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Guest editorial: special issue on multi-robot and multi-agent systems

Nora Ayanian, Paolo Robuffo Giordano, Robert Fitch, Antonio Franchi, Lorenzo Sabattini
2020 Autonomous Robots  
Future robots will form a backbone of pervasive, efficient networks of taskable agents-but, crucially, these will be systems of communicating, cooperative devices.  ...  This special issue presents contributions that describe state-of-the-art results in distributed robotics and multi-agent systems research as applicable to autonomous robot systems.  ...  The paper "Efficient recursive distributed state estimation of hidden Markov models over unreliable networks" by Amirhossein Tamjidi, Reza Oftadeh, Suman Chakravorty, and Dylan Shell presents a recursive  ... 
doi:10.1007/s10514-020-09908-x fatcat:ty4zagmoa5bolojtkwg3gekenm

Algorithms for Hidden Markov Models with Imprecisely Specified Parameters

Denis Deratani Maua, Cassio Polpo de Campos, Alessandro Antonucci
2014 2014 Brazilian Conference on Intelligent Systems  
Hidden Markov models (HMMs) are widely used models for sequential data.  ...  We present a generalized version of HMMs, whose quantification can be done by sets of, instead of single, probability distributions.  ...  Hidden Markov models (HMMs) are widely used generative probabilistic models of sequential data that assume observations to be produced from a chain of hidden (i.e., unobserved) states [1] .  ... 
doi:10.1109/bracis.2014.42 dblp:conf/bracis/MauaCA14 fatcat:uonsdi3vmrha5fltq676zqod3u

Probabilistic QoS Analysis of Web Services [chapter]

Waseem Ahmed, Yong Wei Wu
2013 Lecture Notes in Computer Science  
In this paper, we propose a novel method for QoS metrification based on Hidden Markov Models.  ...  We demonstrate the feasibility and usefulness of our methodology by drawing experiments on real world data.  ...  I am extremely grateful to Cesar Roberto de Souza (Federal University of Sao Carlos) and for his support, encouragement & proofreading of the draft version of my paper.  ... 
doi:10.1007/978-3-642-40820-5_33 fatcat:rkqzlv6d55ewfe52yfyt6lcewq

Layered Coding of Hidden Markov Sources [article]

Mehdi Salehifar, Tejaswi Nanjundaswamy, Kenneth Rose
2018 arXiv   pre-print
A new fundamental source coding approach for HMS is proposed, based on tracking an estimate of the state probability distribution, and is shown to be optimal.  ...  The paper studies optimal coding of hidden Markov sources (HMS), which represent a broad class of practical sources obtained through noisy acquisition processes, beside their explicit modeling use in speech  ...  ACKNOWLEDGMENT The authors would like to thank NSF funding under the code of NSF-CCF-1016861 and NSF-CCF-1320599.  ... 
arXiv:1802.02709v1 fatcat:ooog32l7p5eqxd5i7spbz3wdzm

Identification Using Feedforward Networks

Asriel U. Levin, Kumpati S. Narendra
1995 Neural Computation  
Script recognition using hidden Markov models. Proc. ICASSP’86 2071-2074. Niles, L. T., and Silverman, H. F. 1990. Combining hidden Markov model and neural network classifiers. Proc.  ...  - sarathy 1990) have suggested the use of recursive neural network models of the form?  ... 
doi:10.1162/neco.1995.7.2.349 fatcat:bf7l27j4hvd5nbat3dckezle4y

Comparison of the Beta and the Hidden Markov Models of Trust in Dynamic Environments [chapter]

Marie E. G. Moe, Bjarne E. Helvik, Svein J. Knapskog
2009 IFIP Advances in Information and Communication Technology  
In this paper we present a comparison of our proposed hidden Markov trust model to the Beta reputation system.  ...  We show that the hidden Markov trust model performs better when it comes to the detection of changes in behavior of agents, due to its larger richness in model features.  ...  Hidden Markov Modeling A hidden Markov model (HMM) consists of a finite set of N hidden states S = {s 1 ,...,s N } with an associated probability distribution.  ... 
doi:10.1007/978-3-642-02056-8_18 fatcat:qgcsqcv27nduzl3dfjwcxfojvy

A review on prognostic techniques for non-stationary and non-linear rotating systems

Man Shan Kan, Andy C.C. Tan, Joseph Mathew
2015 Mechanical systems and signal processing  
The general concept of these techniques, the pros and cons of applying these methods, as well as their applications in the research field are discussed.  ...  The field of prognostics has attracted significant interest from the research community in recent times.  ...  Hidden Markov model (HMM) and hidden semi-Markov model (HSMM) Hidden Markov model (HMM) is a statistical approach based on the principle of Markov chains for modelling signals that evolve through a finite  ... 
doi:10.1016/j.ymssp.2015.02.016 fatcat:rlg2fd26yzhh5ivusfb5vppopi

2020 Index IEEE Transactions on Cybernetics Vol. 50

2020 IEEE Transactions on Cybernetics  
., Reference Trajectory Reshaping Optimi-zation and Control of Robotic Exoskeletons for Human-Robot Co-Manipulation; TCYB Aug. 2020 3740-3751 Wu, X., Jiang, B., Yu, K., Miao, c., and Chen, H  ...  ., +, TCYB May 2020 1887-1899 Hidden Markov Model-Based Nonfragile State Estimation of Switched Neu- ral Network With Probabilistic Quantized Outputs.  ...  ., +, TCYB Sept. 2020 4098-4109 Hidden Markov Model-Based Nonfragile State Estimation of Switched Neu- ral Network With Probabilistic Quantized Outputs.  ... 
doi:10.1109/tcyb.2020.3047216 fatcat:5giw32c2u5h23fu4drupnh644a

On the quantitative analysis of deep belief networks

Ruslan Salakhutdinov, Iain Murray
2008 Proceedings of the 25th international conference on Machine learning - ICML '08  
Deep Belief Networks (DBN's) are generative models that contain many layers of hidden variables.  ...  We further show how an AIS estimator, along with approximate inference, can be used to estimate a lower bound on the log-probability that a DBN model with multiple hidden layers assigns to the test data  ...  Iain Murray is supported by the government of Canada.  ... 
doi:10.1145/1390156.1390266 dblp:conf/icml/SalakhutdinovM08 fatcat:xklszjo5tre4xn5lu5zjaobsuu

Detection of bursts in extracellular spike trains using hidden semi-Markov point process models

Surya Tokdar, Peiyi Xi, Ryan C. Kelly, Robert E. Kass
2009 Journal of Computational Neuroscience  
When more general probability distributions are used to describe the state transitions the two-state point process model becomes a hidden semi-Markov model (HSMM).  ...  If in (2) the transitions from non-burst to burst and burst to non-burst states are memoryless, this becomes a hidden Markov model (HMM).  ...  This algorithm uses a fast forward-backward recursion to perform maximum likelihood estimation of model parameters and conditional probability evaluation of the hidden state given the estimated parameters  ... 
doi:10.1007/s10827-009-0182-2 pmid:19697116 fatcat:pplfr62msnhopgk2yhp7i574yy

A review on statistical inference methods for discrete Markov random fields [article]

Julien Stoehr
2017 arXiv   pre-print
Developing satisfactory methodology for the analysis of Markov random field is a very challenging task.  ...  This report gives an overview of some of the methods used in the literature to analyse such observed or unobserved random fields.  ...  Over the past decade, only few works have addressed the model choice issue for hidden Markov random field from that BIC perspective.  ... 
arXiv:1704.03331v1 fatcat:tvtbv46qcjfpzkxtwstb7elw3q

Learning Latent Tree Graphical Models [article]

Myung Jin Choi, Vincent Y. F. Tan, Animashree Anandkumar, Alan S. Willsky
2010 arXiv   pre-print
We compare the proposed algorithms to other methods by performing extensive numerical experiments on various latent tree graphical models such as hidden Markov models and star graphs.  ...  This results in more accurate and efficient learning of latent trees. We also present regularized versions of our algorithms that learn latent tree approximations of arbitrary distributions.  ...  The distribution p (over W and Markov on some tree in T ≥3 ) is said to be a minimal tree extension of p V .  ... 
arXiv:1009.2722v1 fatcat:etibrvcw65acjlm4rp6dqta73y

Rational thoughts in neural codes

Zhengwei Wu, Minhae Kwon, Saurabh Daptardar, Paul Schrater, Xaq Pitkow
2020 Proceedings of the National Academy of Sciences of the United States of America  
Complex behaviors are often driven by an internal model, which integrates sensory information over time and facilitates long-term planning to reach subjective goals.  ...  We also provide a framework for interpreting encoding, recoding, and decoding of neural data in light of this rational model for behavior.  ...  This belief state can be expressed recursively using the Markov property as a function of its previous value (SI Appendix, Eq. 1).  ... 
doi:10.1073/pnas.1912336117 pmid:33229521 fatcat:iuyse2aypzhhlhwr3kos6p7qhi

Sequential Anomaly Detection in Wireless Sensor Networks and Effects of Long-Range Dependent Data

Shanshan Zheng, John S. Baras
2012 Sequential Analysis  
In our work, we build a wavelet-domain multilevel hidden Markov model for the LRD network traffic.  ...  To reduce the effect of LRD on anomaly detection performance, we proposed a wavelet-domain hidden Markov model for capturing the normal network traffic.  ... 
doi:10.1080/07474946.2012.719435 fatcat:7opt2vayevgcjhpgwp2huc3kcy

Rational thoughts in neural codes [article]

Zhengwei Wu, Minhae Kwon, Saurabh Daptardar, Paul R Schrater, Xaq S Pitkow
2019 bioRxiv   pre-print
Complex behaviors are often driven by an internal model, which integrates sensory information over time and facilitates long-term planning to reach subjective goals.  ...  We also provide a framework for interpreting encoding, recoding and decoding of neural data in light of this rational model for behavior.  ...  Nonetheless it can be calculated using the Markov property of the POMDP: the actions and observations constitute a Markov chain where the agent's belief state is a hidden variable.  ... 
doi:10.1101/765867 fatcat:yxro2yve7zehle5hvemm6h4dtm
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