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Learning Probabilistic Automata for Model Checking

Hua Mao, Yingke Chen, Manfred Jaeger, Thomas D. Nielsen, Kim G. Larsen, Brian Nielsen
<span title="">2011</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/mr2re5en4rajnjdlytdvj35iyy" style="color: black;">2011 Eighth International Conference on Quantitative Evaluation of SysTems</a> </i> &nbsp;
We adapt algorithms for learning finite probabilistic automata from observed system behaviors.  ...  Thus, we can perform PLTL model-checking on the learned model to infer properties of the system. We perform experiments learning models from system observations at different levels of abstraction.  ...  In this paper we show that methods for learning probabilistic finite automata [5] , [6] , [8] can be adapted for the task of learning Markov Chain system models for verification.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/qest.2011.21">doi:10.1109/qest.2011.21</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/qest/MaoCJNLN11.html">dblp:conf/qest/MaoCJNLN11</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/nakxdm27xzbvjmb25mdogt2yjy">fatcat:nakxdm27xzbvjmb25mdogt2yjy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170808145144/http://people.cs.aau.dk/~tdn/papers/qest11_final_version.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ff/a0/ffa0274b96896cddc5f064f4163df59f175f1c51.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/qest.2011.21"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Introduction to the Special Issue on Grammatical Inference

Jeffrey Heinz, C. de la Higuera, Tim Oates
<span title="2013-12-14">2013</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/h4nnd7sxwzcwhetu5qkjbcdh6u" style="color: black;">Machine Learning</a> </i> &nbsp;
Thus we hope this special issue is of interest to the readership of Machine Learning.  ...  As a field, Grammatical Inference addresses both theoretical and empirical learning problems, and the collection of papers within this special issue attests both to the diversity of these problems as well  ...  In 2012 the PAutomaC competition took place, with the goal of learning probabilistic finite automata and hidden Markov models.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s10994-013-5428-6">doi:10.1007/s10994-013-5428-6</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/b5ehf6qurvcp5nhex4bjw7w3vm">fatcat:b5ehf6qurvcp5nhex4bjw7w3vm</a> </span>
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Automated Learning of Probabilistic Assumptions for Compositional Reasoning [chapter]

Lu Feng, Marta Kwiatkowska, David Parker
<span title="">2011</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
We do so using algorithmic learning techniques, which have already proved to be successful for the generation of assumptions for compositional verification of non-probabilistic systems.  ...  Probabilistic verification techniques have been applied to the formal modelling and analysis of a wide range of systems, from communication protocols such as Bluetooth, to nanoscale computing devices,  ...  ] for probabilistic automata [35, 36] , a natural model for compositional reasoning.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-642-19811-3_2">doi:10.1007/978-3-642-19811-3_2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ghpdevjwxndgrhjo35deeqxgeq">fatcat:ghpdevjwxndgrhjo35deeqxgeq</a> </span>
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Compositional Stochastic Model Checking Probabilistic Automata via Assume-guarantee Reasoning

Yang Liu, Rui Li
<span title="">2020</span> <i title="Atlantis Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/mitejrukingkdiyzijpbo7msre" style="color: black;">International Journal of Networked and Distributed Computing (IJNDC)</a> </i> &nbsp;
i i i M i ⋅ ∈ ∧ = ∧ = ∑ 1 2    ( ( ) t i (5) Model Checking for Probabilistic Automata Here, we concentrate on action-based properties over PAs, defined regarding their traces.  ...  We can obtain an adversary s ∈ Adv M1||M2||···||Mn , such that Pr PG Figure 5 | 5 (a) Probabilistic automata M 1 , (b) Probabilistic automata M 2 , (c) Probabilistic automata M 3 and (d) DFA P err for  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.2991/ijndc.k.190918.001">doi:10.2991/ijndc.k.190918.001</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/55ee6cvpp5btfdox3qg7qezyci">fatcat:55ee6cvpp5btfdox3qg7qezyci</a> </span>
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Assume-Guarantee Verification for Probabilistic Systems [chapter]

Marta Kwiatkowska, Gethin Norman, David Parker, Hongyang Qu
<span title="">2010</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
In addition, the compositional verification method is efficient and fully automated, based on a reduction to the problem of multi-objective probabilistic model checking.  ...  model checked, in PRISM. † Results are maximum probabilities of error so actual values are these subtracted from 1.  ...  Our tool is based on the probabilistic model checker PRISM [11] , which already supports LTL model checking of probabilistic automata.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-642-12002-2_3">doi:10.1007/978-3-642-12002-2_3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/kzcdr7obtjf2fexu66vrlpfp3y">fatcat:kzcdr7obtjf2fexu66vrlpfp3y</a> </span>
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Improving Probabilistic Automata Learning with Additional Knowledge [chapter]

Christopher Kermorvant, Colin de la Higuera, Pierre Dupont
<span title="">2004</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
In this paper, we propose a way of incorporating additional knowledge in probabilistic automata inference, by using typed automata.  ...  The inference of typed automata with statistically labeled data provides language models competitive with state-of-the-art n-grams on the Air Travel Information System (ATIS) task.  ...  We have compared the use of two kinds of labeling for probabilistic typed automata inference.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-540-27868-9_27">doi:10.1007/978-3-540-27868-9_27</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/w5wtsgrdbzaq3k6hn372fsokcq">fatcat:w5wtsgrdbzaq3k6hn372fsokcq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190502210250/https://link.springer.com/content/pdf/10.1007%2F978-3-540-27868-9_27.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/c7/02/c7027f44f720bacca4695ee4744ffeb057e74949.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-540-27868-9_27"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Leveraging Weighted Automata in Compositional Reasoning about Concurrent Probabilistic Systems

Fei He, Xiaowei Gao, Bow-Yaw Wang, Lijun Zhang
<span title="">2015</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2qaxtqe2rfgjnfefqcokcnzelq" style="color: black;">Proceedings of the 42nd Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages - POPL &#39;15</a> </i> &nbsp;
We propose the first sound and complete learning-based compositional verification technique for probabilistic safety properties on concurrent systems where each component is an Markov decision process.  ...  Since weighted assumptions can be implicitly represented by multi-terminal binary decision diagrams (MTBDD's), we give an L * -based learning algorithm for MTBDD's to infer weighted assumptions.  ...  Probabilistic Model Checking for MDP's Fix a finite set AP of atomic propositions.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2676726.2676998">doi:10.1145/2676726.2676998</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/popl/HeGWZ15.html">dblp:conf/popl/HeGWZ15</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/kqf3rs4y7bel3iarxicy2wsewu">fatcat:kqf3rs4y7bel3iarxicy2wsewu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170809135259/http://lcs.ios.ac.cn/~zhanglj/papers/popl15.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/c5/45/c545d0327999c2b93daba4b27b064b32fa1b9d76.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2676726.2676998"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

Leveraging Weighted Automata in Compositional Reasoning about Concurrent Probabilistic Systems

Fei He, Xiaowei Gao, Bow-Yaw Wang, Lijun Zhang
<span title="2015-01-14">2015</span> <i title="Association for Computing Machinery (ACM)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/xu5bk2lj5rbdxlx6222nw7tsxi" style="color: black;">SIGPLAN notices</a> </i> &nbsp;
We propose the first sound and complete learning-based compositional verification technique for probabilistic safety properties on concurrent systems where each component is an Markov decision process.  ...  Since weighted assumptions can be implicitly represented by multi-terminal binary decision diagrams (MTBDD's), we give an L * -based learning algorithm for MTBDD's to infer weighted assumptions.  ...  Probabilistic Model Checking for MDP's Fix a finite set AP of atomic propositions.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2775051.2676998">doi:10.1145/2775051.2676998</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rttoe2x6pzcxxfoxuubj5kgouu">fatcat:rttoe2x6pzcxxfoxuubj5kgouu</a> </span>
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Compositional Verification of Probabilistic Systems Using Learning

Lu Feng, Marta Kwiatkowska, David Parker
<span title="">2010</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/mr2re5en4rajnjdlytdvj35iyy" style="color: black;">2010 Seventh International Conference on the Quantitative Evaluation of Systems</a> </i> &nbsp;
Our approach builds upon a recently proposed assume-guarantee framework for probabilistic automata, in which assumptions and guarantees are probabilistic safety properties, represented using finite automata  ...  To overcome this, we propose a novel learning technique based on the L* algorithm, which automatically generates probabilistic assumptions using the results of queries executed by a probabilistic model  ...  Probabilistic Automata Probabilistic automata [1] , [11] model systems whose behaviour is both probabilistic and nondeterministic. Definition 1.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/qest.2010.24">doi:10.1109/qest.2010.24</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/qest/FengKP10.html">dblp:conf/qest/FengKP10</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/kretrqvlcjcwnmrdraan42rni4">fatcat:kretrqvlcjcwnmrdraan42rni4</a> </span>
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Mungojerrie: Reinforcement Learning of Linear-Time Objectives [article]

Ernst Moritz Hahn, Mateo Perez, Sven Schewe, Fabio Somenzi, Ashutosh Trivedi, Dominik Wojtczak
<span title="2021-06-18">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The tool contains reinforcement learning algorithms and a probabilistic model checker. Mungojerrie supports models specified in PRISM and ω-automata specified in HOA.  ...  Mungojerrie (https://plv.colorado.edu/mungojerrie/) is a tool for testing reward schemes for ω-regular objectives on finite models.  ...  These automata can then be directly composed with MDPs for model checking or used to produce rewards for learning.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2106.09161v2">arXiv:2106.09161v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/k7jvqed2wzebfbthfg2zwhxzo4">fatcat:k7jvqed2wzebfbthfg2zwhxzo4</a> </span>
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Page 5239 of Mathematical Reviews Vol. , Issue 98H [page]

<span title="">1998</span> <i title="American Mathematical Society"> <a target="_blank" rel="noopener" href="https://archive.org/details/pub_mathematical-reviews" style="color: black;">Mathematical Reviews </a> </i> &nbsp;
: application to model-checking (135-150); Nadia Busi and G.  ...  Weimar, Cellular automata for reaction- diffusion systems (1699-1715 i Most of the papers are being reviewed individually. } pp. tt and 98h:68007 68-06 68T0S 68T30 * Computational learning and probabilistic  ... 
<span class="external-identifiers"> </span>
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Modeling time series of climatic parameters with probabilistic finite automata

L. Mora-López, J. Mora, R. Morales-Bueno, M. Sidrach-de-Cardona
<span title="">2005</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/irxlz6wefjcmnddydjot2v3uii" style="color: black;">Environmental Modelling &amp; Software</a> </i> &nbsp;
The first problem in most models from machine learning is that they are developed for discrete values; however, most phenomena in nature are continuous.  ...  This model includes the following three steps: dynamic discretization of continuous values, construction of probabilistic finite automata and prediction of new series with randomness.  ...  For example, the machine-learning models based on probabilistic finite automata have been used to model several types of natural sequences.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.envsoft.2004.04.007">doi:10.1016/j.envsoft.2004.04.007</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/iqlr4vhc5falnnhxnpgk5vqmke">fatcat:iqlr4vhc5falnnhxnpgk5vqmke</a> </span>
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Probabilistic Bisimulation for Parameterized Systems [chapter]

Chih-Duo Hong, Anthony W. Lin, Rupak Majumdar, Philipp Rümmer
<span title="">2019</span> <i title="Springer International Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
Moreover, with the help of standard automata learning algorithms, we show that the candidate relations can be synthesized fully automatically, making the verification fully automated.  ...  We work in the framework of regular model checking, and specify an infinitestate system as a regular relation described by a first-order formula over a universal automatic structure, i.e., a logical theory  ...  The framework is amenable to automata-theoretic algorithms in the spirit of regular model checking [3, 13] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-030-25540-4_27">doi:10.1007/978-3-030-25540-4_27</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xqlxhtqbhjdclk3n3g5airbaeq">fatcat:xqlxhtqbhjdclk3n3g5airbaeq</a> </span>
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Statistical Models Of Network Traffic

Barath Kumar, Oliver Niggemann, Juergen Jasperneite
<span title="2010-01-29">2010</span> <i title="Zenodo"> Zenodo </i> &nbsp;
The main contribution of this paper is the introduction of a model formalism called 'probabilistic regression automaton' suitable for the tasks mentioned above.  ...  the model, (iii) While the system is running, the model could be used to diagnose non-normal system behavior.  ...  The learned model has then been used to analyze the network behavior: • Worst-case timing has been checked for signal transmissions.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5281/zenodo.1055518">doi:10.5281/zenodo.1055518</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rdkst7haazal3m437ysl5qzpma">fatcat:rdkst7haazal3m437ysl5qzpma</a> </span>
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From software verification to 'everyware' verification

Marta Kwiatkowska
<span title="2013-09-07">2013</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/vdv2cjwoijecjpy4gktx7ptxuq" style="color: black;">Computer Science - Research and Development</a> </i> &nbsp;
Our increasing dependence on ubiquitous computing creates an urgent need for modelling and verification technologies to support the design process, and hence improve the reliability and reduce production  ...  In this paper we describe the objectives and initial progress of the research aimed at extending the capabilities of quantitative, probabilistic verification to challenging ubiquitous computing scenarios  ...  Model checking for quantitative specifications draws on techniques for conventional model checking, such as graph-theoretic algorithms, symbolic approaches and automata-theoretic methods, but these must  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s00450-013-0249-1">doi:10.1007/s00450-013-0249-1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/yijv5tfqwjb5rlopsnmy6gpsf4">fatcat:yijv5tfqwjb5rlopsnmy6gpsf4</a> </span>
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