--- _id: '14259' abstract: - lang: eng text: "We provide a learning-based technique for guessing a winning strategy in a parity game originating from an LTL synthesis problem. A cheaply obtained guess can be useful in several applications. Not only can the guessed strategy be applied as best-effort in cases where the game’s huge size prohibits rigorous approaches, but it can also increase the scalability of rigorous LTL synthesis in several ways. Firstly, checking whether a guessed strategy is winning is easier than constructing one. Secondly, even if the guess is wrong in some places, it can be fixed by strategy iteration faster than constructing one from scratch. Thirdly, the guess can be used in on-the-fly approaches to prioritize exploration in the most fruitful directions.\r\nIn contrast to previous works, we (i) reflect the highly structured logical information in game’s states, the so-called semantic labelling, coming from the recent LTL-to-automata translations, and (ii) learn to reflect it properly by learning from previously solved games, bringing the solving process closer to human-like reasoning." acknowledgement: This research was funded in part by the German Research Foundation (DFG) project 427755713 Group-By Objectives in Probabilistic Verification (GOPro). alternative_title: - LNCS article_processing_charge: Yes (in subscription journal) author: - first_name: Jan full_name: Kretinsky, Jan id: 44CEF464-F248-11E8-B48F-1D18A9856A87 last_name: Kretinsky orcid: 0000-0002-8122-2881 - first_name: Tobias full_name: Meggendorfer, Tobias id: b21b0c15-30a2-11eb-80dc-f13ca25802e1 last_name: Meggendorfer orcid: 0000-0002-1712-2165 - first_name: Maximilian full_name: Prokop, Maximilian last_name: Prokop - first_name: Sabine full_name: Rieder, Sabine last_name: Rieder citation: ama: 'Kretinsky J, Meggendorfer T, Prokop M, Rieder S. Guessing winning policies in LTL synthesis by semantic learning. In: 35th International Conference on Computer Aided Verification . Vol 13964. Springer Nature; 2023:390-414. doi:10.1007/978-3-031-37706-8_20' apa: 'Kretinsky, J., Meggendorfer, T., Prokop, M., & Rieder, S. (2023). Guessing winning policies in LTL synthesis by semantic learning. In 35th International Conference on Computer Aided Verification (Vol. 13964, pp. 390–414). Paris, France: Springer Nature. https://doi.org/10.1007/978-3-031-37706-8_20' chicago: Kretinsky, Jan, Tobias Meggendorfer, Maximilian Prokop, and Sabine Rieder. “Guessing Winning Policies in LTL Synthesis by Semantic Learning.” In 35th International Conference on Computer Aided Verification , 13964:390–414. Springer Nature, 2023. https://doi.org/10.1007/978-3-031-37706-8_20. ieee: J. Kretinsky, T. Meggendorfer, M. Prokop, and S. Rieder, “Guessing winning policies in LTL synthesis by semantic learning,” in 35th International Conference on Computer Aided Verification , Paris, France, 2023, vol. 13964, pp. 390–414. ista: 'Kretinsky J, Meggendorfer T, Prokop M, Rieder S. 2023. Guessing winning policies in LTL synthesis by semantic learning. 35th International Conference on Computer Aided Verification . CAV: Computer Aided Verification, LNCS, vol. 13964, 390–414.' mla: Kretinsky, Jan, et al. “Guessing Winning Policies in LTL Synthesis by Semantic Learning.” 35th International Conference on Computer Aided Verification , vol. 13964, Springer Nature, 2023, pp. 390–414, doi:10.1007/978-3-031-37706-8_20. short: J. Kretinsky, T. Meggendorfer, M. Prokop, S. Rieder, in:, 35th International Conference on Computer Aided Verification , Springer Nature, 2023, pp. 390–414. conference: end_date: 2023-07-22 location: Paris, France name: 'CAV: Computer Aided Verification' start_date: 2023-07-17 date_created: 2023-09-03T22:01:16Z date_published: 2023-07-17T00:00:00Z date_updated: 2023-09-06T08:27:33Z day: '17' ddc: - '000' department: - _id: KrCh doi: 10.1007/978-3-031-37706-8_20 file: - access_level: open_access checksum: ed66278b61bb869e1baba3d9b9081271 content_type: application/pdf creator: dernst date_created: 2023-09-06T08:25:50Z date_updated: 2023-09-06T08:25:50Z file_id: '14276' file_name: 2023_LNCS_CAV_Kretinsky.pdf file_size: 428354 relation: main_file success: 1 file_date_updated: 2023-09-06T08:25:50Z has_accepted_license: '1' intvolume: ' 13964' language: - iso: eng month: '07' oa: 1 oa_version: Published Version page: 390-414 publication: '35th International Conference on Computer Aided Verification ' publication_identifier: eissn: - 1611-3349 isbn: - '9783031377051' issn: - 0302-9743 publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' status: public title: Guessing winning policies in LTL synthesis by semantic learning 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: 13964 year: '2023' ... --- _id: '14318' abstract: - lang: eng text: "Probabilistic recurrence relations (PRRs) are a standard formalism for describing the runtime of a randomized algorithm. Given a PRR and a time limit κ, we consider the tail probability Pr[T≥κ], i.e., the probability that the randomized runtime T of the PRR exceeds κ. Our focus is the formal analysis of tail bounds that aims at finding a tight asymptotic upper bound u≥Pr[T≥κ]. To address this problem, the classical and most well-known approach is the cookbook method by Karp (JACM 1994), while other approaches are mostly limited to deriving tail bounds of specific PRRs via involved custom analysis.\r\nIn this work, we propose a novel approach for deriving the common exponentially-decreasing tail bounds for PRRs whose preprocessing time and random passed sizes observe discrete or (piecewise) uniform distribution and whose recursive call is either a single procedure call or a divide-and-conquer. We first establish a theoretical approach via Markov’s inequality, and then instantiate the theoretical approach with a template-based algorithmic approach via a refined treatment of exponentiation. Experimental evaluation shows that our algorithmic approach is capable of deriving tail bounds that are (i) asymptotically tighter than Karp’s method, (ii) match the best-known manually-derived asymptotic tail bound for QuickSelect, and (iii) is only slightly worse (with a loglogn factor) than the manually-proven optimal asymptotic tail bound for QuickSort. Moreover, our algorithmic approach handles all examples (including realistic PRRs such as QuickSort, QuickSelect, DiameterComputation, etc.) in less than 0.1 s, showing that our approach is efficient in practice." acknowledgement: We thank Prof. Bican Xia for valuable information on the exponential theory of reals. The work is partially supported by the National Natural Science Foundation of China (NSFC) with Grant No. 62172271, ERC CoG 863818 (ForM-SMArt), the Hong Kong Research Grants Council ECS Project Number 26208122, the HKUST-Kaisa Joint Research Institute Project Grant HKJRI3A-055 and the HKUST Startup Grant R9272. alternative_title: - LNCS article_processing_charge: Yes (in subscription journal) author: - first_name: Yican full_name: Sun, Yican last_name: Sun - first_name: Hongfei full_name: Fu, Hongfei last_name: Fu - first_name: Krishnendu full_name: Chatterjee, Krishnendu id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87 last_name: Chatterjee orcid: 0000-0002-4561-241X - first_name: Amir Kafshdar full_name: Goharshady, Amir Kafshdar id: 391365CE-F248-11E8-B48F-1D18A9856A87 last_name: Goharshady orcid: 0000-0003-1702-6584 citation: ama: 'Sun Y, Fu H, Chatterjee K, Goharshady AK. Automated tail bound analysis for probabilistic recurrence relations. In: Computer Aided Verification. Vol 13966. Springer Nature; 2023:16-39. doi:10.1007/978-3-031-37709-9_2' apa: 'Sun, Y., Fu, H., Chatterjee, K., & Goharshady, A. K. (2023). Automated tail bound analysis for probabilistic recurrence relations. In Computer Aided Verification (Vol. 13966, pp. 16–39). Paris, France: Springer Nature. https://doi.org/10.1007/978-3-031-37709-9_2' chicago: Sun, Yican, Hongfei Fu, Krishnendu Chatterjee, and Amir Kafshdar Goharshady. “Automated Tail Bound Analysis for Probabilistic Recurrence Relations.” In Computer Aided Verification, 13966:16–39. Springer Nature, 2023. https://doi.org/10.1007/978-3-031-37709-9_2. ieee: Y. Sun, H. Fu, K. Chatterjee, and A. K. Goharshady, “Automated tail bound analysis for probabilistic recurrence relations,” in Computer Aided Verification, Paris, France, 2023, vol. 13966, pp. 16–39. ista: 'Sun Y, Fu H, Chatterjee K, Goharshady AK. 2023. Automated tail bound analysis for probabilistic recurrence relations. Computer Aided Verification. CAV: Computer Aided Verification, LNCS, vol. 13966, 16–39.' mla: Sun, Yican, et al. “Automated Tail Bound Analysis for Probabilistic Recurrence Relations.” Computer Aided Verification, vol. 13966, Springer Nature, 2023, pp. 16–39, doi:10.1007/978-3-031-37709-9_2. short: Y. Sun, H. Fu, K. Chatterjee, A.K. Goharshady, in:, Computer Aided Verification, Springer Nature, 2023, pp. 16–39. conference: end_date: 2023-07-22 location: Paris, France name: 'CAV: Computer Aided Verification' start_date: 2023-07-17 date_created: 2023-09-10T22:01:12Z date_published: 2023-07-17T00:00:00Z date_updated: 2023-09-20T08:25:57Z day: '17' ddc: - '000' department: - _id: KrCh doi: 10.1007/978-3-031-37709-9_2 ec_funded: 1 file: - access_level: open_access checksum: 42917e086f8c7699f3bccf84f74fe000 content_type: application/pdf creator: dernst date_created: 2023-09-20T08:24:47Z date_updated: 2023-09-20T08:24:47Z file_id: '14348' file_name: 2023_LNCS_Sun.pdf file_size: 624647 relation: main_file success: 1 file_date_updated: 2023-09-20T08:24:47Z has_accepted_license: '1' intvolume: ' 13966' language: - iso: eng month: '07' oa: 1 oa_version: Published Version page: 16-39 project: - _id: 0599E47C-7A3F-11EA-A408-12923DDC885E call_identifier: H2020 grant_number: '863818' name: 'Formal Methods for Stochastic Models: Algorithms and Applications' publication: Computer Aided Verification publication_identifier: eissn: - 1611-3349 isbn: - '9783031377082' issn: - 0302-9743 publication_status: published publisher: Springer Nature quality_controlled: '1' related_material: link: - relation: software url: https://github.com/boyvolcano/PRR scopus_import: '1' status: public title: Automated tail bound analysis for probabilistic recurrence relations 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: 13966 year: '2023' ... --- _id: '14317' abstract: - lang: eng text: "Markov decision processes can be viewed as transformers of probability distributions. While this view is useful from a practical standpoint to reason about trajectories of distributions, basic reachability and safety problems are known to be computationally intractable (i.e., Skolem-hard) to solve in such models. Further, we show that even for simple examples of MDPs, strategies for safety objectives over distributions can require infinite memory and randomization.\r\nIn light of this, we present a novel overapproximation approach to synthesize strategies in an MDP, such that a safety objective over the distributions is met. More precisely, we develop a new framework for template-based synthesis of certificates as affine distributional and inductive invariants for safety objectives in MDPs. We provide two algorithms within this framework. One can only synthesize memoryless strategies, but has relative completeness guarantees, while the other can synthesize general strategies. The runtime complexity of both algorithms is in PSPACE. We implement these algorithms and show that they can solve several non-trivial examples." acknowledgement: This work was supported in part 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 as well as DST/CEFIPRA/INRIA project EQuaVE and SERB Matrices grant MTR/2018/00074. alternative_title: - LNCS article_processing_charge: Yes (in subscription journal) author: - first_name: S. full_name: Akshay, S. last_name: Akshay - first_name: Krishnendu full_name: Chatterjee, Krishnendu id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87 last_name: Chatterjee orcid: 0000-0002-4561-241X - first_name: Tobias full_name: Meggendorfer, Tobias id: b21b0c15-30a2-11eb-80dc-f13ca25802e1 last_name: Meggendorfer orcid: 0000-0002-1712-2165 - first_name: Dorde full_name: Zikelic, Dorde id: 294AA7A6-F248-11E8-B48F-1D18A9856A87 last_name: Zikelic orcid: 0000-0002-4681-1699 citation: ama: 'Akshay S, Chatterjee K, Meggendorfer T, Zikelic D. MDPs as distribution transformers: Affine invariant synthesis for safety objectives. In: International Conference on Computer Aided Verification. Vol 13966. Springer Nature; 2023:86-112. doi:10.1007/978-3-031-37709-9_5' apa: 'Akshay, S., Chatterjee, K., Meggendorfer, T., & Zikelic, D. (2023). MDPs as distribution transformers: Affine invariant synthesis for safety objectives. In International Conference on Computer Aided Verification (Vol. 13966, pp. 86–112). Paris, France: Springer Nature. https://doi.org/10.1007/978-3-031-37709-9_5' chicago: 'Akshay, S., Krishnendu Chatterjee, Tobias Meggendorfer, and Dorde Zikelic. “MDPs as Distribution Transformers: Affine Invariant Synthesis for Safety Objectives.” In International Conference on Computer Aided Verification, 13966:86–112. Springer Nature, 2023. https://doi.org/10.1007/978-3-031-37709-9_5.' ieee: 'S. Akshay, K. Chatterjee, T. Meggendorfer, and D. Zikelic, “MDPs as distribution transformers: Affine invariant synthesis for safety objectives,” in International Conference on Computer Aided Verification, Paris, France, 2023, vol. 13966, pp. 86–112.' ista: 'Akshay S, Chatterjee K, Meggendorfer T, Zikelic D. 2023. MDPs as distribution transformers: Affine invariant synthesis for safety objectives. International Conference on Computer Aided Verification. CAV: Computer Aided Verification, LNCS, vol. 13966, 86–112.' mla: 'Akshay, S., et al. “MDPs as Distribution Transformers: Affine Invariant Synthesis for Safety Objectives.” International Conference on Computer Aided Verification, vol. 13966, Springer Nature, 2023, pp. 86–112, doi:10.1007/978-3-031-37709-9_5.' short: S. Akshay, K. Chatterjee, T. Meggendorfer, D. Zikelic, in:, International Conference on Computer Aided Verification, Springer Nature, 2023, pp. 86–112. conference: end_date: 2023-07-22 location: Paris, France name: 'CAV: Computer Aided Verification' start_date: 2023-07-17 date_created: 2023-09-10T22:01:12Z date_published: 2023-07-17T00:00:00Z date_updated: 2023-09-20T09:04:40Z day: '17' ddc: - '000' department: - _id: KrCh doi: 10.1007/978-3-031-37709-9_5 ec_funded: 1 file: - access_level: open_access checksum: f143c8eedf609f20f2aad2eeb496d53f content_type: application/pdf creator: dernst date_created: 2023-09-20T08:46:43Z date_updated: 2023-09-20T08:46:43Z file_id: '14349' file_name: 2023_LNCS_Akshay.pdf file_size: 531745 relation: main_file success: 1 file_date_updated: 2023-09-20T08:46:43Z has_accepted_license: '1' intvolume: ' 13966' language: - iso: eng month: '07' oa: 1 oa_version: Published Version page: 86-112 project: - _id: 2564DBCA-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '665385' name: International IST Doctoral Program - _id: 0599E47C-7A3F-11EA-A408-12923DDC885E call_identifier: H2020 grant_number: '863818' name: 'Formal Methods for Stochastic Models: Algorithms and Applications' publication: International Conference on Computer Aided Verification publication_identifier: eissn: - 1611-3349 isbn: - '9783031377082' issn: - 0302-9743 publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' status: public title: 'MDPs as distribution transformers: Affine invariant synthesis for safety objectives' 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: 13966 year: '2023' ... --- _id: '12738' abstract: - lang: eng text: We study turn-based stochastic zero-sum games with lexicographic preferences over objectives. Stochastic games are standard models in control, verification, and synthesis of stochastic reactive systems that exhibit both randomness as well as controllable and adversarial non-determinism. Lexicographic order allows one to consider multiple objectives with a strict preference order. To the best of our knowledge, stochastic games with lexicographic objectives have not been studied before. For a mixture of reachability and safety objectives, we show that deterministic lexicographically optimal strategies exist and memory is only required to remember the already satisfied and violated objectives. For a constant number of objectives, we show that the relevant decision problem is in NP∩coNP, matching the current known bound for single objectives; and in general the decision problem is PSPACE-hard and can be solved in NEXPTIME∩coNEXPTIME. We present an algorithm that computes the lexicographically optimal strategies via a reduction to the computation of optimal strategies in a sequence of single-objectives games. For omega-regular objectives, we restrict our analysis to one-player games, also known as Markov decision processes. We show that lexicographically optimal strategies exist and need either randomization or finite memory. We present an algorithm that solves the relevant decision problem in polynomial time. We have implemented our algorithms and report experimental results on various case studies. acknowledgement: Tobias Winkler and Joost-Pieter Katoen are supported by the DFG RTG 2236 UnRAVeL and the innovation programme under the Marie Skłodowska-Curie grant agreement No. 101008233 (Mission). Krishnendu Chatterjee is supported by the ERC CoG 863818 (ForM-SMArt) and the Vienna Science and Technology Fund (WWTF) Project ICT15-003. Maximilian Weininger is supported by the DFG projects 383882557 Statistical Unbounded Verification (SUV) and 427755713 Group-By Objectives in Probabilistic Verification (GOPro). Stefanie Mohr is supported by the DFG RTG 2428 CONVEY. Open Access funding enabled and organized by Projekt DEAL. article_processing_charge: No article_type: original author: - first_name: Krishnendu full_name: Chatterjee, Krishnendu id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87 last_name: Chatterjee orcid: 0000-0002-4561-241X - first_name: Joost P full_name: Katoen, Joost P id: 4524F760-F248-11E8-B48F-1D18A9856A87 last_name: Katoen - first_name: Stefanie full_name: Mohr, Stefanie last_name: Mohr - first_name: Maximilian full_name: Weininger, Maximilian last_name: Weininger - first_name: Tobias full_name: Winkler, Tobias last_name: Winkler citation: ama: Chatterjee K, Katoen JP, Mohr S, Weininger M, Winkler T. Stochastic games with lexicographic objectives. Formal Methods in System Design. 2023. doi:10.1007/s10703-023-00411-4 apa: Chatterjee, K., Katoen, J. P., Mohr, S., Weininger, M., & Winkler, T. (2023). Stochastic games with lexicographic objectives. Formal Methods in System Design. Springer Nature. https://doi.org/10.1007/s10703-023-00411-4 chicago: Chatterjee, Krishnendu, Joost P Katoen, Stefanie Mohr, Maximilian Weininger, and Tobias Winkler. “Stochastic Games with Lexicographic Objectives.” Formal Methods in System Design. Springer Nature, 2023. https://doi.org/10.1007/s10703-023-00411-4. ieee: K. Chatterjee, J. P. Katoen, S. Mohr, M. Weininger, and T. Winkler, “Stochastic games with lexicographic objectives,” Formal Methods in System Design. Springer Nature, 2023. ista: Chatterjee K, Katoen JP, Mohr S, Weininger M, Winkler T. 2023. Stochastic games with lexicographic objectives. Formal Methods in System Design. mla: Chatterjee, Krishnendu, et al. “Stochastic Games with Lexicographic Objectives.” Formal Methods in System Design, Springer Nature, 2023, doi:10.1007/s10703-023-00411-4. short: K. Chatterjee, J.P. Katoen, S. Mohr, M. Weininger, T. Winkler, Formal Methods in System Design (2023). date_created: 2023-03-19T23:00:59Z date_published: 2023-03-08T00:00:00Z date_updated: 2023-10-03T11:36:13Z day: '08' ddc: - '000' department: - _id: KrCh doi: 10.1007/s10703-023-00411-4 ec_funded: 1 external_id: isi: - '000946174300001' isi: 1 language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.1007/s10703-023-00411-4 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' - _id: 25892FC0-B435-11E9-9278-68D0E5697425 grant_number: ICT15-003 name: Efficient Algorithms for Computer Aided Verification publication: Formal Methods in System Design publication_identifier: eissn: - 1572-8102 publication_status: epub_ahead publisher: Springer Nature quality_controlled: '1' related_material: record: - id: '8272' relation: earlier_version status: public scopus_import: '1' status: public title: Stochastic games with lexicographic objectives 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: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2023' ... --- _id: '10770' abstract: - lang: eng text: Mathematical models often aim to describe a complicated mechanism in a cohesive and simple manner. However, reaching perfect balance between being simple enough or overly simplistic is a challenging task. Frequently, game-theoretic models have an underlying assumption that players, whenever they choose to execute a specific action, do so perfectly. In fact, it is rare that action execution perfectly coincides with intentions of individuals, giving rise to behavioural mistakes. The concept of incompetence of players was suggested to address this issue in game-theoretic settings. Under the assumption of incompetence, players have non-zero probabilities of executing a different strategy from the one they chose, leading to stochastic outcomes of the interactions. In this article, we survey results related to the concept of incompetence in classic as well as evolutionary game theory and provide several new results. We also suggest future extensions of the model and argue why it is important to take into account behavioural mistakes when analysing interactions among players in both economic and biological settings. acknowledgement: "The authors would like to acknowledge stimulating email discussions with Dr Wayne Lobb of W.A. Lobb LLC on the topic of evolutionary games. We also thank Dr Thomas Taimre for his input to the material in Sect. 3.\r\nThe authors would like to acknowledge partial support from the Australian Research Council under the Discovery grant DP180101602 and support by the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Grant Agreement #754411." article_processing_charge: No article_type: original author: - first_name: Thomas full_name: Graham, Thomas last_name: Graham - first_name: Maria full_name: Kleshnina, Maria id: 4E21749C-F248-11E8-B48F-1D18A9856A87 last_name: Kleshnina - first_name: Jerzy A. full_name: Filar, Jerzy A. last_name: Filar citation: ama: Graham T, Kleshnina M, Filar JA. Where do mistakes lead? A survey of games with incompetent players. Dynamic Games and Applications. 2023;13:231-264. doi:10.1007/s13235-022-00425-3 apa: Graham, T., Kleshnina, M., & Filar, J. A. (2023). Where do mistakes lead? A survey of games with incompetent players. Dynamic Games and Applications. Springer Nature. https://doi.org/10.1007/s13235-022-00425-3 chicago: Graham, Thomas, Maria Kleshnina, and Jerzy A. Filar. “Where Do Mistakes Lead? A Survey of Games with Incompetent Players.” Dynamic Games and Applications. Springer Nature, 2023. https://doi.org/10.1007/s13235-022-00425-3. ieee: T. Graham, M. Kleshnina, and J. A. Filar, “Where do mistakes lead? A survey of games with incompetent players,” Dynamic Games and Applications, vol. 13. Springer Nature, pp. 231–264, 2023. ista: Graham T, Kleshnina M, Filar JA. 2023. Where do mistakes lead? A survey of games with incompetent players. Dynamic Games and Applications. 13, 231–264. mla: Graham, Thomas, et al. “Where Do Mistakes Lead? A Survey of Games with Incompetent Players.” Dynamic Games and Applications, vol. 13, Springer Nature, 2023, pp. 231–64, doi:10.1007/s13235-022-00425-3. short: T. Graham, M. Kleshnina, J.A. Filar, Dynamic Games and Applications 13 (2023) 231–264. date_created: 2022-02-20T23:01:32Z date_published: 2023-03-01T00:00:00Z date_updated: 2023-10-04T09:24:30Z day: '01' ddc: - '000' department: - _id: KrCh doi: 10.1007/s13235-022-00425-3 ec_funded: 1 external_id: isi: - '000753777100001' file: - access_level: open_access checksum: cd53b07e96f9030ddb348f305e5b58c7 content_type: application/pdf creator: dernst date_created: 2022-02-21T08:54:17Z date_updated: 2022-02-21T08:54:17Z file_id: '10781' file_name: 2022_DynamicGamesApplic_Graham.pdf file_size: 1890512 relation: main_file success: 1 file_date_updated: 2022-02-21T08:54:17Z has_accepted_license: '1' intvolume: ' 13' isi: 1 language: - iso: eng month: '03' oa: 1 oa_version: Published Version page: 231-264 project: - _id: 260C2330-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '754411' name: ISTplus - Postdoctoral Fellowships publication: Dynamic Games and Applications publication_identifier: eissn: - 2153-0793 issn: - 2153-0785 publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' status: public title: Where do mistakes lead? A survey of games with incompetent players 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: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 13 year: '2023' ...