing them as pre-computed external input forces in robot dynamics. The proposed algorithm additionally incorporates different methods to avoid field local minima problems associated with using potential field functions in planning. The motion planner does not enforce predefined trajectories or any formation geometry on the robots and is a comprehensive solution for cooperative obstacle avoidance in the context of multi-robot target tracking. Video of simulation studies: https://***/umkdm82Tt0M. Copyright © 2018, The Authors. All rights reserved.

关键词： Multipurpose robots

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Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots

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Internet of Things and Cyber-Physical systems 2024年第1期4卷 19-23页

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Faculty of Computing and Data Science FLAME University Maharashtra Pune India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023 The Authors

关键词： Machine learning

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Energy-optimal online algorithms for broadcasting in wireless networks

Energy-optimal online algorithms for broadcasting in wireles...

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Annual Conference on Wireless On Demand Network systems and Services (WONS)

作者： S. Kutten H. Ono D. Peleg K. Sadakane M. Yamashita Faculty of Industrial Engineering and Management Information Systems Group Technion-Israel Institute of Technology Haifa Israel Department of Computer Science and Communication Engineering Kyushu University Fukuoka Japan Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot Israel

The paper considers the design of energy-efficient online protocols for the basic problem of message transmission to hosts positioned at unknown distances in ad-hoc wireless networks. The paper formulates a number of ... 详细信息

关键词： Broadcasting Wireless networks Energy efficiency Energy consumption Wireless application protocol Algorithm design and analysis computer science Multicast algorithms Attenuation Decoding

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Learner typing for electronic learning systems

Learner typing for electronic learning systems

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International Conference on Advanced Learning Technologies (ICALT)

作者： R. Kaschek K.D. Schewe B. Thalheim T. Kuss B. Tschiedel Department of Information Systems Massey University Palmerston New Zealand Computer Science and Applied Mathematics Institute University of Kiel Germany Center for Cognitive Science University of Freiburg Germany Department Informatics Technical University of Cottbus Cottbus Germany

e-Learning systems will provide new pathways to knowledge and skills acquisition. To increase their quality using a parametric learner model is proposed. Tailoring the learning material presented to the respective parameter values may fit this material to the actual learner's needs. We use a stereotype user model. Our learner characteristics complement the IMS UP. We first represent learners by points in a learner space. Then we propose increasingly generic ways of grouping together such points to form learner types. The less generic one models learner types using convex regions in the learner space. The more generic one models learner types using characteristic functions. While the latter approach allows for more flexible learner type combination the former approach allows for convincing definition of initial learner types. Combining both approaches thus may result in a reasonable number of useful learner types.

关键词： Electronic learning Information systems computer science mathematics cognitive science Informatics Context modeling Human factors Proposals

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Navigating a smart wheelchair with a brain-computer interface interpreting steady-state visual evoked potentials

Navigating a smart wheelchair with a brain-computer interfac...

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2009 IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Christian Mandel Thorsten Lüth Tim Laue Thomas Röfer Axel Gräser Bernd Krieg-Brückner Department of Mathematics and Computer Science University of Brethemen Breman Germany Institute of Automation University of Brethemen Breman Germany German Research Center for Artificial Intelligence Research Group Safe and Secure Cognitive Systems Breman Germany

In order to allow severely disabled people who cannot move their arms and legs to steer an automated wheelchair, this work proposes the combination of a non-invasive EEG-based human-robot interface and an autonomous navigation system that safely executes the issued commands. The robust classification of steady-state visual evoked potentials in brain activity allows for the seamless projection of qualitative directional navigation commands onto a frequently updated route graph representation of the environment. The deduced metrical target locations are navigated to by the application of an extended version of the well-established nearness diagram navigation method. The applicability of the system proposed is demonstrated by a real-world pilot study in which eight out of nine untrained subjects successfully navigated an automated wheelchair, requiring only some ten minutes of preparation.

关键词： Navigation Wheelchairs Brain computer interfaces Steady-state Communication system control Hardware Contracts Signal processing Electroencephalography Intelligent robots

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A constraint-reduced MPC algorithm for convex quadratic programming, with a modified active set identification scheme

arXiv

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arXiv 2018年

作者： Paul Laiu, M. Tits, André L. Computational and Applied Mathematics Group Computer Science and Mathematics Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Department of Electrical and Computer Engineering Institute for Systems Research University of Maryland College ParkMD20742 United States

A constraint-reduced Mehrotra–Predictor–Corrector algorithm for convex quadratic programming is proposed. (At each iteration, such algorithms use only a subset of the inequality constraints in constructing the search direction, resulting in CPU savings.) The proposed algorithm makes use of a regularization scheme to cater to cases where the reduced constraint matrix is rank deficient. Global and local convergence properties are established under arbitrary working-set selection rules subject to satisfaction of a general condition. A modified active-set identification scheme that fulfills this condition is introduced. Numerical tests show great promise for the proposed algorithm, in particular for its active-set identification scheme. While the focus of the present paper is on dense systems, application of the main ideas to large sparse systems is briefly discussed. Copyright © 2018, The Authors. All rights reserved.

关键词： Constraint theory

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AN INFEASIBLE-START FRAMEWORK FOR CONVEX QUADRATIC OPTIMIZATION, WITH APPLICATION TO CONSTRAINT-REDUCED INTERIOR-POINT METHODS ∗

arXiv

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arXiv 2019年

A framework is proposed for solving general convex quadratic programs (CQPs) from an infeasible starting point by invoking an existing feasible-start algorithm tailored for inequalityconstrained CQPs. The central tool is an exact penalty function scheme equipped with a penalty-parameter updating rule. The feasible-start algorithm merely has to satisfy certain general requirements, and so is the updating rule. Under mild assumptions, the framework is proved to converge on CQPs with both inequality and equality constraints and, at a negligible additional cost per iteration, produces an infeasibility certificate, together with a feasible point for an (approximately) `1-least relaxed feasible problem when the given problem does not have a feasible solution. The framework is applied to a feasible-start constraint-reduced interior-point algorithm previously proved to be highly performant on problems with many more constraints than variables ("imbalanced"). Numerical comparison with popular codes (SDPT3, SeDuMi, MOSEK) is reported on both randomly generated problems and support-vector machine classifier training problems. The results show that the former typically outperforms the latter on imbalanced problems. Copyright © 2019, The Authors. All rights reserved.

关键词： Support vector machines

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Microscopic Disruption Management: Energy Consumption and Passenger Compensation Optimisation

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Transportation Research Procedia 2022年 62卷 43-50页

作者： Luis Cadarso Esteve Codina Ricardo García-Ródenas Aerospace Systems and Transport Research Group European Institute for Aviation Training and Accreditation (EIATA) Rey Juan Carlos University Fuenlabrada 28942 Madrid Spain Department of Statistics and Operations Research Universitat Politècnica de Catalunya (UPC) Spain Department of Mathematics School of Computer Science Institute of Applied Mathematics in Science and Engineering (IMACI) University of Castilla-La Mancha Ciudad Real Spain

Rail operations are often disrupted by accidents that cause traffic to diverge from the scheduled operations, rendering it difficult to run the schedule as planned. In such a case, the operator must change the schedule to return to the original schedule. If passengers are delayed, a train operator may have a policy of economically compensating them (e.g., refunding ticket fare). Compensation amounts are usually determined by the length of the delay. As a result, it is critical to have a smart way of determining whether to accelerate trains to absorb delays, thus increasing energy usage, or to compensate passengers. This paper presents a mathematical model for determining the speed profile while taking passenger usage into account. The model determines the best sequence of operating regimes and switching points between them for a variety of different situations and train types, all while accounting for delays and passenger compensation policies. The aim is to reduce both the amount of energy consumed and the amount of compensation paid to passengers. There are constraints on traction and braking forces, train velocity, forces induced by vertical and horizontal track profile, and passenger compensation policy. The results of computational tests performed on practical problem instances of the Spanish rail operator RENFE are showed. The suggested approach is capable of producing strategies that strike an excellent balance between different managerial objectives.

关键词： Train re-scheduling Passenger compensation Delays Optimal control

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Reports of the AAAI 2014 conference workshops

Reports of the AAAI 2014 conference workshops

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作者： Albrecht, Stefano V. Barreto, André M. S. Braziunas, Darius Buckeridge, David L. Cuayáhuitl, Heriberto Dethlefs, Nina Endres, Markus Farahmand, Amir-Massoud Fox, Mark Frommberger, Lutz Ganzfried, Sam Guillet, Sébastien Gil, Yolanda Hunter, Lawrence E. Jhala, Arnav Kersting, Kristian Konidaris, George Lecue, Freddy McIlraith, Sheila Natarajan, Sriraam Noorian, Zeinab Poole, David Ronfard, Rémi Saffiotti, Alessandro Shaban-Nejad, Arash Srivastava, Biplav Tesauro, Gerald Uceda-Sosa, Rosario Van Den Broeck, Guy Van Otterlo, Martijn Wallace, Byron C. Weng, Paul Wiens, Jenna Zhang, Jie School of Informatics University of Edinburgh United Kingdom Department of Applied and Computational Mathematics Brazilian National Laboratory Brazil Big Data Group Kobo Inc. Brazil Department of Epidemiology and Biostatistics McGill University Canada School of Mathematical and Computer Sciences Heriot-Watt University in Edinburgh United Kingdom University of Augsburg Germany Carnegie Mellon University United States University of Toronto Canada Cognitive Systems Group University of Bremen Germany LIARA UQAC Chicoutimi Canada Information Sciences Institute University of Southern California Computer Science Department United States Center for Computational Pharmacology University of Colorado School of Medicine United States Department of Computational Media University of California Santa Cruz United States Computer Science Department Technical University of Dortmund Germany MIT Computer Science and Artificial Intelligence Laboratory CambridgeMA United States IBM Research - Smarter Cities Technology Centre Dubline Ireland School of Informatics and Computing Indiana University United States Department of Computer Science University of Saskatchewan Canada University of British Columbia Canada INRIA University of Grenoble France Orebro University Sweden McGill Clinical and Health Informatics Group McGill University Canada IBM Research United States Cognitive Computing Department IBM Research Yorktown HeightsNY United States Computer Science Department Katholieke Universiteit Leuven Belgium Cognitive Artificial Intelligence Department Radboud University Nijmegen Netherlands University of Texas Austin United States Pierre and Marie Curie University Paris France University of Michigan United States School of Computer Engineering Nanyang Technological University Singapore

The AAAI-14 Workshop program was held Sunday and Monday, July 27-28, 2014, at the Québec City Convention Centre in Québec, Canada. The AAAI-14 workshop program included 15 workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were Artificial Intelligence and Robotics;Artificial Intelligence applied to Assistive Technologies and Smart Environments;cognitive Computing for Augmented Human Intelligence;computer Poker and Imperfect Information;Discovery Informatics;Incentives and Trust in Electronic Communities;Intelligent Cinematography and Editing;Machine Learning for Interactive systems: Bridging the Gap Between Perception, Action, and Communication;Modern Artificial Intelligence for Health Analytics;Multiagent Interaction Without Prior Coordination;Multidisciplinary Workshop on Advances in Preference Handling;Semantic Cities - Beyond Open Data to Models, Standards, and Reasoning;Sequential Decision Making with Big Data;Statistical Relational AI;and the World Wide Web and Public Health Intelligence. This article presents short summaries of those events. © 2015, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Decision making

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Embodied Runtime Monitoring of Learning-enabled Robot Perception Components

Embodied Runtime Monitoring of Learning-enabled Robot Percep...

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IEEE International Conference on Automation science and Engineering (CASE)

作者： Deebul Nair Sathwik Panchangam Miguel A. Olivares-Mendez Nico Hochgeschwender Institute for Artificial Intelligence and Autonomous Systems Bonn-Rhein-Sieg University of Applied Sciences Sankt Augustin Germany Space Robotics Research Group (SpaceR) Interdisciplinary Centre for Security Reliability and Trust (SnT) University of Luxembourg Luxembourg Department of Mathematics and Computer Science University of Bremen Germany

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Monitoring during runtime, the estimated uncertainty from learning-enabled robot perception components, is a vital tool for assuring and evaluating model correctness. By utilizing different uncertainty estimation methods, monitors exploit the perceived sensor data and predictions generated by the perception model to make informed decisions, such as in safety-critical situations. In robotics, informed decisions should be aligned with embodied information regarding space and time. We argue and show that runtime monitors of learning-enabled components can be made more robust by including the embodiment information of the robot. However, it remains challenging to build uncertainty-based runtime monitors with embodiment information using only real-world datasets, which are tedious to collect. To this end, we propose a solution that leverages simulation-based datasets to characterize expected uncertainty estimates with robot embodiment. By constructing a nominal runtime monitor model based on simulated datasets, we establish a reference for the expected uncertainty along with the embodiment information that can be subsequently used to monitor real-world deployed perception components. We demonstrate the zero-shot sim-to-real transfer of our approach on a Kinova Gen3 manipulator picking task, for which the runtime monitor reliability model was trained using simulation-based data containing embodiment information of distance from the object being selected.

关键词： Runtime Uncertainty Computational modeling Estimation Predictive models Robot sensing systems Manipulators Reliability engineering Data models Monitoring

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