--- _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' ...