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Online Reinforcement Learning for Self-adaptive Information Systems [chapter]

Alexander Palm, Andreas Metzger, Klaus Pohl
2020 Lecture Notes in Computer Science  
Existing online RL approaches for self-adaptive information systems exhibit two shortcomings that limit the degree of automation: they require manually fine-tuning the exploration rate and may require  ...  To develop a self-adaptive information system, information system engineers have to create self-adaptation logic that encodes when and how the system should adapt itself.  ...  for their constructive comments.  ... 
doi:10.1007/978-3-030-49435-3_11 fatcat:ebl2mtyzyzd67keui7lmah7yqi

A Meta Reinforcement Learning-based Approach for Self-Adaptive System [article]

Mingyue Zhang, Jialong Li, Haiyan Zhao, Kenji Tei, Shinichi Honiden, Zhi Jin
2021 arXiv   pre-print
A self-learning adaptive system (SLAS) uses machine learning to enable and enhance its adaptability. Such systems are expected to perform well in dynamic situations.  ...  In addition, it designs a meta-reinforcement learning algorithm for learning the meta policy over the multiple models, so that the meta policy can quickly adapt to the real environment-system dynamics.  ...  Related Work A self-learning adaptive system is able to adjust or re-plan the adaptation policy online.  ... 
arXiv:2105.04986v1 fatcat:mbwvpuye3vf75ou4dc5oqgpg7e

Text/Conference Paper

Simon Reichhuber
2019 Jahrestagung der Gesellschaft für Informatik  
Achieving this, the fields of Multi-Agent Learning, Organic Computing, Transfer Learning, and Online Learning are combined to an unified architecture.  ...  In order to show the practical use case of such systems, the author presents two scenarios.  ...  Applications in distributed domains In this section possible scenarios are described, for which the knowledge self-adaptive multi-agent learning system can be integrated and evaluated.  ... 
doi:10.18420/inf2019_ws54 dblp:conf/gi/Reichhuber19 fatcat:5ropw5uokbc2xe5sthze7tsdl4

Self-adaptive process optimisation method for SBS cloud application based on reinforcement learning

Haiyan Hu, Chang Su
2021 International Journal of Information and Communication Technology  
on reinforcement learning was proposed.  ...  Establish the adaptive action type selection model, realise the optimal choice of operation type, build the cloud application adaptive process optimisation model for resource cost, convert the problem  ...  Self-adaptive process training based on reinforcement learning Assuming processing information a can get b, then processing b can get information c, it can be proved that mutual information between a and  ... 
doi:10.1504/ijict.2021.111923 fatcat:swwerziv65az5bicvu55cpvq7a

Adaptive Mechanism Based on Shared Learning in Multi-agent System [chapter]

Qingshan Li, Hua Chu, Liang Diao, Lu Wang
2014 IFIP Advances in Information and Communication Technology  
the reinforcement learning technology and software agent technology to propose an adaptive mechanism based on shared learning in multiple agent system.  ...  Based on this, framework for constructing adaptive systems and shared learning algorithm of agent are given.  ...  Conclusion This paper proposes an operation mechanism and learning algorithm for self-adaptive multi-agent system, which makes the self-adaptive agent have the learning capacity and can use the learning  ... 
doi:10.1007/978-3-662-44980-6_13 fatcat:4db5luf3a5hm7p6gycg7di4tze

Intelligent learning and control of autonomous robotic agents operating in unstructured environments

Hani Hagras, Tarek Sobh
2002 Information Sciences  
Because environments and users of systems continuously change, robotic agents have to be adaptive.  ...  Intelligence helps because it gives systems the capacity to adapt more rapidly to environmental changes or to handle much more complex functions.  ...  Thus it is not feasible for a simple GA to learn online and adapt in real-time.  ... 
doi:10.1016/s0020-0255(02)00221-9 fatcat:ul5wno4oonhvphivc3257pkgqa

Reinforcement Learning Approach for Adaptive e-Learning Based on Multiple Learner Characteristics

Dan Oyuga Anne, Elizaphan Maina
2021 Open Journal for Information Technology  
We finally pass the system extracted information the adaptivity domain which uses the off-policy Q-learning model free algorithm (Jang et al., 2019) to structure the learning path into tutorials, lectures  ...  We include the theories of learning style to categorize and identify specific individuals so as to improve their experience on the online learning platform and apply it in the model.  ...  This research did not receive any specific grant from funding agencies in the public commercial, or not-for-profit sectors. The authors declare no competing interests.  ... 
doi:10.32591/coas.ojit.0402.03055o fatcat:4uxsbx57ana77ppm74uzlu7tta

Special Issue of BICS 2016

Cheng-Lin Liu, Amir Hussain, Bin Luo, Kay Chen Tan, Yi Zeng, Zhaoxiang Zhang
2018 Cognitive Computation  
neuron system and apply it to a humanoid robot for self-recognition.  ...  biasing effect on reinforcement learning in basal ganglia; (2) Dopamine signals continuously update reward-relevant information for both basal ganglia and working memory in the prefrontal cortex.  ...  neuron system and apply it to a humanoid robot for self-recognition.  ... 
doi:10.1007/s12559-018-9551-3 fatcat:ighf7k3fsvhntk3usvfayy6hn4

Synchronous Reinforcement Learning-Based Control for Cognitive Autonomy

Kyriakos G. Vamvoudakis, Nick-Marios T. Kokolakis
2020 Foundations and Trends® in Systems and Control  
Introduction In this monograph we present a family of model-free, and modelbased online adaptive learning algorithms for single and multi-agent systems using measurements along the system trajectories  ...  Adaptive controllers learn online, i.e., process data and decide in real-time, to control unknown systems using data measured along the system trajectories.  ... 
doi:10.1561/2600000022 fatcat:opy7x3dktfcd7i6d6uo3kuowwe

Guest Editorial: AI Applications to Intelligent Vehicles for Advancing Intelligent Transport Systems

2020 IET Intelligent Transport Systems  
He serves as the reviewer from more than 20 known journals, and was awarded as an outstanding reviewer by Mechanical Systems and Signal Processing and Mechatronics in 2018.  ...  In 'Hierarchical reinforcement learning for self-driving decision-making without reliance on labelled driving data', Duan et al. present a hierarchical reinforcement learning method for decision making  ...  Moreover, by online learning, their model addresses the data limitation problem and get an adaptive ability to new scenes.  ... 
doi:10.1049/iet-its.2020.0189 fatcat:7cgsidr4lfeqvd3ffjtmv4guwi

Challenges for interactivist-constructivist robotics

Răzvan V. Florian
2010 New Ideas in Psychology  
Training may be performed by reinforcement learning, imitation or guidance.  ...  a sound framework for understanding cognition and representation and for designing genuinely intelligent artificial systems.  ...  An alternative way to designing artificial intelligent systems is to mimic nature, since humans and some animals are the only systems that do feature adaptive, robust intelligence.  ... 
doi:10.1016/j.newideapsych.2009.09.009 fatcat:qx7sd2lyszcr5gupgdcwq5q5xy

Introduction to the Special Section on Artificial Intelligence Security: Adversarial Attack and Defense

Xiaojiang Du, Willy Susilo, Mohsen Guizani, Zhihong Tian
2021 IEEE Transactions on Network Science and Engineering  
, and security issues in federated learning, reinforcement learning, and online learning.  ...  Yin et al. in "Online Learning Aided Adaptive Multiple Attribute-based Physical Layer Authentication in Dynamic Environments" consider an online learning aided adaptive PHY-layer authentication framework  ... 
doi:10.1109/tnse.2021.3073637 fatcat:ib5qh53qq5bu5hrfjejm3fp76i

Using Chatbots to Teach Languages [article]

Yu Li, Chun-Yen Chen, Dian Yu, Sam Davidson, Ryan Hou, Xun Yuan, Yinghua Tan, Derek Pham, Zhou Yu
2022 arXiv   pre-print
Our next step is to make our system more adaptive to user profile information by using reinforcement learning algorithms.  ...  This paper reports on progress towards building an online language learning tool to provide learners with conversational experience by using dialog systems as conversation practice partners.  ...  We will develop a reinforcement learning-based adaptive policy in our system in the future.  ... 
arXiv:2208.00376v1 fatcat:ej52lhizjfanhibi6jtmfebzry

A Survey of Self-Organization Mechanisms in Multiagent Systems

Dayong Ye, Minjie Zhang, Athanasios V. Vasilakos
2017 IEEE Transactions on Systems, Man & Cybernetics. Systems  
This paper serves as a guide and a starting point for anyone who will conduct research on self-organisation in multi-agent systems.  ...  Self-organisation mechanisms in other fields have been thoroughly surveyed. However, there has not been a survey of self-organisation mechanisms developed for use in multiagent systems.  ...  Based on the self-adaptive approach, the performance of the continuous online learning is improved and the continuous online learning can adapt to environmental variations.  ... 
doi:10.1109/tsmc.2015.2504350 fatcat:pxcpilmesjc6hdijxci23zk2pu

ACTIVE LEARNING IN E-LEARNING: ADVANCING A SYSTEMIC MODEL

2012 Issues in Information Systems  
This paper advances a systemic model for active learning in e-learning that builds on a string of previous research.  ...  The model is systemic because the three stages and the prerequisite elements are interrelated to each other and together influence the "learning" in e-learning.  ...  Figure 1 illustrates the systemic model for active learning in e-learning.  ... 
doi:10.48009/1_iis_2012_68-76 fatcat:fi5t23cixjdnznxikvnz3h3qvm
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