REVIEW 3 major objections 5 minor 141 references
Analyzing Cyber-Physical Systems from the Perspective of Artificial Intelligence
T0 review · 3 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash
Pith's one-line read This paper argues that the uncertainty introduced by AI components and unpredictable real-world events makes reinforcement-learning-based analysis a warranted approach for cyber-physical systems, assembling a literature review rather than…
desk verdict A serviceable but sloppy survey whose advertised RL thesis is unsupported; useful as a literature map, not as an argument. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The mechanism carrying the argument is the pairing of the safety/liveness requirement distinction with reinforcement learning's trial-and-error exploration. On the CPS side, requirements are written in temporal logics and checked by model checking or falsification; on the AI side, unknown complex systems are explored by agents that act and learn from reward. The concrete embodiment the paper highlights is Adversarial Resilience Learning (ARL), in which attacker and defender agents share a model of a CPS with only minimal knowledge—the mathematical description of observation and action spaces—and continuously train against each other. This mechanism is what allows the paper to claim that AI-based analysis of a CPS can proceed without full domain-specific knowledge while still producing both attack vectors and defensive strategies.
What would settle it
Run a head-to-head benchmark on a representative set of automotive or power-grid falsification problems in which trained reinforcement-learning agents search for safety violations while classical stochastic falsification tools search the same temporal-logic specifications. If the classical tools consistently find counterexamples faster and with fewer simulator evaluations than the RL agents across several benchmarks, the paper's central claim that AI-introduced uncertainty warrants reinforcement-learning-based analysis would fail in exactly the setting it targets.
Extended reading notes
Core claim
The paper's central claim is that the line between cyber-physical systems and AI is not a gap to be closed but a source of uncertainty to be exploited. Traditional CPS analysis expresses two kinds of requirements—safety ('nothing bad ever happens') and liveness ('something good eventually happens')—and checks systems against them using temporal logic, model checking, contracts, and falsification. The paper argues that neural-network controllers, proactive multi-agent systems, user behavior, weather, and accidents produce inputs and behaviors that escape these classical methods, and that reinforcement learning, which learns by exploring unknown environments, is the natural way to probe such systems. It reviews falsification methods for neural controllers, simulation and co-simulation frameworks, multi-agent consensus and communication protocols, and attack-vector derivation, and points to the authors' Adversarial Resilience Learning (ARL) as a concrete instance: attacker and defender agents, knowing only the mathematical description of each other's sensor and actuator spaces, train against each other on a shared CPS model, with the attacker finding destabilizing actions and the defender learning resilient operating strategies.
Load-bearing premise
The conclusion that AI-based CPS analysis 'will provide a valuable path' rests on the unstated assumption that the surveyed techniques, especially Adversarial Resilience Learning, generalize from small case studies to real, large-scale cyber-physical systems, and the paper offers no quantitative evidence for that generalization.
Editorial extensions
If this is right
- Safety analysis of CPS would shift from proving that no counterexample exists to training agents that search for counterexamples in a high-dimensional behavior space.
- ARL-style attacker–defender training would yield not only discovered attack vectors but also learned defensive operating strategies, coupling vulnerability analysis with resilience.
- The need for verified AI does not disappear; since RL itself introduces uncertainty, provable correctness and learned exploration would have to coexist.
- Simulation and co-simulation frameworks become necessary infrastructure, because reinforcement learning cannot be run as trial-and-error on real critical infrastructure.
- Adversarial examples at the component level and false-data injection at the system level would be treated as the same phenomenon: inputs that fool a monitoring or control mechanism, both amenable to learning-based search.
Reading between the lines
- The paper leaves implicit a concrete benchmark: run reinforcement-learning-based falsification and classical stochastic falsification on the same set of CPS specifications to measure when the extra sample cost of learning buys faster or deeper discovery.
- The inc-dec gaming example suggests an economic extension the paper does not pursue: an attacker–defender RL setup could learn bidding and redispatch strategies in zonal electricity markets, turning a market-design vulnerability into a training ground for economic resilience.
- If ARL truly needs only sensor and actuator descriptions, it should transfer across CPS domains; a direct transfer-learning experiment would test the 'no domain knowledge' claim.
- A negative result in such benchmarks—classical tools consistently finding counterexamples faster—would localize the value of RL to the hardest, least-specified regions of the behavior space rather than the whole analysis pipeline.
Signed reviews
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper is a survey of CPS modelling and analysis written from an AI researcher's perspective. It introduces safety and liveness requirements, formal specification and synthesis, falsification of neural-network controllers, simulation and co-simulation frameworks, multi-agent systems and contracts, and attack-vector derivation for CPS. The conclusion argues that AI-based CPS analysis, especially the authors' Adversarial Resilience Learning (ARL) approach, will be a valuable path, and the abstract asserts that the uncertainty introduced by distributed heuristics, AI components, user perspective, and unpredictable effects is enough to warrant reinforcement-learning-based approaches.
Significance. If its central claim were established, the paper would provide a useful map of the AI/CPS analysis landscape and an argument for increased use of reinforcement learning in CPS analysis. The survey covers a broad and relevant literature, including concrete falsification tools such as Breach, S-TaLiRo, C2E2, and RRT-REX, the distinction between component-level and system-level adversarial testing, co-simulation frameworks such as mosaik, Ptolemy II, and FMI, and power-system attack literature. It is a reasonable entry point for readers new to the field. However, the central RL claim is a position rather than a demonstrated finding: RL appears only in passing in Section 4, and the only concrete mechanism offered, ARL, is described as a 'towards' proposal with no experimental evaluation.
major comments (3)
- [Abstract; Section 8] The abstract's assertion that the surveyed sources of uncertainty 'warrant reinforcement-learning-based approaches' is not supported in the body: RL is mentioned only in passing in Section 4 via the digital-twin idea, and it is not compared or evaluated against the safety-falsification or verification methods surveyed in Sections 3-5. Section 8's 'valuable path' is a research agenda, not an evidenced conclusion. Please either provide concrete evidence for the RL claim or explicitly reframe the abstract and conclusion as stating a hypothesis and research direction.
- [Section 8 versus Section 7] The conclusion claims that AI can be used to analyze a CPS for its safety requirements 'even without CPS domain knowledge required,' but Section 7 states that ARL requires a minimal description of sensors and actuators and that 'the notion of stability is up to the experimenter to define.' Those are domain choices, so the conclusion overstates what the presented method actually assumes; the claim should be qualified accordingly.
- [Section 7] The paper never defines what ARL returns as analysis output or how that output maps back to the safety and liveness requirements introduced in Section 2. Without such a mapping, the paper's central recommendation that ARL-like methods provide a valuable path for CPS safety analysis is not operational. The authors should specify the interface between ARL and requirement-level analysis, or clearly state that this mapping is an open problem.
minor comments (5)
- [Section 1] Several cross-references in the introduction are incorrect: the text refers to 'Section 1' for program derivation, whole-system simulation frameworks, MAS, and attack-vector techniques, but these topics are covered in Sections 3, 5, 6, and 7, respectively.
- [Section 7] In the sentence beginning 'Countermeasures are being taken,' the text refers to 'the previous Section 1'; this should likely refer to Section 4, where the falsification methods are described.
- [Section 2] The section title mis-spells 'Cyber' as 'Cypber'; please correct it.
- [Section 4] The sentence 'All white-box falsification methods currently attack feed-forward ANNs' is a strong universal claim with no supporting citation. Please add a source or qualify the statement to the methods surveyed.
- [References] Reference [31] for the P language lacks venue and year information; complete bibliographic details would help readers.
Circularity Check
No circular derivation: the paper is a survey whose conclusion is an opinionated research agenda, not a result forced by its own definitions or fitted parameters.
full rationale
The paper does not claim to derive a prediction or a first-principles result. It surveys existing CPS analysis methods, from temporal-logic falsification to simulation and multi-agent systems, and then argues that uncertainty in distributed heuristics, AI components, user perspective, and unpredictable effects motivates reinforcement-learning-based approaches. The conclusion that AI-based CPS analysis 'will provide a valuable path' is explicitly framed with 'We also believe', marking it as a stated position rather than a consequence of an equation or a fitted quantity. The main self-cited mechanism, ARL, is introduced in Section 7 via reference [139], whose own title contains 'towards systemic vulnerability analysis', signaling that it is a proposal rather than a validated result; the paper uses it as an illustration for its research agenda, not as a formal premise whose output is re-exported as the conclusion. The other self-citations, such as COHDA, Winzent, and agent-based control work, appear as examples of existing approaches and do not function as load-bearing uniqueness theorems or hidden ansatze. No parameter is fitted and then renamed as a prediction, and no definition is constructed in terms of the claimed outcome. The absence of experimental validation for ARL is a real support gap and a credibility concern, but it is not circularity: the central claim remains an independently grounded opinion about research directions rather than a derivation equivalent to its inputs by construction.
Assumptions & free parameters
assumptions (3)
- standard math Universal approximation theorem for feed-forward neural networks
- domain assumption Temporal logic (MTL/MITL) is the standard formalism for CPS specification
- ad hoc to paper Reinforcement learning is a suitable approach for CPS analysis under uncertainty
Cite this review
Pith. "Pith review of Analyzing Cyber-Physical Systems from the Perspective of Artificial Intelligence." pith.science (2026). https://pith.science/paper/7MXY6UOB
@misc{pith2026190811779,
author = {Pith},
title = {Pith review of: Analyzing Cyber-Physical Systems from the Perspective of Artificial Intelligence},
year = {2026},
howpublished = {\url{https://pith.science/paper/7MXY6UOB}},
note = {Machine review of arXiv:1908.11779}
}
read the original abstract
Principles of modern cyber-physical system (CPS) analysis are based on analytical methods that depend on whether safety or liveness requirements are considered. Complexity is abstracted through different techniques, ranging from stochastic modelling to contracts. However, both distributed heuristics and Artificial Intelligence (AI)-based approaches as well as the user perspective or unpredictable effects, such as accidents or the weather, introduce enough uncertainty to warrant reinforcement-learning-based approaches. This paper compares traditional approaches in the domain of CPS modelling and analysis with the AI researcher perspective to exploring unknown complex systems.
Reference graph
Works this paper leans on
-
[139]
L. Fischer, J.-M. Memmen, E. M. V eith, and M. Tröschel, “Adversarial resilience learning—towards systemic vulnerability analysis for large and complex systems”, in The Ninth International Conference on Smart Grids, Green Communications and IT Energy-aware T echnologies (ENERGY 2019), vol. 9, 2019, pp. 24–32. 16
work page 2019
-
[1]
National Science Foundation, Cyber-physical systems (CPS), 2006
U.S. National Science Foundation, Cyber-physical systems (CPS), 2006
2006
-
[2]
COHDA: A combinatorial optimization heuristic for dis- tributed agents
C. Hinrichs, S. Lehnhoff, and M. Sonnenschein, “COHDA: A combinatorial optimization heuristic for dis- tributed agents”, in International Conference on Agents and Artificial Intellig ence, Springer, 2013, pp. 23– 39
2013
-
[3]
A lightweight distributed software agent for automatic de- mand—supply calculation in smart grids
E. M. V eith, B. Steinbach, and J. Windeln, “A lightweight distributed software agent for automatic de- mand—supply calculation in smart grids”, International Journal On Advances in Internet T echnology , vol. 7, no. 1, pp. 97–113, 2014
2014
-
[4]
An evolutiona ry training algorithm for artificial neural networks with dynamic offspring spread and implicit gradient inform ation
M. Ruppert, E. M. V eith, and B. Steinbach, “An evolutiona ry training algorithm for artificial neural networks with dynamic offspring spread and implicit gradient inform ation”, in The Sixth International Conference on Emerging Network Intelligence (EMERGING 2014) , International Academy, Research, and Industry Associa- tion (IARIA), IARIA XPS Press, 2014
2014
-
[5]
E. M. V eith, Universal smart grid agent for distributed power generatio n management. Logos V erlag Berlin GmbH, 2017
2017
-
[6]
Agent-based power equilib rium in a smart grid with XBOOLE
E. M. V eith and B. Steinbach, “Agent-based power equilib rium in a smart grid with XBOOLE”, in Information and Digital T echnologies 2017, IEEE, 2017
2017
-
[7]
J. M. P . Schumann and Y . Liu, Applications of neural networks in high assurance systems . Springer, 2010, vol. 268
2010
Show all 141 references
-
[8]
Perspect ives on the validation and verification of machine learning systems in the context of highly automated vehicle s
W . Damm, M. Fränzle, S. Gerwinn, and P . Kröger, “Perspect ives on the validation and verification of machine learning systems in the context of highly automated vehicle s”, pp. 512–515, 2018
2018
-
[9]
Alur, Principles of cyber-physical systems
R. Alur, Principles of cyber-physical systems . MIT Press, 2015
2015
-
[10]
On the decidability of metr ic temporal logic
J. Ouaknine and J. Worrell, “On the decidability of metr ic temporal logic”, in 20th Annual IEEE Symposium on Logic in Computer Science (LICS’05) , IEEE, 2005, pp. 188–197
2005
-
[11]
Øhrstrøm and P
P . Øhrstrøm and P . Hasle, Temporal logic: From ancient ideas to artificial intelligen ce. Springer Science & Business Media, 2007, vol. 57
2007
-
[12]
E. M. Clarke Jr, O. Grumberg, D. Kroening, D. Peled, and H . V eith,Model checking. MIT press, 2018
2018
-
[13]
Specification and verific ation of concurrent systems in cesar
J.-P . Queille and J. Sifakis, “Specification and verific ation of concurrent systems in cesar”, in International Symposium on programming, Springer, 1982, pp. 337–351
1982
-
[14]
Fairness and related properties in transition sys tems—a temporal logic to deal with fairness
——, “Fairness and related properties in transition sys tems—a temporal logic to deal with fairness”, Acta Informatica, vol. 19, no. 3, pp. 195–220, 1983
1983
-
[15]
Breach, a toolbox for verification and parame ter synthesis of hybrid systems
A. Donzé, “Breach, a toolbox for verification and parame ter synthesis of hybrid systems”, in International Conference on Computer Aided V erification , Springer, 2010, pp. 167–170
2010
-
[16]
S-TaLiRo: A tool for temporal logic falsi- fication for hybrid systems
Y . Annpureddy, C. Liu, G. Fainekos, and S. Sankaranaray anan, “S-TaLiRo: A tool for temporal logic falsi- fication for hybrid systems”, in International Conference on T ools and Algorithms for the Co nstruction and Analysis of Systems, Springer, 2011, pp. 254–257
2011
-
[17]
C2E2: A verification tool for stateflow models
P . S. Duggirala, S. Mitra, M. Viswanathan, and M. Potok, “C2E2: A verification tool for stateflow models”, in International Conference on T ools and Algorithms for the Co nstruction and Analysis of Systems , Springer, 2015, pp. 68–82
2015
-
[18]
Efficient guiding strategies for testing of temporal properties of hybrid systems
T. Dreossi, T. Dang, A. Donzé, J. Kapinski, X. Jin, and J. V . Deshmukh, “Efficient guiding strategies for testing of temporal properties of hybrid systems”, in NASA F ormal Methods Symposium, Springer, 2015, pp. 127–142
2015
-
[19]
Design and synthesis of s ynchronization skeletons using branching time temporal logic
E. M. Clarke and E. A. Emerson, “Design and synthesis of s ynchronization skeletons using branching time temporal logic”, in W orkshop on Logic of Programs, Springer, 1981, pp. 52–71
1981
-
[20]
Frege’s logic, theorem, and foundations f or arithmetic
E. N. Zalta, “Frege’s logic, theorem, and foundations f or arithmetic”, 1998
1998
-
[21]
Frege, Die Grundlagen der Arithmetik: Eine logisch-mathematisch e Untersuchung über den Begriff der Zahl (Breslau: W
G. Frege, Die Grundlagen der Arithmetik: Eine logisch-mathematisch e Untersuchung über den Begriff der Zahl (Breslau: W . Koebner , 1884). Translated as The F oundations of Arithmetic: A Logico-mathematical En- quiry into the Concept of Number by JL Austin , J. L. Austin, Ed. ...
1950
-
[22]
Wille, Gottlob Frege: Begriffsschrift, eine der arithmetischen n achgebildete F ormelsprache des reinen Denkens
M. Wille, Gottlob Frege: Begriffsschrift, eine der arithmetischen n achgebildete F ormelsprache des reinen Denkens. Springer-V erlag, 2018
2018
-
[23]
Russell and A
B. Russell and A. N. Whitehead, Principia mathematica. Cambridge University Press, 1978. 11 Analyzing Cyber-Physical Systems from the Perspective of A rtificial Intelligence A PREPRINT
1978
-
[24]
Skolem, “Logico-combinatorical investigations in the satisfiability or provabilitiy of mathematical proposi - tions: A simplified proof of a theorem by L
T. Skolem, “Logico-combinatorical investigations in the satisfiability or provabilitiy of mathematical proposi - tions: A simplified proof of a theorem by L. Löwenheim and gene ralizations of the theorem”, in From Frege to Gödel: A source book in mathematical logic, 1879-1931 , ...
1931
-
[25]
Badesa, The birth of model theory: Löwenheim’s theorem in the frame o f the theory of relatives
C. Badesa, The birth of model theory: Löwenheim’s theorem in the frame o f the theory of relatives . Princeton University Press, 2009
2009
-
[26]
How number theory got the best of the pentium c hip
B. Cipra, “How number theory got the best of the pentium c hip”, Science, vol. 267, no. 5195, pp. 175–176, 1995
1995
-
[27]
Introduction to HOL: A theorem proving environment for higher order logic
M. Gordon and T. Melham, “Introduction to HOL: A theorem proving environment for higher order logic”, 1993
1993
-
[28]
Pvs: A prototype v erification system
S. Owre, J. M. Rushby, and N. Shankar, “Pvs: A prototype v erification system”, in International Conference on Automated Deduction, Springer, 1992, pp. 748–752
1992
-
[29]
Kaufmann, P
M. Kaufmann, P . Manolios, and J. S. Moore, Computer-aided reasoning: Acl2 case studies . Springer Science & Business Media, 2013, vol. 4
2013
-
[30]
Combining model checking and runtime verification for safe robotics
A. Desai, T. Dreossi, and S. A. Seshia, “Combining model checking and runtime verification for safe robotics”, S. Lahiri and G. Reger, Eds., Chambridge, MA, USA: Springer I nternational Publishing, 2017, pp. 172–189, ISBN : 9783319675312. DOI : 10.1007/978-3-319-67531-2
2017 doi
-
[31]
P: Safe asynchro nous event-driven programming
A. Desai, E. Jackson, and D. Zufferey, “P: Safe asynchro nous event-driven programming”,
-
[32]
Towards V erifi ed Artificial Intelligence
S. A. Seshia, D. Sadigh, and S. S. Sastry, “Towards V erifi ed Artificial Intelligence”, pp. 1–13, 2016. arXiv: 1606.08514. [Online]. Available: http://arxiv.org/abs/1606.08514
2016 arXiv
-
[33]
A theory of formal synthesis via inductive learning
S. Jha and S. A. Seshia, “A theory of formal synthesis via inductive learning”, Acta Informatica , vol. 54, no. 7, pp. 693–726, 2017, ISSN : 14320525. DOI : 10.1007/s00236-017-0294-5
2017 doi
-
[34]
Language identification in the limit
E. M. Gold, “Language identification in the limit”, Information and control, vol. 10, no. 5, pp. 447–474, 1967
1967
-
[35]
E. Y . Shapiro, Algorithmic program debugging, ser. ACM Distinguished Dissertation. MIT press, 1982
1982
-
[36]
A methodology for lisp program construc tion from examples
P . D. Summers, “A methodology for lisp program construc tion from examples”, Journal of the ACM (JACM) , vol. 24, no. 1, pp. 161–175, 1977
1977
-
[37]
Inductive inference: Theor y and methods
D. Angluin and C. H. Smith, “Inductive inference: Theor y and methods”, ACM Computing Surveys (CSUR) , vol. 15, no. 3, pp. 237–269, 1983
1983
-
[38]
S. J. Russell and P . Norvig, Artificial intelligence: A modern approach . Pearson Education Limited, 2016
2016
-
[39]
Minin g Requirements From Closed-Loop Control Models
X. Jin, A. Donzé, J. V . Deshmukh, and S. A. Seshia, “Minin g Requirements From Closed-Loop Control Models”, IEEE Transactions on Computer-Aided Design of Integrated C ircuits and Systems , vol. 34, no. 11, pp. 1704–1717, 2015, ISSN : 02780070. DOI : 10.1109/TCAD.2015.2421907
2015
-
[40]
Backpropa- gation Applied to Handwritten Zip Code Recognition
Y . LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howa rd, W . Hubbard, and L. D. Jackel, “Backpropa- gation Applied to Handwritten Zip Code Recognition”, Neural Computation, vol. 1, no. 4, pp. 541–551, 1989, ISSN : 0899-7667. DOI : 10.1162/neco.1989.1.4.541
1989 doi
-
[41]
Deep, big, simple neural nets for handwrit- ten digit recognition
D. C. Cire¸ san, U. Meier, L. M. Gambardella, and J. Schmi dhuber, “Deep, big, simple neural nets for handwrit- ten digit recognition”, Neural computation, vol. 22, no. 12, pp. 3207–3220, 2010
2010
-
[42]
Deep neural netwo rks are easily fooled: High confidence pre- dictions for unrecognizable images
A. Nguyen, J. Y osinski, and J. Clune, “Deep neural netwo rks are easily fooled: High confidence pre- dictions for unrecognizable images”, in Proceedings of the IEEE conference on computer vision and pattern recognition , Dec. 2015, pp. 427–436. DOI : 10.1080/04580630600872547....
2015 doi
-
[43]
Explainin g and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explainin g and harnessing adversarial examples”, pp. 1–11, 2014. arXiv: 1412.6572. [Online]. Available: http://arxiv.org/abs/1412.6572
2014 arXiv
-
[44]
Practi- cal Black-Box Attacks against Machine Learning
N. Papernot, P . McDaniel, I. Goodfellow, S. Jha, Z. B. Ce lik, and A. Swami, “Practi- cal Black-Box Attacks against Machine Learning”, Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security - ASIA C CS ’17 , pp. 506–519,
2017
-
[45]
The limitations of deep learning in adversarial settings
N. Papernot, P . Mcdaniel, S. Jha, M. Fredrikson, Z. B. Ce lik, and A. Swami, “The limitations of deep learning in adversarial settings”, 2016. arXiv: arXiv:1511.07528v1
2016 arXiv
-
[46]
ZOO: Zeroth order optimiza- tion based black-box attacks to deep neural networks withou t training substitute mod- els
P .-Y . Chen, H. Zhang, Y . Sharma, J. Yi, and C.-J. Hsieh, “ ZOO: Zeroth order optimiza- tion based black-box attacks to deep neural networks withou t training substitute mod- els”, 2017. DOI : 10.1145/3128572.3140448. arXiv: 1708.03999. [Online]. Available: http://arxiv.org/...
2017
-
[47]
DeepXplore: Automa ted whitebox testing of deep learn- ing systems
K. Pei, Y . Cao, J. Y ang, and S. Jana, “DeepXplore: Automa ted whitebox testing of deep learn- ing systems”, 2017. DOI : 10.1145/3132747.3132785. arXiv: 1705.06640. [Online]. Available: http://arxiv.org/abs/1705.06640%7B%5C%%7D0Ahttp://dx.doi.org/10.1145/3132747.3132785
2017
-
[48]
DeepTest: Automate d testing of deep-neural-network-driven autonomous cars
Y . Tian, K. Pei, S. Jana, and B. Ray, “DeepTest: Automate d testing of deep-neural-network-driven autonomous cars”, 2017. arXiv: 1708.08559. [Online]. Available: http://arxiv.org/abs/1708.08559
2017 arXiv
-
[49]
AI2: Safety and robust- ness certification of neural networks with abstract interpr etation
T. Gehr, M. Mirman, D. Drachsler-Cohen, P . Tsankov, S. C haudhuri, and M. V echev, “AI2: Safety and robust- ness certification of neural networks with abstract interpr etation”, in 2018 IEEE Symposium on Security and Privacy (SP), IEEE, 2018, pp. 1–18
2018
-
[50]
Sabour, N
S. Sabour, N. Frosst, and G. E. Hinton, “Capsule”, in 31st Conference on Neural Information Processing Systems (NIPS 2017) , 2017, pp. 3856–3866. DOI : 10.1177/1535676017742133
2017 doi
-
[51]
Capsule network performance on complex data
E. Xi, S. Bing, and Y . Jin, “Capsule network performance on complex data”, ArXiv preprint arXiv:1712.03480, 2017
2017 arXiv
-
[52]
An optimization view on dynamic rout ing between capsules
D. Wang and Q. Liu, “An optimization view on dynamic rout ing between capsules”, in International Confer- ence on Learning Representations (ICLR) , 2018, pp. 1–4
2018
-
[53]
Multilayer fe edforward networks are universal approximators
K. Hornik, M. Stinchcombe, and H. White, “Multilayer fe edforward networks are universal approximators”, Neural networks, vol. 2, no. 5, pp. 359–366, 1989
1989
-
[54]
Approximation by superpositions of a sigm oidal function
G. Cybenko, “Approximation by superpositions of a sigm oidal function”, Mathematics of control, signals and systems, vol. 2, no. 4, pp. 303–314, 1989
1989
-
[55]
Compositional f alsification of cyber-physical systems with machine learning components
T. Dreossi, A. Donzé, and S. A. Seshia, “Compositional f alsification of cyber-physical systems with machine learning components”, Lecture Notes in Computer Science (including subseries Lec ture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , vol. 10227 L...
2017 arXiv
-
[56]
Über die Gleichverteilung von Zahlen mod. ein s
H. Weyl, “Über die Gleichverteilung von Zahlen mod. ein s”, Mathematische Annalen, vol. 77, no. 3, pp. 313– 352, 1916
1916
-
[57]
Pointwise ergodic theore ms via harmonic analysis
J. Rosenblatt and M. Wierdl, “Pointwise ergodic theore ms via harmonic analysis”, in Proc. Conference on Ergodic Theory (Alexandria, Egypt, 1993), London Mathemat ical Society Lecture Notes, 1995, pp. 3–151
1993
-
[58]
Gray-box adversarial tes ting for control systems with machine learning compo- nents
S. Y aghoubi and G. Fainekos, “Gray-box adversarial tes ting for control systems with machine learning compo- nents”, vol. 1, no. 1, pp. 179–184, 2019. DOI : 10.1145/3302504.3311814. arXiv: arXiv:1812.11958v1
2019
-
[59]
Functi onal gradient descent method for metric temporal logic specifications
H. Abbas, A. Winn, G. Fainekos, and A. A. Julius, “Functi onal gradient descent method for metric temporal logic specifications”, in 2014 American Control Conference, IEEE, 2014, pp. 2312–2317
2014
-
[60]
Multiple shooting, cegar-based falsification for hybrid systems
A. Zutshi, J. V . Deshmukh, S. Sankaranarayanan, and J. K apinski, “Multiple shooting, cegar-based falsification for hybrid systems”, in Proceedings of the 14th International Conference on Embedd ed Software, ACM, 2014, p. 5
2014
-
[61]
Optimal control b ased falsification of unknown systems with time delays: A gasoline engine A/F ratio control case study
N. Li, A. Girard, and I. Kolmanovsky, “Optimal control b ased falsification of unknown systems with time delays: A gasoline engine A/F ratio control case study”, 201 7
-
[62]
A structure by which a recur rent neural network can approximate a nonlinear dynamic system
D. R. Seidl and R. D. Lorenz, “A structure by which a recur rent neural network can approximate a nonlinear dynamic system”, in IJCNN-91-Seattle International Joint Conference on Neura l Networks, vol. ii, Jul. 1991, 709–714 vol.2. DOI : 10.1109/IJCNN.1991.155422
1991
-
[63]
On the computational power of neural nets
H. T. Siegelmann and E. D. Sontag, “On the computational power of neural nets”, Journal of computer and system sciences, vol. 50, no. 1, pp. 132–150, 1995
1995
-
[64]
F. M. Bianchi, E. Maiorino, M. C. Kampffmeyer, A. Rizzi, and R. Jenssen, Recurrent neural networks for short-term load forecasting: An overview and comparative a nalysis. Springer, 2017
2017
-
[65]
Finding structure in time
J. L. Elman, “Finding structure in time”, Cognitive science, vol. 14, no. 2, pp. 179–211, 1990
1990
-
[66]
Learn- ing phrase representations using rnn encoder-decoder for s tatistical machine translation
K. Cho, B. V an Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y . Bengio, “Learn- ing phrase representations using rnn encoder-decoder for s tatistical machine translation”, ArXiv preprint arXiv:1406.1078, 2014
2014 arXiv
-
[67]
Long short-term mem ory
S. Hochreiter and J. Schmidhuber, “Long short-term mem ory”, Neural computation , vol. 9, no. 8, pp. 1735– 1780, 1997
1997
-
[68]
Customer experience challenges: Bringing together digital, physic al and social realms
R. N. Bolton, J. R. McColl-Kennedy, L. Cheung, A. Gallan , C. Orsingher, L. Witell, and M. Zaki, “Customer experience challenges: Bringing together digital, physic al and social realms”, Journal of Service Management, vol. 29, no. 5, pp. 776–808, 2018
2018
-
[69]
Playing atari with deep reinforcement learning
V . Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antono glou, D. Wierstra, and M. Riedmiller, “Playing atari with deep reinforcement learning”, ArXiv preprint arXiv:1312.5602 , 2013
2013 arXiv
-
[70]
Hybrid computing using a neural network with dynamic exte rnal memory
A. Graves, G. Wayne, M. Reynolds, T. Harley, I. Danihelk a, A. Grabska-Barwi ´nska, S. G. Colmenarejo, E. Grefenstette, T. Ramalho, J. Agapiou, et al., “Hybrid computing using a neural network with dynamic exte rnal memory”, Nature, vol. 538, no. 7626, p. 471, 2016. 13 Analyzi...
2016
-
[71]
Sim ulation-based Adversarial Test Generation for Autonomous V ehicles with Machine Learning Components
C. E. Tuncali, G. Fainekos, H. Ito, and J. Kapinski, “Sim ulation-based Adversarial Test Generation for Autonomous V ehicles with Machine Learning Components” , IEEE Intelligent V ehicles Symposium, Proceedings, vol. 2018-June, no. Iv, pp. 1555–1562, 2018. DOI : 10.1109/IVS.20...
2018
-
[72]
Safe At Any Speed: A Simula tion-Based Test Harness for Autonomous
M. O. Kelly and A. Rodionova, “Safe At Any Speed: A Simula tion-Based Test Harness for Autonomous”, Cyber Physical Systems. Design, Modeling, and Evaluation, vol. 11267, no. October, pp. 94–106, 2017. [Online]. Avail able: http://link.springer.com/10.1007/978-3-030-17910-6% 7B...
2017 doi
-
[73]
A simulation study system atization
E. C. Lobao and A. J. V . Porto, “A simulation study system atization”, in Proceedings of the XVII ENEGEP — National Congress of Industrial Engineering , Gramado, Rio Grande do Sul, Brazil, 1997
1997
-
[74]
Simulation projects: Building the right conceptual model
S. Robinson, “Simulation projects: Building the right conceptual model”, Industrial Engineering-Norcross , vol. 26, no. 9, pp. 34–36, 1994
1994
-
[75]
Robinson, Simulation — the practice of model development and use
S. Robinson, Simulation — the practice of model development and use . Chichester, United Kingdom: John Wiley & Sons, 2004
2004
-
[76]
Co-simulation: State of the art
C. Gomes, C. Thule, D. Broman, P . G. Larsen, and H. V anghe luwe, “Co-simulation: State of the art”, ArXiv preprint arXiv:1702.00686, 2017
2017 arXiv
-
[77]
On conce ptual structuration and coupling methods of co- simulation frameworks in cyber-physical energy system val idation
V . Nguyen, Y . Besanger, Q. Tran, and T. Nguyen, “On conce ptual structuration and coupling methods of co- simulation frameworks in cyber-physical energy system val idation”, Energies, vol. 10, no. 12, p. 1977, 2017
1977
-
[78]
Cosimulation of intelligent power sys- tems: Fundamentals, software architecture, numerics, and coupling
P . Palensky, A. A. V an Der Meer, C. D. Lopez, A. Joseph, an d K. Pan, “Cosimulation of intelligent power sys- tems: Fundamentals, software architecture, numerics, and coupling”, IEEE Industrial Electronics Magazine , vol. 11, no. 1, pp. 34–50, 2017
2017
-
[79]
Mosaik-a modular platform for the evalua- tion of agent-based smart grid control
S. Rohjans, S. Lehnhoff, S. Schütte, S. Scherfke, and S. Hussain, “Mosaik-a modular platform for the evalua- tion of agent-based smart grid control”, in IEEE PES ISGT Europe 2013 , IEEE, 2013, pp. 1–5
2013
-
[80]
Ptolemaeus, System design, modeling, and simulation: Using ptolemy ii
C. Ptolemaeus, System design, modeling, and simulation: Using ptolemy ii. Ptolemy. org Berkeley, 2014, vol. 1
2014
-
[81]
Functional mockup interface 2.0: The standard for tool in dependent exchange of simula- tion models
T. Blochwitz, M. Otter, J. Akesson, M. Arnold, C. Clauss , H. Elmqvist, M. Friedrich, A. Junghanns, J. Mauss, D. Neumerkel, et al., “Functional mockup interface 2.0: The standard for tool in dependent exchange of simula- tion models”, in Proceedings of the 9th International MO...
2012
-
[82]
ENABLE-S3: Testing & V alidation of Highly Automat ed Systems
A. Leitner, A. Akkermann, B. A. Hjøllo, B. Wirtz, D. Nick ovic, E. Möhlmann, H. Holzer, J. ven der V oet, J. Niehaus, M. Sarrazin, M. Zofka, M. Rooker, M. Paulweber, M . Siegel, M. Rautila, N. Marko, P . Tummelt- shammer, P . Rosenberger, R. Rott, S. Muckenhuber, S. Kalisv aar...
2019
-
[83]
Wooldridge, An introduction to multiagent systems
M. Wooldridge, An introduction to multiagent systems . John Wiley & Sons, 2009
2009
-
[84]
Bussmann, N
S. Bussmann, N. R. Jennings, and M. Wooldridge, Multiagent systems for manufacturing control: A design methodology. Springer Science & Business Media, 2013
2013
-
[85]
Market- based self-organized provision of active power and ancilla ry services: An agent-based approach for smart distribution grids
A. Nieße, S. Lehnhoff, M. Tröschel, M. Uslar, C. Wissing , H.-J. Appelrath, and M. Sonnenschein, “Market- based self-organized provision of active power and ancilla ry services: An agent-based approach for smart distribution grids”, in 2012 Complexity in Engineering (COMPENG)....
2012
-
[86]
Approach for ancil lary service provision by decentralized energy de- vices with the focus on load frequency control
R. Schwerdfeger and D. Westermann, “Approach for ancil lary service provision by decentralized energy de- vices with the focus on load frequency control”, 2014
2014
-
[88]
Multi-agent systems for power engineering applic ations—part I: Concepts, approaches, and technical challenges
——, “Multi-agent systems for power engineering applic ations—part I: Concepts, approaches, and technical challenges”, in IEEE Transactions on Power Systems , vol. 22, 2007, pp. 1743–1752, ISBN : 0885-8950 VO -
2007
-
[89]
Multiagent traffic management : An improved intersection control mechanism
K. Dresner and P . Stone, “Multiagent traffic management : An improved intersection control mechanism”, in Proceedings of the fourth international joint conference o n Autonomous agents and multiagent systems, ACM, 2005, pp. 471–477
2005
-
[90]
DOI : 10.1109/TPWRS.2007.908471
2007
-
[91]
The chaotic nature of TCP congesti on control
A. V eres and M. Boda, “The chaotic nature of TCP congesti on control”, in Proceedings IEEE INFOCOM
-
[92]
A collaborative driving sy stem based on multiagent modelling and simulations
S. Hallé and B. Chaib-draa, “A collaborative driving sy stem based on multiagent modelling and simulations”, Transportation Research Part C: Emerging T echnologies, vol. 13, no. 4, pp. 320–345, 2005
2005
-
[93]
Efficient algorithms for distributed snap shots and global virtual time approximation
F. Mattern, “Efficient algorithms for distributed snap shots and global virtual time approximation”,
-
[94]
Consensus and Cooperation in Networked Multi-Agent Sys- tems
R. Olfati-Saber, J. A. Fax, and R. M. Murray, “Consensus and Cooperation in Networked Multi-Agent Sys- tems”, Proceedings of the IEEE , vol. 95, no. 1, pp. 215–233, 2007
2007
-
[95]
Distributed snapshots: de termining global states of distributed systems
K. M. Chandy and L. Lamport, “Distributed snapshots: de termining global states of distributed systems”, ACM Transactions on Computer Systems, 1985, ISSN : 07342071. DOI : 10.1145/214451.214456
1985
-
[96]
The distributed negotiation of egalitarian resource allocations
P .-A. Matt, F. Toni, and D. Dionysiou, “The distributed negotiation of egalitarian resource allocations”, in Proceedings of the 1st international workshop on computati onal social choice (COMSOC06) , 2006, pp. 304– 316
2006
-
[97]
Negotiation decision functions for autonomous agents
P . Faratin, C. Sierra, and N. R. Jennings, “Negotiation decision functions for autonomous agents”, Robotics and Autonomous Systems, vol. 24, no. 3-4, pp. 159–182, 1998
1998
-
[98]
Protocol moderator s as active middle-agents in multi-agent systems
C. Hanachi and C. Sibertin-Blanc, “Protocol moderator s as active middle-agents in multi-agent systems”, Au- tonomous Agents and Multi-Agent Systems , vol. 8, no. 2, pp. 131–164, 2004
2004
-
[99]
An Agent-Based Approach for Dy namic Manufacturing Scheduling
W . Shen and D. H. Norrie, “An Agent-Based Approach for Dy namic Manufacturing Scheduling”, in W orkshop Notes of the Agent-Based Manufacturing W orkshop, Autonomous Agents ’98 , 1998
1998
-
[100]
Host extensions for IP multicasting
S. Deering, “Host extensions for IP multicasting”, RF C Editor, RFC 1112, Aug. 1989, pp. 1–17. [Online]. Available: https://www.rfc-editor.org/rfc/rfc1112.txt
1989
-
[101]
The contract net protocol: High-level com munication and control in a distributed problem solver
R. G. Smith, “The contract net protocol: High-level com munication and control in a distributed problem solver”, IEEE Transactions on computers, no. 12, pp. 1104–1113, 1980
1980
-
[102]
Periodic event-t riggered synchronization of linear multi-agent systems with communication delays
E. Garcia, Y . Cao, and D. W . Casbeer, “Periodic event-t riggered synchronization of linear multi-agent systems with communication delays”, IEEE Transactions on Automatic Control , vol. 62, no. 1, pp. 366–371, 2017, ISSN : 00189286. DOI : 10.1109/TAC.2016.2555484. arXiv: arXi...
2017
-
[103]
Collective dynamics of ‘small-world’networks
D. J. Watts and S. H. Strogatz, “Collective dynamics of ‘small-world’networks”, Nature, vol. 393, no. 6684, p. 440, 1998
1998
-
[104]
A protocol for multi-a gent negotiation in a group-choice decision mak- ing process
T. Wanyama and B. Homayoun Far, “A protocol for multi-a gent negotiation in a group-choice decision mak- ing process”, Journal of Network and Computer Applications , vol. 30, no. 3, pp. 1173–1195, 2007, ISSN : 10848045. DOI : 10.1016/j.jnca.2006.04.009
2007 doi
-
[105]
Ultrafast consensus in small-worl d networks
R. Olfati-Saber, “Ultrafast consensus in small-worl d networks”, in Proceedings of the 2005, American Control Conference, 2005., IEEE, 2005, pp. 2371–2378
2005
-
[106]
On local minima i n distributed energy scheduling
A. Nieße, J. Bremer, and S. Lehnhoff, “On local minima i n distributed energy scheduling.”, in FedCSIS Posi- tion Papers, 2017, pp. 61–68
2017
-
[107]
Fast linear iterations for distri buted averaging
L. Xiao and S. Boyd, “Fast linear iterations for distri buted averaging”, Systems & Control Letters, vol. 53, no. 1, pp. 65–78, 2004
2004
-
[108]
Applying “design by contract
B. Meyer, “Applying “design by contract””, Computer, vol. 10, no. 25, pp. 40–51, 1992
1992
-
[109]
Guarded commands, nondeterminacy, a nd formal derivation of programs
E. W . Dijkstra, “Guarded commands, nondeterminacy, a nd formal derivation of programs”, in Programming Methodology, Springer, 1978, pp. 166–175
1978
-
[110]
Controlled self-organizat ion in smart grids
A. Nieße and M. Tröschel, “Controlled self-organizat ion in smart grids”, in 2016 IEEE International Sympo- sium on Systems Engineering (ISSE) , IEEE, 2016, pp. 1–6
2016
-
[111]
Contracts, games, and re finement
R.-J. Back and J. von Wright, “Contracts, games, and re finement”, Information and Computation, vol. 156, no. 1-2, pp. 25–45, 2000
2000
-
[112]
Back and J
R.-J. Back and J. Wright, Refinement calculus: A systematic introduction . Springer Science & Business Media, 2012
2012
-
[113]
Win and sin: Predicate transformers for c oncurrency
L. Lamport, “Win and sin: Predicate transformers for c oncurrency”, ACM Transactions on Programming Lan- guages and Systems, vol. 12, no. 3, pp. 396–428, 1990
1990
-
[114]
Hierarchical models of synchronous circuit s
D. Dill, “Hierarchical models of synchronous circuit s”, in International Conference on Concurrency Theory , Springer, 1994, pp. 161–161
1994
-
[115]
Interface automata
L. De Alfaro and T. A. Henzinger, “Interface automata” , in ACM SIGSOFT Software Engineering Notes, ACM, vol. 26, 2001, pp. 109–120
2001
-
[116]
D. L. Dill, Trace theory for automatic hierarchical verification of spe ed-independent circuits. MIT press Cam- bridge, MA, 1989, vol. 24
1989
-
[117]
Passerone and A
R. Passerone and A. L. Sangiovanni-Vincentelli, Semantic foundations for heterogeneous systems . University of California, Berkeley, 2004. 15 Analyzing Cyber-Physical Systems from the Perspective of A rtificial Intelligence A PREPRINT
2004
-
[118]
Overcoming heterophobia: Modeling concurrency in heterogeneous systems
J. Burch, R. Passerone, and A. L. Sangiovanni-Vincent elli, “Overcoming heterophobia: Modeling concurrency in heterogeneous systems”, in Proceedings Second International Conference on Applicati on of Concurrency to System Design , IEEE, 2001, pp. 13–32
2001
-
[119]
Process spaces
R. Negulescu, “Process spaces”, in International Conference on Concurrency Theory, Springer, 2000, pp. 199– 213
2000
-
[120]
An inte grated trust and reputation model for open multi- agent systems
T. D. Huynh, N. R. Jennings, and N. R. Shadbolt, “An inte grated trust and reputation model for open multi- agent systems”, Autonomous Agents and Multi-Agent Systems , vol. 13, no. 2, pp. 119–154, 2006
2006
-
[121]
Fuzz y trust evaluation and credibility development in multi-agent systems
S. Schmidt, R. Steele, T. S. Dillon, and E. Chang, “Fuzz y trust evaluation and credibility development in multi-agent systems”, Applied Soft Computing , vol. 7, no. 2, pp. 492–505, 2007
2007
-
[122]
Competitive Contract Net Protocol
J. V ok ˇrínek, J. Bíba, J. Hodík, J. Vybíhal, and M. Pˇechouˇcek, “Competitive Contract Net Protocol”, no. 027169, pp. 656–668, 2007. DOI : 10.1007/978-3-540-69507-3_57
2007 doi
-
[123]
Autofuzz: Automated n etwork protocol fuzzing framework
S. Gorbunov and A. Rosenbloom, “Autofuzz: Automated n etwork protocol fuzzing framework”, IJCSNS, vol. 10, no. 8, p. 239, 2010
2010
-
[124]
Power system state estimation residual analysis: An algorithm using network topology
K. Clements, G. Krumpholz, and P . Davis, “Power system state estimation residual analysis: An algorithm using network topology”, IEEE Transactions on Power Apparatus and Systems , no. 4, pp. 1779–1787, 1981
1981
-
[125]
Understanding how image qualit y affects deep neural networks
S. Dodge and L. Karam, “Understanding how image qualit y affects deep neural networks”, in 2016 Eighth International Conference on Quality of Multimedia E xperience (QoMEX) , Jun. 2016, pp. 1–6. DOI : 10.1109/QoMEX.2016.7498955
2016
-
[126]
On secu rity indices for state estimators in power networks
H. Sandberg, A. Teixeira, and K. H. Johansson, “On secu rity indices for state estimators in power networks”, in First W orkshop on Secure Control Systems (SCS), Stockholm, 2010, 2010
2010
-
[127]
Cyber security analysis of state estima- tors in electric power systems
A. Teixeira, S. Amin, H. Sandberg, K. H. Johansson, and S. S. Sastry, “Cyber security analysis of state estima- tors in electric power systems”, Proceedings of the IEEE Conference on Decision and Control, pp. 5991–5998, 2010, ISSN : 01912216. DOI : 10.1109/CDC.2010.5717318
2010
-
[128]
Detection of topology errors by state estimation (power systems)
F. F. Wu and W .-H. Liu, “Detection of topology errors by state estimation (power systems)”, IEEE Transactions on Power Systems, vol. 4, no. 1, pp. 176–183, 1989
1989
-
[129]
Distri bution system state estimation using an artificial neural network approach for pseudo measurement modeling
E. Manitsas, R. Singh, B. C. Pal, and G. Strbac, “Distri bution system state estimation using an artificial neural network approach for pseudo measurement modeling”, IEEE Transactions on Power Systems , vol. 27, no. 4, pp. 1888–1896, 2012
2012
-
[130]
False data injection attacks against state estimation in electric power grids
Y . Liu, P . Ning, and M. K. Reiter, “False data injection attacks against state estimation in electric power grids”, ACM Transactions on Information and System Security (TISSE C), vol. 14, no. 1, p. 13, 2011
2011
-
[131]
Abur and A
A. Abur and A. G. Exposito, Power system state estimation: Theory and implementation . CRC press, 2004
2004
-
[132]
Automated vulnerability analysis of ac state estimation under constrained false data injection in elect ric power systems
S. Gao, L. Xie, A. Solar-Lezama, D. Serpanos, and H. Shr obe, “Automated vulnerability analysis of ac state estimation under constrained false data injection in elect ric power systems”, in Proceedings of the IEEE Conference on Decision and Control , vol. 54, IEEE, 2015, pp. 2...
2015
-
[133]
State estimation under false data injection attacks: Security analysis and system protection
L. Hu, Z. Wang, Q.-L. Han, and X. Liu, “State estimation under false data injection attacks: Security analysis and system protection”, Automatica, vol. 87, pp. 176–183, 2018
2018
-
[134]
Adversarial attacks to distributed v oltage control in power distribution networks with DERs
P . Ju and X. Lin, “Adversarial attacks to distributed v oltage control in power distribution networks with DERs”, in Proceedings of the Ninth International Conference on Futur e Energy Systems , ACM, 2018, pp. 291–302, ISBN : 9781450357678. DOI : 10.1145/3208903.3208912
2018
-
[135]
Real-time d ata reassurance in electrical power systems based on artificial neural networks
S. Mousavian, J. V alenzuela, and J. Wang, “Real-time d ata reassurance in electrical power systems based on artificial neural networks”, Electric Power Systems Research, vol. 96, pp. 285–295, 2013
2013
-
[136]
De tection of fault data injection attack on uav using adaptive neural network
A. Abbaspour, K. K. Y en, S. Noei, and A. Sargolzaei, “De tection of fault data injection attack on uav using adaptive neural network”, Procedia computer science, vol. 95, pp. 193–200, 2016
2016
-
[137]
Real-time detection of false data injection attacks in smart grid: A deep learning-based intelligent mechanism
Y . He, G. J. Mendis, and J. Wei, “Real-time detection of false data injection attacks in smart grid: A deep learning-based intelligent mechanism”, IEEE Transactions on Smart Grid , vol. 8, no. 5, pp. 2505–2516, 2017
2017
-
[138]
Empirics of intrada y and real-time markets in Europe: Great Britain
C. Konstantinidis and G. Strbac, “Empirics of intrada y and real-time markets in Europe: Great Britain”, DIW – Deutsches Institut für Wirtschaftsforschung, Berlin, Ge rmany, Tech. Rep., 2015, p. 21
2015
-
[140]
Market-Based Redispatch in Zonal Electricity Markets
L. Hirth and I. Schlecht, “Market-Based Redispatch in Zonal Electricity Markets”, SSRN Electronic Journal , no. 055, 2018. DOI : 10.2139/ssrn.3286798
2018 doi
-
[2000]
Nineteenth A nnual Joint Conference of the IEEE Computer and Communications Societies (Cat
Conference on Computer Communications. Nineteenth A nnual Joint Conference of the IEEE Computer and Communications Societies (Cat. No. 00CH37064) , IEEE, vol. 3, 2000, pp. 1715–1723. 14 Analyzing Cyber-Physical Systems from the Perspective of A rtificial Intelligence A PREPRINT
2000
-
[2017]
arXiv: arXiv:1602.02697v4
DOI : 10.1145/3052973.3053009. arXiv: arXiv:1602.02697v4. [Online]. Available: http://dl.acm.org/citation.cfm?doid=3052973.3053009
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