---
_id: '13142'
abstract:
- lang: eng
text: Reinforcement learning has received much attention for learning controllers
of deterministic systems. We consider a learner-verifier framework for stochastic
control systems and survey recent methods that formally guarantee a conjunction
of reachability and safety properties. Given a property and a lower bound on the
probability of the property being satisfied, our framework jointly learns a control
policy and a formal certificate to ensure the satisfaction of the property with
a desired probability threshold. Both the control policy and the formal certificate
are continuous functions from states to reals, which are learned as parameterized
neural networks. While in the deterministic case, the certificates are invariant
and barrier functions for safety, or Lyapunov and ranking functions for liveness,
in the stochastic case the certificates are supermartingales. For certificate
verification, we use interval arithmetic abstract interpretation to bound the
expected values of neural network functions.
acknowledgement: This work was supported in part by the ERC-2020-AdG 101020093, ERC
CoG 863818 (FoRM-SMArt) and the European Union’s Horizon 2020 research and innovation
programme under the Marie Skłodowska-Curie Grant Agreement No. 665385.
alternative_title:
- LNCS
article_processing_charge: No
author:
- first_name: Krishnendu
full_name: Chatterjee, Krishnendu
id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-4561-241X
- first_name: Thomas A
full_name: Henzinger, Thomas A
id: 40876CD8-F248-11E8-B48F-1D18A9856A87
last_name: Henzinger
orcid: 0000-0002-2985-7724
- first_name: Mathias
full_name: Lechner, Mathias
id: 3DC22916-F248-11E8-B48F-1D18A9856A87
last_name: Lechner
- first_name: Dorde
full_name: Zikelic, Dorde
id: 294AA7A6-F248-11E8-B48F-1D18A9856A87
last_name: Zikelic
citation:
ama: 'Chatterjee K, Henzinger TA, Lechner M, Zikelic D. A learner-verifier framework
for neural network controllers and certificates of stochastic systems. In: Tools
and Algorithms for the Construction and Analysis of Systems . Vol 13993. Springer
Nature; 2023:3-25. doi:10.1007/978-3-031-30823-9_1'
apa: 'Chatterjee, K., Henzinger, T. A., Lechner, M., & Zikelic, D. (2023). A
learner-verifier framework for neural network controllers and certificates of
stochastic systems. In Tools and Algorithms for the Construction and Analysis
of Systems (Vol. 13993, pp. 3–25). Paris, France: Springer Nature. https://doi.org/10.1007/978-3-031-30823-9_1'
chicago: Chatterjee, Krishnendu, Thomas A Henzinger, Mathias Lechner, and Dorde
Zikelic. “A Learner-Verifier Framework for Neural Network Controllers and Certificates
of Stochastic Systems.” In Tools and Algorithms for the Construction and Analysis
of Systems , 13993:3–25. Springer Nature, 2023. https://doi.org/10.1007/978-3-031-30823-9_1.
ieee: K. Chatterjee, T. A. Henzinger, M. Lechner, and D. Zikelic, “A learner-verifier
framework for neural network controllers and certificates of stochastic systems,”
in Tools and Algorithms for the Construction and Analysis of Systems ,
Paris, France, 2023, vol. 13993, pp. 3–25.
ista: 'Chatterjee K, Henzinger TA, Lechner M, Zikelic D. 2023. A learner-verifier
framework for neural network controllers and certificates of stochastic systems.
Tools and Algorithms for the Construction and Analysis of Systems . TACAS: Tools
and Algorithms for the Construction and Analysis of Systems, LNCS, vol. 13993,
3–25.'
mla: Chatterjee, Krishnendu, et al. “A Learner-Verifier Framework for Neural Network
Controllers and Certificates of Stochastic Systems.” Tools and Algorithms for
the Construction and Analysis of Systems , vol. 13993, Springer Nature, 2023,
pp. 3–25, doi:10.1007/978-3-031-30823-9_1.
short: K. Chatterjee, T.A. Henzinger, M. Lechner, D. Zikelic, in:, Tools and Algorithms
for the Construction and Analysis of Systems , Springer Nature, 2023, pp. 3–25.
conference:
end_date: 2023-04-27
location: Paris, France
name: 'TACAS: Tools and Algorithms for the Construction and Analysis of Systems'
start_date: 2023-04-22
date_created: 2023-06-18T22:00:47Z
date_published: 2023-04-22T00:00:00Z
date_updated: 2023-06-19T08:30:54Z
day: '22'
ddc:
- '000'
department:
- _id: KrCh
- _id: ToHe
doi: 10.1007/978-3-031-30823-9_1
ec_funded: 1
file:
- access_level: open_access
checksum: 3d8a8bb24d211bc83360dfc2fd744307
content_type: application/pdf
creator: dernst
date_created: 2023-06-19T08:29:30Z
date_updated: 2023-06-19T08:29:30Z
file_id: '13150'
file_name: 2023_LNCS_Chatterjee.pdf
file_size: 528455
relation: main_file
success: 1
file_date_updated: 2023-06-19T08:29:30Z
has_accepted_license: '1'
intvolume: ' 13993'
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
month: '04'
oa: 1
oa_version: Published Version
page: 3-25
project:
- _id: 0599E47C-7A3F-11EA-A408-12923DDC885E
call_identifier: H2020
grant_number: '863818'
name: 'Formal Methods for Stochastic Models: Algorithms and Applications'
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
publication: 'Tools and Algorithms for the Construction and Analysis of Systems '
publication_identifier:
eissn:
- 1611-3349
isbn:
- '9783031308222'
issn:
- 0302-9743
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: A learner-verifier framework for neural network controllers and certificates
of stochastic systems
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 13993
year: '2023'
...
---
_id: '12787'
abstract:
- lang: eng
text: "Populations evolve in spatially heterogeneous environments. While a certain
trait might bring a fitness advantage in some patch of the environment, a different
trait might be advantageous in another patch. Here, we study the Moran birth–death
process with two types of individuals in a population stretched across two patches
of size N, each patch favouring one of the two types. We show that the long-term
fate of such populations crucially depends on the migration rate μ\r\n between
the patches. To classify the possible fates, we use the distinction between polynomial
(short) and exponential (long) timescales. We show that when μ is high then one
of the two types fixates on the whole population after a number of steps that
is only polynomial in N. By contrast, when μ is low then each type holds majority
in the patch where it is favoured for a number of steps that is at least exponential
in N. Moreover, we precisely identify the threshold migration rate μ⋆ that separates
those two scenarios, thereby exactly delineating the situations that support long-term
coexistence of the two types. We also discuss the case of various cycle graphs
and we present computer simulations that perfectly match our analytical results."
acknowledgement: J.S. and K.C. acknowledge support from the ERC CoG 863818 (ForM-SMArt)
article_number: '20220685'
article_processing_charge: No
article_type: original
author:
- first_name: Jakub
full_name: Svoboda, Jakub
id: 130759D2-D7DD-11E9-87D2-DE0DE6697425
last_name: Svoboda
- first_name: Josef
full_name: Tkadlec, Josef
id: 3F24CCC8-F248-11E8-B48F-1D18A9856A87
last_name: Tkadlec
orcid: 0000-0002-1097-9684
- first_name: Kamran
full_name: Kaveh, Kamran
last_name: Kaveh
- first_name: Krishnendu
full_name: Chatterjee, Krishnendu
id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-4561-241X
citation:
ama: 'Svoboda J, Tkadlec J, Kaveh K, Chatterjee K. Coexistence times in the Moran
process with environmental heterogeneity. Proceedings of the Royal Society
A: Mathematical, Physical and Engineering Sciences. 2023;479(2271). doi:10.1098/rspa.2022.0685'
apa: 'Svoboda, J., Tkadlec, J., Kaveh, K., & Chatterjee, K. (2023). Coexistence
times in the Moran process with environmental heterogeneity. Proceedings of
the Royal Society A: Mathematical, Physical and Engineering Sciences. The
Royal Society. https://doi.org/10.1098/rspa.2022.0685'
chicago: 'Svoboda, Jakub, Josef Tkadlec, Kamran Kaveh, and Krishnendu Chatterjee.
“Coexistence Times in the Moran Process with Environmental Heterogeneity.” Proceedings
of the Royal Society A: Mathematical, Physical and Engineering Sciences. The
Royal Society, 2023. https://doi.org/10.1098/rspa.2022.0685.'
ieee: 'J. Svoboda, J. Tkadlec, K. Kaveh, and K. Chatterjee, “Coexistence times in
the Moran process with environmental heterogeneity,” Proceedings of the Royal
Society A: Mathematical, Physical and Engineering Sciences, vol. 479, no.
2271. The Royal Society, 2023.'
ista: 'Svoboda J, Tkadlec J, Kaveh K, Chatterjee K. 2023. Coexistence times in the
Moran process with environmental heterogeneity. Proceedings of the Royal Society
A: Mathematical, Physical and Engineering Sciences. 479(2271), 20220685.'
mla: 'Svoboda, Jakub, et al. “Coexistence Times in the Moran Process with Environmental
Heterogeneity.” Proceedings of the Royal Society A: Mathematical, Physical
and Engineering Sciences, vol. 479, no. 2271, 20220685, The Royal Society,
2023, doi:10.1098/rspa.2022.0685.'
short: 'J. Svoboda, J. Tkadlec, K. Kaveh, K. Chatterjee, Proceedings of the Royal
Society A: Mathematical, Physical and Engineering Sciences 479 (2023).'
date_created: 2023-04-02T22:01:09Z
date_published: 2023-03-29T00:00:00Z
date_updated: 2023-08-01T13:58:34Z
day: '29'
ddc:
- '000'
department:
- _id: KrCh
doi: 10.1098/rspa.2022.0685
ec_funded: 1
external_id:
isi:
- '000957125500002'
file:
- access_level: open_access
checksum: 13953d349fbefcb5d21ccc6b303297eb
content_type: application/pdf
creator: dernst
date_created: 2023-04-03T06:25:29Z
date_updated: 2023-04-03T06:25:29Z
file_id: '12796'
file_name: 2023_ProceedingsRoyalSocietyA_Svoboda.pdf
file_size: 827784
relation: main_file
success: 1
file_date_updated: 2023-04-03T06:25:29Z
has_accepted_license: '1'
intvolume: ' 479'
isi: 1
issue: '2271'
language:
- iso: eng
month: '03'
oa: 1
oa_version: Published Version
project:
- _id: 0599E47C-7A3F-11EA-A408-12923DDC885E
call_identifier: H2020
grant_number: '863818'
name: 'Formal Methods for Stochastic Models: Algorithms and Applications'
publication: 'Proceedings of the Royal Society A: Mathematical, Physical and Engineering
Sciences'
publication_identifier:
eissn:
- 1471-2946
issn:
- 1364-5021
publication_status: published
publisher: The Royal Society
quality_controlled: '1'
related_material:
link:
- relation: research_data
url: https://doi.org/10.6084/m9.figshare.21261771.v1
scopus_import: '1'
status: public
title: Coexistence times in the Moran process with environmental heterogeneity
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 479
year: '2023'
...
---
_id: '12861'
abstract:
- lang: eng
text: The field of indirect reciprocity investigates how social norms can foster
cooperation when individuals continuously monitor and assess each other’s social
interactions. By adhering to certain social norms, cooperating individuals can
improve their reputation and, in turn, receive benefits from others. Eight social
norms, known as the “leading eight," have been shown to effectively promote the
evolution of cooperation as long as information is public and reliable. These
norms categorize group members as either ’good’ or ’bad’. In this study, we examine
a scenario where individuals instead assign nuanced reputation scores to each
other, and only cooperate with those whose reputation exceeds a certain threshold.
We find both analytically and through simulations that such quantitative assessments
are error-correcting, thus facilitating cooperation in situations where information
is private and unreliable. Moreover, our results identify four specific norms
that are robust to such conditions, and may be relevant for helping to sustain
cooperation in natural populations.
acknowledgement: 'This work was supported by the European Research Council CoG 863818
(ForM-SMArt) (to K.C.) and the European Research Council Starting Grant 850529:
E-DIRECT (to C.H.). L.S. received additional partial support by the Austrian Science
Fund (FWF) under grant Z211-N23 (Wittgenstein Award), and also thanks the support
by the Stochastic Analysis and Application Research Center (SAARC) under National
Research Foundation of Korea grant NRF-2019R1A5A1028324. The authors additionally
thank Stefan Schmid for providing access to his lab infrastructure at the University
of Vienna for the purpose of collecting simulation data.'
article_number: '2086'
article_processing_charge: No
article_type: original
author:
- first_name: Laura
full_name: Schmid, Laura
id: 38B437DE-F248-11E8-B48F-1D18A9856A87
last_name: Schmid
orcid: 0000-0002-6978-7329
- first_name: Farbod
full_name: Ekbatani, Farbod
last_name: Ekbatani
- first_name: Christian
full_name: Hilbe, Christian
id: 2FDF8F3C-F248-11E8-B48F-1D18A9856A87
last_name: Hilbe
orcid: 0000-0001-5116-955X
- first_name: Krishnendu
full_name: Chatterjee, Krishnendu
id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-4561-241X
citation:
ama: Schmid L, Ekbatani F, Hilbe C, Chatterjee K. Quantitative assessment can stabilize
indirect reciprocity under imperfect information. Nature Communications.
2023;14. doi:10.1038/s41467-023-37817-x
apa: Schmid, L., Ekbatani, F., Hilbe, C., & Chatterjee, K. (2023). Quantitative
assessment can stabilize indirect reciprocity under imperfect information. Nature
Communications. Springer Nature. https://doi.org/10.1038/s41467-023-37817-x
chicago: Schmid, Laura, Farbod Ekbatani, Christian Hilbe, and Krishnendu Chatterjee.
“Quantitative Assessment Can Stabilize Indirect Reciprocity under Imperfect Information.”
Nature Communications. Springer Nature, 2023. https://doi.org/10.1038/s41467-023-37817-x.
ieee: L. Schmid, F. Ekbatani, C. Hilbe, and K. Chatterjee, “Quantitative assessment
can stabilize indirect reciprocity under imperfect information,” Nature Communications,
vol. 14. Springer Nature, 2023.
ista: Schmid L, Ekbatani F, Hilbe C, Chatterjee K. 2023. Quantitative assessment
can stabilize indirect reciprocity under imperfect information. Nature Communications.
14, 2086.
mla: Schmid, Laura, et al. “Quantitative Assessment Can Stabilize Indirect Reciprocity
under Imperfect Information.” Nature Communications, vol. 14, 2086, Springer
Nature, 2023, doi:10.1038/s41467-023-37817-x.
short: L. Schmid, F. Ekbatani, C. Hilbe, K. Chatterjee, Nature Communications 14
(2023).
date_created: 2023-04-23T22:01:03Z
date_published: 2023-04-12T00:00:00Z
date_updated: 2023-08-01T14:15:57Z
day: '12'
ddc:
- '000'
department:
- _id: KrCh
doi: 10.1038/s41467-023-37817-x
ec_funded: 1
external_id:
isi:
- '001003644100020'
pmid:
- '37045828'
file:
- access_level: open_access
checksum: a4b3b7b36fbef068cabf4fb99501fef6
content_type: application/pdf
creator: dernst
date_created: 2023-04-25T09:13:53Z
date_updated: 2023-04-25T09:13:53Z
file_id: '12868'
file_name: 2023_NatureComm_Schmid.pdf
file_size: 1786475
relation: main_file
success: 1
file_date_updated: 2023-04-25T09:13:53Z
has_accepted_license: '1'
intvolume: ' 14'
isi: 1
language:
- iso: eng
month: '04'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: 0599E47C-7A3F-11EA-A408-12923DDC885E
call_identifier: H2020
grant_number: '863818'
name: 'Formal Methods for Stochastic Models: Algorithms and Applications'
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: The Wittgenstein Prize
publication: Nature Communications
publication_identifier:
eissn:
- 2041-1723
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Quantitative assessment can stabilize indirect reciprocity under imperfect
information
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 14
year: '2023'
...
---
_id: '14242'
abstract:
- lang: eng
text: We study the problem of training and certifying adversarially robust quantized
neural networks (QNNs). Quantization is a technique for making neural networks
more efficient by running them using low-bit integer arithmetic and is therefore
commonly adopted in industry. Recent work has shown that floating-point neural
networks that have been verified to be robust can become vulnerable to adversarial
attacks after quantization, and certification of the quantized representation
is necessary to guarantee robustness. In this work, we present quantization-aware
interval bound propagation (QA-IBP), a novel method for training robust QNNs.
Inspired by advances in robust learning of non-quantized networks, our training
algorithm computes the gradient of an abstract representation of the actual network.
Unlike existing approaches, our method can handle the discrete semantics of QNNs.
Based on QA-IBP, we also develop a complete verification procedure for verifying
the adversarial robustness of QNNs, which is guaranteed to terminate and produce
a correct answer. Compared to existing approaches, the key advantage of our verification
procedure is that it runs entirely on GPU or other accelerator devices. We demonstrate
experimentally that our approach significantly outperforms existing methods and
establish the new state-of-the-art for training and certifying the robustness
of QNNs.
acknowledgement: "This work was supported in part by the ERC-2020-AdG 101020093, ERC
CoG 863818 (FoRM-SMArt) and the European Union’s Horizon 2020 research and innovation
programme under the Marie Skłodowska-Curie Grant Agreement No. 665385. Research
was sponsored by the United\r\nStates Air Force Research Laboratory and the United
States Air Force Artificial Intelligence Accelerator and was accomplished under
Cooperative Agreement Number FA8750-19-2-\r\n1000. The views and conclusions contained
in this document are those of the authors and should not be interpreted as representing
the official policies, either expressed or implied,\r\nof the United States Air
Force or the U.S. Government. The U.S. Government is authorized to reproduce and
distribute reprints for Government purposes notwithstanding any copyright\r\nnotation
herein. The research was also funded in part by the AI2050 program at Schmidt Futures
(Grant G-22-63172) and Capgemini SE."
article_processing_charge: No
author:
- first_name: Mathias
full_name: Lechner, Mathias
id: 3DC22916-F248-11E8-B48F-1D18A9856A87
last_name: Lechner
- first_name: Dorde
full_name: Zikelic, Dorde
id: 294AA7A6-F248-11E8-B48F-1D18A9856A87
last_name: Zikelic
orcid: 0000-0002-4681-1699
- first_name: Krishnendu
full_name: Chatterjee, Krishnendu
id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-4561-241X
- first_name: Thomas A
full_name: Henzinger, Thomas A
id: 40876CD8-F248-11E8-B48F-1D18A9856A87
last_name: Henzinger
orcid: 0000-0002-2985-7724
- first_name: Daniela
full_name: Rus, Daniela
last_name: Rus
citation:
ama: 'Lechner M, Zikelic D, Chatterjee K, Henzinger TA, Rus D. Quantization-aware
interval bound propagation for training certifiably robust quantized neural networks.
In: Proceedings of the 37th AAAI Conference on Artificial Intelligence.
Vol 37. Association for the Advancement of Artificial Intelligence; 2023:14964-14973.
doi:10.1609/aaai.v37i12.26747'
apa: 'Lechner, M., Zikelic, D., Chatterjee, K., Henzinger, T. A., & Rus, D.
(2023). Quantization-aware interval bound propagation for training certifiably
robust quantized neural networks. In Proceedings of the 37th AAAI Conference
on Artificial Intelligence (Vol. 37, pp. 14964–14973). Washington, DC, United
States: Association for the Advancement of Artificial Intelligence. https://doi.org/10.1609/aaai.v37i12.26747'
chicago: Lechner, Mathias, Dorde Zikelic, Krishnendu Chatterjee, Thomas A Henzinger,
and Daniela Rus. “Quantization-Aware Interval Bound Propagation for Training Certifiably
Robust Quantized Neural Networks.” In Proceedings of the 37th AAAI Conference
on Artificial Intelligence, 37:14964–73. Association for the Advancement of
Artificial Intelligence, 2023. https://doi.org/10.1609/aaai.v37i12.26747.
ieee: M. Lechner, D. Zikelic, K. Chatterjee, T. A. Henzinger, and D. Rus, “Quantization-aware
interval bound propagation for training certifiably robust quantized neural networks,”
in Proceedings of the 37th AAAI Conference on Artificial Intelligence,
Washington, DC, United States, 2023, vol. 37, no. 12, pp. 14964–14973.
ista: 'Lechner M, Zikelic D, Chatterjee K, Henzinger TA, Rus D. 2023. Quantization-aware
interval bound propagation for training certifiably robust quantized neural networks.
Proceedings of the 37th AAAI Conference on Artificial Intelligence. AAAI: Conference
on Artificial Intelligence vol. 37, 14964–14973.'
mla: Lechner, Mathias, et al. “Quantization-Aware Interval Bound Propagation for
Training Certifiably Robust Quantized Neural Networks.” Proceedings of the
37th AAAI Conference on Artificial Intelligence, vol. 37, no. 12, Association
for the Advancement of Artificial Intelligence, 2023, pp. 14964–73, doi:10.1609/aaai.v37i12.26747.
short: M. Lechner, D. Zikelic, K. Chatterjee, T.A. Henzinger, D. Rus, in:, Proceedings
of the 37th AAAI Conference on Artificial Intelligence, Association for the Advancement
of Artificial Intelligence, 2023, pp. 14964–14973.
conference:
end_date: 2023-02-14
location: Washington, DC, United States
name: 'AAAI: Conference on Artificial Intelligence'
start_date: 2023-02-07
date_created: 2023-08-27T22:01:17Z
date_published: 2023-06-26T00:00:00Z
date_updated: 2023-09-05T07:06:14Z
day: '26'
department:
- _id: ToHe
- _id: KrCh
doi: 10.1609/aaai.v37i12.26747
ec_funded: 1
external_id:
arxiv:
- '2211.16187'
intvolume: ' 37'
issue: '12'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2211.16187
month: '06'
oa: 1
oa_version: Preprint
page: 14964-14973
project:
- _id: 62781420-2b32-11ec-9570-8d9b63373d4d
call_identifier: H2020
grant_number: '101020093'
name: Vigilant Algorithmic Monitoring of Software
- _id: 0599E47C-7A3F-11EA-A408-12923DDC885E
call_identifier: H2020
grant_number: '863818'
name: 'Formal Methods for Stochastic Models: Algorithms and Applications'
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
publication: Proceedings of the 37th AAAI Conference on Artificial Intelligence
publication_identifier:
isbn:
- '9781577358800'
publication_status: published
publisher: Association for the Advancement of Artificial Intelligence
quality_controlled: '1'
scopus_import: '1'
status: public
title: Quantization-aware interval bound propagation for training certifiably robust
quantized neural networks
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 37
year: '2023'
...
---
_id: '14243'
abstract:
- lang: eng
text: 'Two-player zero-sum "graph games" are central in logic, verification, and
multi-agent systems. The game proceeds by placing a token on a vertex of a graph,
and allowing the players to move it to produce an infinite path, which determines
the winner or payoff of the game. Traditionally, the players alternate turns in
moving the token. In "bidding games", however, the players have budgets and in
each turn, an auction (bidding) determines which player moves the token. So far,
bidding games have only been studied as full-information games. In this work we
initiate the study of partial-information bidding games: we study bidding games
in which a player''s initial budget is drawn from a known probability distribution.
We show that while for some bidding mechanisms and objectives, it is straightforward
to adapt the results from the full-information setting to the partial-information
setting, for others, the analysis is significantly more challenging, requires
new techniques, and gives rise to interesting results. Specifically, we study
games with "mean-payoff" objectives in combination with "poorman" bidding. We
construct optimal strategies for a partially-informed player who plays against
a fully-informed adversary. We show that, somewhat surprisingly, the "value" under
pure strategies does not necessarily exist in such games.'
acknowledgement: This research was supported in part by ISF grant no.1679/21, by the
ERC CoG 863818 (ForM-SMArt), and the European Union’s Horizon 2020 research and
innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 665385.
article_processing_charge: No
author:
- first_name: Guy
full_name: Avni, Guy
id: 463C8BC2-F248-11E8-B48F-1D18A9856A87
last_name: Avni
orcid: 0000-0001-5588-8287
- first_name: Ismael R
full_name: Jecker, Ismael R
id: 85D7C63E-7D5D-11E9-9C0F-98C4E5697425
last_name: Jecker
- first_name: Dorde
full_name: Zikelic, Dorde
id: 294AA7A6-F248-11E8-B48F-1D18A9856A87
last_name: Zikelic
orcid: 0000-0002-4681-1699
citation:
ama: 'Avni G, Jecker IR, Zikelic D. Bidding graph games with partially-observable
budgets. In: Proceedings of the 37th AAAI Conference on Artificial Intelligence.
Vol 37. ; 2023:5464-5471. doi:10.1609/aaai.v37i5.25679'
apa: Avni, G., Jecker, I. R., & Zikelic, D. (2023). Bidding graph games with
partially-observable budgets. In Proceedings of the 37th AAAI Conference on
Artificial Intelligence (Vol. 37, pp. 5464–5471). Washington, DC, United States.
https://doi.org/10.1609/aaai.v37i5.25679
chicago: Avni, Guy, Ismael R Jecker, and Dorde Zikelic. “Bidding Graph Games with
Partially-Observable Budgets.” In Proceedings of the 37th AAAI Conference on
Artificial Intelligence, 37:5464–71, 2023. https://doi.org/10.1609/aaai.v37i5.25679.
ieee: G. Avni, I. R. Jecker, and D. Zikelic, “Bidding graph games with partially-observable
budgets,” in Proceedings of the 37th AAAI Conference on Artificial Intelligence,
Washington, DC, United States, 2023, vol. 37, no. 5, pp. 5464–5471.
ista: 'Avni G, Jecker IR, Zikelic D. 2023. Bidding graph games with partially-observable
budgets. Proceedings of the 37th AAAI Conference on Artificial Intelligence. AAAI:
Conference on Artificial Intelligence vol. 37, 5464–5471.'
mla: Avni, Guy, et al. “Bidding Graph Games with Partially-Observable Budgets.”
Proceedings of the 37th AAAI Conference on Artificial Intelligence, vol.
37, no. 5, 2023, pp. 5464–71, doi:10.1609/aaai.v37i5.25679.
short: G. Avni, I.R. Jecker, D. Zikelic, in:, Proceedings of the 37th AAAI Conference
on Artificial Intelligence, 2023, pp. 5464–5471.
conference:
end_date: 2023-02-14
location: Washington, DC, United States
name: 'AAAI: Conference on Artificial Intelligence'
start_date: 2023-02-07
date_created: 2023-08-27T22:01:18Z
date_published: 2023-06-27T00:00:00Z
date_updated: 2023-09-05T08:37:00Z
day: '27'
department:
- _id: ToHe
- _id: KrCh
doi: 10.1609/aaai.v37i5.25679
ec_funded: 1
external_id:
arxiv:
- '2211.13626'
intvolume: ' 37'
issue: '5'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1609/aaai.v37i5.25679
month: '06'
oa: 1
oa_version: Published Version
page: 5464-5471
project:
- _id: 0599E47C-7A3F-11EA-A408-12923DDC885E
call_identifier: H2020
grant_number: '863818'
name: 'Formal Methods for Stochastic Models: Algorithms and Applications'
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
publication: Proceedings of the 37th AAAI Conference on Artificial Intelligence
publication_identifier:
isbn:
- '9781577358800'
publication_status: published
quality_controlled: '1'
scopus_import: '1'
status: public
title: Bidding graph games with partially-observable budgets
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 37
year: '2023'
...