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From Logic to Cognitive Science [chapter]

Sandro Skansi
2018 Undergraduate Topics in Computer Science  
processing, computer vision and robotics.  ...  A sentence like this is realizable in a neural network if and only if the network can compute it, and all sentences for which there is a network which computes them are called a temporal propositional  ... 
doi:10.1007/978-3-319-73004-2_1 fatcat:wo7bnuuolnforb36hfknhw4jpi

Artificial Intelligence and Robotics [article]

Javier Andreu Perez, Fani Deligianni, Daniele Ravi, Guang-Zhong Yang
2018 arXiv   pre-print
Robotics and AI amplify human potentials, increase productivity and are moving from simple reasoning towards human-like cognitive abilities.  ...  and future directions.  ...  A number of deep learning architectures, such as deep neural networks, deep convolutional neural networks and deep belief networks, have been applied to fields such as computer vision, automatic speech  ... 
arXiv:1803.10813v1 fatcat:p2czbmak4jcyxbtncqfqlkxtma

Unit commitment considering multiple charging and discharging scenarios of plug-in electric vehicles

Zhile Yang, Kang Li, Qun Niu, Aoife Foley
2015 2015 International Joint Conference on Neural Networks (IJCNN)  
P197 Multiscale collaborative speech denoising based on deep stacking network [#15471] Wei Jiang, Hao Zheng, Shuai Nie and Wenju Liu P198 A KALDI-DNN-based ASR system for Italian -Experiments on Children  ...  and Hayaru Shouno P368 Stochastic Least Squares Learning for Deep Architectures [#15330] Girish Kumar, Jian Min Sim, Eng Yeow Cheu and Xiaoli Li P369 Optimized Deep Belief Networks on CUDA GPUs [  ... 
doi:10.1109/ijcnn.2015.7280446 dblp:conf/ijcnn/YangLNF15 fatcat:6xlakikcfzfyhhm2spooe2j7ra

Recent Advances in Deep Reinforcement Learning Applications for Solving Partially Observable Markov Decision Processes (POMDP) Problems: Part 1—Fundamentals and Applications in Games, Robotics and Natural Language Processing

Xuanchen Xiang, Simon Foo
2021 Machine Learning and Knowledge Extraction  
In this overview, we introduce Markov Decision Processes (MDP) problems and Reinforcement Learning and applications of DRL for solving POMDP problems in games, robotics, and natural language processing  ...  A follow-up paper will cover applications in transportation, communications and networking, and industries.  ...  Acknowledgments: The authors would like to express their appreciation to friends and colleagues who had assisted in the preparation of this paper.  ... 
doi:10.3390/make3030029 fatcat:u3y7bqkoljac5not2eq5konnnm

Advancements in Deep Learning Theory and Applications: Perspective in 2020 and beyond [chapter]

Md Nazmus Saadat, Muhammad Shuaib
2020 Advances and Applications in Deep Learning  
The aim of this chapter is to introduce newcomers to deep learning, deep learning platforms, algorithms, applications, and open-source datasets.  ...  This chapter will give you a broad overview of the term deep learning, in context to deep learning machine learning, and Artificial Intelligence (AI) is also introduced.  ...  Recurrent neural network. Figure 4 . 4 Figure 4. Deep belief network. Robots are the agents who are artificially intelligent and working in the realworld replacing humans.  ... 
doi:10.5772/intechopen.92271 fatcat:27x3rlajwrc3lg5fwnyjmaf77q

On the joint use of Artificial Intelligence and Brain-Imaging Techniques in Technology-enhanced Learning Environments: A Systematic Literature Review

Kamilla Tenório, Jário Santos, Victor Accete, Sterfanno Remigio, Alan Pedro Da Silva, Diego Dermeval, Ig Ibert Bittencourt, Leonardo Brandão Marques
2021 Revista Brasileira de Informática na Educação  
Some of these studies are making use of artificial intelligence to provide real-time monitoring of students' cognitive phenomena supplied by brain-imaging techniques such as electroencephalography and  ...  The search was conducted in seven academic databases in January 2020 and resulted in a total of 6089 studies that was reduced to 20 studies for the final analysis.  ...  Ni et al. (2017) applied bidirectional LSTM to classify students' confusion, in addition to SVM, KNN, CNN, Deep Belief Network and RNN.  ... 
doi:10.5753/rbie.2021.29.0.502 fatcat:l653yvkzfbcdti2bpceaguzyye

From Humans and Back: a Survey on Using Machine Learning to both Socially Perceive Humans and Explain to Them Robot Behaviours

Adina M. Panchea, François Ferland
2020 Current Robotics Reports  
There are papers which report models for robots to imitate humans and also for humans to imitate robots.  ...  Summary This paper reports a review on social perception and explainable behaviours based on ML methods.  ...  Nao [8] 2016 Survey on of using vocal prosody to convey emotion in robot speech Hidden Markov models Deep belief networks Deep neural networks [48] Socially adaptive path planning Inverse reinforcement  ... 
doi:10.1007/s43154-020-00013-6 fatcat:l5dneve33faolgvlvf3ddqkv24

Parallel Computing for Brain Simulation

L. A. Pastur-Romay, A. B. Porto-Pazos, F. Cedron, A. Pazos
2017 Current Topics in Medicinal Chemistry  
Aims: For decades, researchers have been trying to make computers reproduce these abilities, focusing on both understanding the nervous system and, on processing data in a more efficient way than before  ...  It is focused on various works that look for advanced progress in Neuroscience and still others which seek new discoveries in Computer Science (neuromorphic hardware, machine learning techniques).  ...  Spinnaker has been connected to Nengo [175] , enabling users to create neural networks and specify the functions that are computed.  Deep Belief Networks: networks of deep learning may be implemented  ... 
doi:10.2174/1568026617666161104105725 pmid:27823566 fatcat:wlcngyt5ubcrxpyhzyepjlsqyu

6G Cognitive Information Theory: A Mailbox Perspective

Yixue Hao, Yiming Miao, Min Chen, Hamid Gharavi, Victor C. M. Leung
2021 Big Data and Cognitive Computing  
Finally, we establish a cognitive communication system assisted by deep learning.  ...  In order to solve the above challenges, we propose a 6G mailbox theory, namely a cognitive information carrier to enable distributed algorithm embedding for intelligence networking.  ...  and estimation work, convolutional neural network, recur- sive neural network, stacked auto-encoder, deep belief network Reinforcement Monte-Carlo learning, Q-learning, strategy Deep information mining  ... 
doi:10.3390/bdcc5040056 fatcat:ffof5likzbhfnopa3yfaobznfa

A Review of Artificial Intelligence, Big Data, and Blockchain Technology Applications in Medicine and Global Health

Supriya M., Vijay Kumar Chattu
2021 Big Data and Cognitive Computing  
Machine learning (ML) is a field of AI that allows computers to learn and improve without being explicitly programmed.  ...  Wearable medical devices are used to continuously monitor an individual's health status and store it in cloud computing.  ...  Zafer Al-Makhadmeh and Amr Tolba suggested a heart illness detection system based on IoT medical devices and the higher-order Boltzmann deep belief neural network (HOBDBNN).  ... 
doi:10.3390/bdcc5030041 fatcat:x25hkk3fxnbtll7cofmikpys3i

Artificial Intelligence (AI) and Cardiovascular Diseases: An Unexpected Alliance

Silvia Romiti, Mattia Vinciguerra, Wael Saade, Iñaki Anso Cortajarena, Ernesto Greco
2020 Cardiology Research and Practice  
AI techniques such as machine learning and deep learning can also improve medical knowledge due to the increase of the volume and complexity of the data, unlocking clinically relevant information.  ...  interpret data and make clinical decisions.  ...  Conflicts of Interest e authors declare no potential conflicts of interest with respect to the research, authorship and/or publication of this article.  ... 
doi:10.1155/2020/4972346 pmid:32676206 pmcid:PMC7336209 fatcat:bsas334w75co7a33avqkmrcp7m

2020 Index IEEE Transactions on Neural Networks and Learning Systems Vol. 31

2020 IEEE Transactions on Neural Networks and Learning Systems  
Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author's name.  ...  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.  ...  Zhang, J., +, TNNLS May 2020 1616-1625 Where Computation and Dynamics Meet: Heteroclinic Network-Based Controllers in Evolutionary Robotics.  ... 
doi:10.1109/tnnls.2020.3045307 fatcat:34qoykdtarewhdscxqj5jvovqy

A Review on Intrusion Detection System Based on Various Learning Techniques

Shiladitya Raj, Megha Jain, Megha kamble
2021 Indian Journal of Artificial Intelligence and Neural Networking  
IDS is commonly used for the detection and recognition of cyberattacks at the network and host stage, in a timely and automatic manner.  ...  This research assesses the creation of a deep neural network (DNN), a form of deep learning model as well as ELM to detect unpredictable and unpredictable cyber-attacks  ...  DEEP BELIEF NETWORK(DBN) These days, deep architecture in ML is very common.  ... 
doi:10.35940/ijainn.b1013.041221 fatcat:6ngpwsdezjedzntsazrsi3gloi

Deep Learning for Modulation Recognition: a Survey with a Demonstration

Ruolin Zhou, Fugang Liu, Christopher W. Gravelle
2020 IEEE Access  
Specifically, deep learning (DL) has shown overwhelming advantages in computer vision, robotics, and voice recognition.  ...  In this paper, we review a variety of deep learning algorithms and models for modulation recognition and classification of wireless communication signals.  ...  Machine learning (ML) and deep learning (DL) have shown overwhelming advantages in computer vision, robotics, and voice recognition.  ... 
doi:10.1109/access.2020.2986330 fatcat:ywiz6qsseja5lllkqam4pm4xt4


2004 Advances in Fuzzy Systems — Applications and Theory  
What are the most important problems of computational intelligence? A sketch of the road to intelligent systems is presented.  ...  Acknowledgments We would like to thank our expert colleagues who supported this project by sending descriptions of problems that according to them are the most challenging issues in the field of computational  ...  Ambitious theories of high cognitive functions were formalized by John Anderson in his Act* theory [3] , and by Newell and his collaborators in the Soar theory [43] .  ... 
doi:10.1142/9789812562531_0001 fatcat:lk3kdecperbt3aynsbd7jnqsoe
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