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Learning and Reasoning in Cognitive Radio Networks

Liljana Gavrilovska, Vladimir Atanasovski, Irene Macaluso, Luiz A. DaSilva
2013 IEEE Communications Surveys and Tutorials  
This fosters optimal resource usage and management allowing a plethora of potential applications such as secondary spectrum access, cognitive wireless backbones, cognitive machine-to-machine etc.  ...  Cognitive radio networks challenge the traditional wireless networking paradigm by introducing concepts firmly stemmed into the Artificial Intelligence (AI) field, i.e., learning and reasoning.  ...  After having briefly summarized some of the game theoretic models used to analyze multi-agent decision making and the process of arriving at stable outcomes (critical for cognitive radios operating in  ... 
doi:10.1109/surv.2013.030713.00113 fatcat:u6lbgu3e4vhqhozertlqgpqkei

Multi-agent deep reinforcement learning: a survey

Sven Gronauer, Klaus Diepold
2021 Artificial Intelligence Review  
We focus primarily on literature from recent years that combines deep reinforcement learning methods with a multi-agent scenario.  ...  Although the multi-agent domain has been overshadowed by its single-agent counterpart during this progress, multi-agent reinforcement learning gains rapid traction, and the latest accomplishments address  ...  Acknowledgements We would like to thank the editor and the three anonymous reviewers for providing  ... 
doi:10.1007/s10462-021-09996-w fatcat:blu4ekwaxjfo5it3y7taqnzq4a

Reinforcement Learning in Healthcare: A Survey [article]

Chao Yu, Jiming Liu, Shamim Nemati
2020 arXiv   pre-print
As a subfield of machine learning, reinforcement learning (RL) aims at empowering one's capabilities in behavioural decision making by using interaction experience with the world and an evaluative feedback  ...  domains that have infiltrated many aspects of a healthcare system.  ...  The Markov decision process (MDP), which has a long history in the research of theoretic decision making in stochastic settings, has been used as a general framework to formalize an RL problem.  ... 
arXiv:1908.08796v4 fatcat:iqqe3jifqvfntmxr6cakl4p2fy

Persuasive Teachable Agent for Intergenerational Learning [article]

Su Fang Lim
2016 arXiv   pre-print
In this book, we have proposed the Persuasive Teachable Agent (PTA), a teachable agent based on the theoretical framework of persuasion, computational and goal-oriented agent modelling.  ...  Teachable agents are commonly used in the areas of science and mathematics learning where learners are able to learn complex concepts and deep reasoning by teaching the teachable agent through graphic  ...  The Betty system learns using hidden Markov models (HMM) (Rabiner & Juang, 1986) generated from student behaviour activity logs to measure self-regulated learning based on learning behaviour in a learner's  ... 
arXiv:1601.07264v1 fatcat:uqgqwbvhbja63fgxi3zz5daori

A Survey on Machine-Learning Techniques for UAV-Based Communications

Petros S Bithas, Emmanouel T Michailidis, Nikolaos Nomikos, Demosthenes Vouyioukas, Athanasios G Kanatas
2019 Sensors  
In this article, we provide a detailed survey of all relevant research works, in which ML techniques have been used on UAV-based communications for improving various design and functional aspects such  ...  Their adoption in various communication-based applications is expected to improve coverage and spectral efficiency, as compared to traditional ground-based solutions.  ...  Towards this end, the author adopted the Markov decision process (MDP) framework to model the defense policy selection.  ... 
doi:10.3390/s19235170 pmid:31779133 pmcid:PMC6929112 fatcat:pnur7lmpj5bj7poebmdfpd6bhi

WiFi Meets ML: A Survey on Improving IEEE 802.11 Performance with Machine Learning [article]

Szymon Szott, Katarzyna Kosek-Szott, Piotr Gawłowicz, Jorge Torres Gómez, Boris Bellalta, Anatolij Zubow, Falko Dressler
2021 arXiv   pre-print
In this survey, we adopt a structured approach to describing the various areas where WiFi can be enhanced using ML.  ...  While classic optimization approaches fail in such conditions, machine learning (ML) is well known for being able to handle complexity.  ...  An anomaly detection approach that uses self-organizing hidden Markov model map (SOHMMM) is considered in [179] .  ... 
arXiv:2109.04786v1 fatcat:darpbu2ysvemhj35r6inht5p6u

Deep Learning in Science [article]

Stefano Bianchini, Moritz Müller, Pierre Pelletier
2020 arXiv   pre-print
These search terms allow us to retrieve DL-related publications from Web of Science across all sciences. Based on that sample, we document the DL diffusion process in the scientific system.  ...  Much of the recent success of Artificial Intelligence (AI) has been spurred on by impressive achievements within a broader family of machine learning methods, commonly referred to as Deep Learning (DL)  ...  This approach to machine intelligence is commonly refer to as 'knowledge-based' approach.  ... 
arXiv:2009.01575v2 fatcat:4ttqgjdjfjbydp7flnhcgg5p7m

Tackling Climate Change with Machine Learning [article]

David Rolnick, Priya L. Donti, Lynn H. Kaack, Kelly Kochanski, Alexandre Lacoste, Kris Sankaran, Andrew Slavin Ross, Nikola Milojevic-Dupont, Natasha Jaques, Anna Waldman-Brown, Alexandra Luccioni, Tegan Maharaj, Evan D. Sherwin, S. Karthik Mukkavilli (+6 others)
2019 arXiv   pre-print
Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate.  ...  We call on the machine learning community to join the global effort against climate change.  ...  The authors gratefully acknowledge support from National Science Foundation grant 1803547, the Center for Climate and Energy Decision Making through a cooperative agreement between the National Science  ... 
arXiv:1906.05433v2 fatcat:ykmqsivkbfcazaz3wl5f7srula

Machine Learning for Security in Vehicular Networks: A Comprehensive Survey [article]

Anum Talpur, Mohan Gurusamy
2021 arXiv   pre-print
An important application domain is vehicular networks wherein ML-based approaches are found to be very useful to address various problems.  ...  Machine Learning (ML) has emerged as an attractive and viable technique to provide effective solutions for a wide range of application domains.  ...  It is a multi-decision intelligent mechanism using SVM classification for intrusion detection.  ... 
arXiv:2105.15035v2 fatcat:5z6aqlvosjgf3o3amts3k6toxu

Modular design patterns for hybrid learning and reasoning systems

Michael van Bekkum, Maaike de Boer, Frank van Harmelen, André Meyer-Vitali, Annette ten Teije
2021 Applied intelligence (Boston)  
The main contributions of this paper are: 1) a taxonomically organised vocabulary to describe both processes and data structures used in hybrid systems; 2) a set of 15+ design patterns for hybrid AI systems  ...  organized in a set of elementary patterns and a set of compositional patterns; 3) an application of these design patterns in two realistic use-cases for hybrid AI systems.  ...  These interactions lead to collective intelligence and emergent social behaviour, such as in swarms, multi-agent systems or human-agent teams.  ... 
doi:10.1007/s10489-021-02394-3 fatcat:ecyruntfdncsbbtdglhllwc6vi

Learning future terrorist targets through temporal meta-graphs

Gian Maria Campedelli, Mihovil Bartulovic, Kathleen M. Carley
2021 Scientific Reports  
Using real-world data on attacks occurred in Afghanistan and Iraq from 2001 to 2018, we propose the use of temporal meta-graphs and deep learning to forecast future terrorist targets.  ...  the potential of artificial intelligence to counter terrorist violence.  ...  Among the tested algorithmic solutions are the use of point process modeling [11] [12] [13] , network-based approaches 14, 15 , Hidden Markov models 16 , near-repeat analysis 17 , and early-warning  ... 
doi:10.1038/s41598-021-87709-7 pmid:33879811 fatcat:oq2feajlfjfmzonqrx7xoph6lu

MIC-O-MAP: a technology-enhanced learning environment for developing micro-macro thinking skills

Anura B. Kenkre, Sahana Murthy
2017 Research and Practice in Technology Enhanced Learning  
In this paper, we report 2 cycles of iterative design, development, and evaluation of MIC-O-MAP, based on a design-based research approach.  ...  The interaction analysis also led us to identify effective actions and learning paths as students learn in interactive TEL environments.  ...  Kenkre and Murthy Research and Practice in Technology Enhanced Learning (2017) 12:23 Competing interests The authors declare that they have no competing interests.  ... 
doi:10.1186/s41039-017-0063-7 pmid:30595728 pmcid:PMC6294204 fatcat:sxfj5xhglvcmtpzkqijv5uz5tm

How to Certify Machine Learning Based Safety-critical Systems? A Systematic Literature Review [article]

Florian Tambon, Gabriel Laberge, Le An, Amin Nikanjam, Paulina Stevia Nouwou Mindom, Yann Pequignot, Foutse Khomh, Giulio Antoniol, Ettore Merlo, François Laviolette
2021 arXiv   pre-print
the question 'How to Certify Machine Learning Based Safety-critical Systems?'.  ...  Conclusion: We highlighted current efforts deployed to enable the certification of ML based software systems, and discuss some future research directions.  ...  Many thanks also goes to Freddy Lécué from Thalès, who provided us feedback on an early version of this manuscript. They all contributed to improving this SLR.  ... 
arXiv:2107.12045v3 fatcat:43vqxywawbeflhs6ehzovvsevm

Modern software cybernetics: New trends

Hongji Yang, Feng Chen, Suleiman Aliyu
2017 Journal of Systems and Software  
Cybernetics is a transdisciplinary approach for exploring regulatory systems, focusing on how systems use information, models, and control actions to steer towards and maintain their goals.  ...  The new cybernetics emphasises on communication between several systems which are trying to steer each other, which sometimes leads to the concepts of self-organisation and self-regulation.  ...  Control theory has its roots in the use of feedback as a means to regulate physical processes and mediate the effect of modelling uncertainty and noise.  ... 
doi:10.1016/j.jss.2016.08.095 fatcat:brt3jzvsrbh4nmbmqqq4735hhq

Federated Learning in Mobile Edge Networks: A Comprehensive Survey [article]

Wei Yang Bryan Lim, Nguyen Cong Luong, Dinh Thai Hoang, Yutao Jiao, Ying-Chang Liang, Qiang Yang, Dusit Niyato, Chunyan Miao
2020 arXiv   pre-print
Traditional cloudbased Machine Learning (ML) approaches require the data to be centralized in a cloud server or data center.  ...  In FL, end devices use their local data to train an ML model required by the server. The end devices then send the model updates rather than raw data to the server for aggregation.  ...  The MFG is further reduced into a single-user Markov decision process that is then solved by a neural Q-learning algorithm.  ... 
arXiv:1909.11875v2 fatcat:a2yxlq672needkejenu4j3izyu
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