{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T20:20:41Z","timestamp":1770754841994,"version":"3.50.0"},"publisher-location":"Cham","reference-count":52,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031067723","type":"print"},{"value":"9783031067730","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-06773-0_10","type":"book-chapter","created":{"date-parts":[[2022,5,19]],"date-time":"2022-05-19T11:24:44Z","timestamp":1652959484000},"page":"193-212","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Verified Probabilistic Policies for\u00a0Deep Reinforcement Learning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0367-898X","authenticated-orcid":false,"given":"Edoardo","family":"Bacci","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4137-8862","authenticated-orcid":false,"given":"David","family":"Parker","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,5,20]]},"reference":[{"key":"10_CR1","doi-asserted-by":"crossref","unstructured":"Alshiekh, M., Bloem, R., Ehlers, R., K\u00f6nighofer, B., Niekum, S., Topcu, U.: Safe reinforcement learning via shielding. In: Proceedings of 32nd AAAI Conference on Artificial Intelligence (AAAI 2018), pp. 2669\u20132678 (2018)","DOI":"10.1609\/aaai.v32i1.11797"},{"key":"10_CR2","unstructured":"Bacci, E.: Formal Verification of Deep Reinforcement Learning Agents. Ph.D. thesis, School of Computer Science, University of Birmingham (2022)"},{"key":"10_CR3","doi-asserted-by":"crossref","unstructured":"Bacci, E., Giacobbe, M., Parker, D.: Verifying reinforcement learning up to infinity. In: Proceedings 30th International Joint Conference on Artificial Intelligence (IJCAI 2021), pp. 2154\u20132160 (2021)","DOI":"10.24963\/ijcai.2021\/297"},{"key":"10_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1007\/978-3-030-57628-8_14","volume-title":"Formal Modeling and Analysis of Timed Systems","author":"E Bacci","year":"2020","unstructured":"Bacci, E., Parker, D.: Probabilistic guarantees for safe deep reinforcement learning. In: Bertrand, N., Jansen, N. (eds.) FORMATS 2020. LNCS, vol. 12288, pp. 231\u2013248. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-57628-8_14"},{"key":"10_CR5","doi-asserted-by":"crossref","unstructured":"Bastani, O.: Safe reinforcement learning with nonlinear dynamics via model predictive shielding. In: Proceedings of the American Control Conference, pp. 3488\u20133494 (2021)","DOI":"10.15607\/RSS.2021.XVII.026"},{"key":"10_CR6","unstructured":"Bastani, O., Pu, Y., Solar-Lezama, A.: Verifiable reinforcement learning via policy extraction. In: Proceedings of 2018 Annual Conference on Neural Information Processing Systems (NeurIPS 2018), pp. 2499\u20132509 (2018)"},{"key":"10_CR7","doi-asserted-by":"crossref","unstructured":"Bogomolov, S., Frehse, G., Giacobbe, M., Henzinger, T.A.: Counterexample-guided refinement of template polyhedra. In: TACAS (1), pp. 589\u2013606 (2017)","DOI":"10.1007\/978-3-662-54577-5_34"},{"key":"10_CR8","unstructured":"Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., Zaremba, W.: OpenAI Gym, June 2016"},{"key":"10_CR9","unstructured":"Bunel, R., Turkaslan, I., Torr, P., Kohli, P., Kumar, P.: A unified view of piecewise linear neural network verification. In: Proceedings of 32nd International Conference on Neural Information Processing Systems (NIPS 2018), pp. 4795\u20134804 (2018)"},{"key":"10_CR10","doi-asserted-by":"publisher","first-page":"819","DOI":"10.1613\/jair.1.12963","volume":"72","author":"S Carr","year":"2021","unstructured":"Carr, S., Jansen, N., Topcu, U.: Task-aware verifiable RNN-based policies for partially observable Markov decision processes. J. Artif. Intell. Res. 72, 819\u2013847 (2021)","journal-title":"J. Artif. Intell. Res."},{"key":"10_CR11","doi-asserted-by":"crossref","unstructured":"Cauchi, N., Laurenti, L., Lahijanian, M., Abate, A., Kwiatkowska, M., Cardelli, L.: Efficiency through uncertainty: scalable formal synthesis for stochastic hybrid systems. In: 22nd ACM International Conference on Hybrid Systems: Computation and Control (2019)","DOI":"10.1145\/3302504.3311805"},{"key":"10_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1007\/978-3-319-68167-2_18","volume-title":"Automated Technology for Verification and Analysis","author":"C-H Cheng","year":"2017","unstructured":"Cheng, C.-H., N\u00fchrenberg, G., Ruess, H.: Maximum resilience of artificial neural networks. In: D\u2019Souza, D., Narayan Kumar, K. (eds.) ATVA 2017. LNCS, vol. 10482, pp. 251\u2013268. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-68167-2_18"},{"key":"10_CR13","doi-asserted-by":"crossref","unstructured":"Cheng, R., Orosz, G., Murray, R.M., Burdick, J.W.: End-to-end safe reinforcement learning through barrier functions for safety-critical continuous control tasks. In: AAAI, pp. 3387\u20133395. AAAI Press (2019)","DOI":"10.1609\/aaai.v33i01.33013387"},{"key":"10_CR14","doi-asserted-by":"crossref","unstructured":"Delgrange, F., Ann Now e, G.A.P.: Distillation of RL policies with formal guarantees via variational abstraction of Markov decision processes. In: Proceedings of 36th AAAI Conference on Artificial Intelligence (AAAI 2022) (2022)","DOI":"10.1609\/aaai.v36i6.20602"},{"key":"10_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1007\/11691617_5","volume-title":"Model Checking Software","author":"H Fecher","year":"2006","unstructured":"Fecher, H., Leucker, M., Wolf, V.: Don\u2019t Know in probabilistic systems. In: Valmari, A. (ed.) SPIN 2006. LNCS, vol. 3925, pp. 71\u201388. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11691617_5"},{"key":"10_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"468","DOI":"10.1007\/978-3-319-96145-3_25","volume-title":"Computer Aided Verification","author":"G Frehse","year":"2018","unstructured":"Frehse, G., Giacobbe, M., Henzinger, T.A.: Space-time interpolants. In: Chockler, H., Weissenbacher, G. (eds.) CAV 2018. LNCS, vol. 10981, pp. 468\u2013486. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-96145-3_25"},{"key":"10_CR17","doi-asserted-by":"crossref","unstructured":"Fulton, N., Platzer, A.: Safe reinforcement learning via formal methods: toward safe control through proof and learning. In: AAAI, pp. 6485\u20136492. AAAI Press (2018)","DOI":"10.1609\/aaai.v32i1.12107"},{"issue":"3","key":"10_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3275521","volume":"13","author":"J Garc\u00eda","year":"2018","unstructured":"Garc\u00eda, J., Fern\u00e1ndez, F.: Probabilistic policy reuse for safe reinforcement learning. ACM Trans. Autonomous Adaptive Syst. 13(3), 1\u201324 (2018)","journal-title":"ACM Trans. Autonomous Adaptive Syst."},{"key":"10_CR19","doi-asserted-by":"crossref","unstructured":"Gu, S., Holly, E., Lillicrap, T.P., Levine, S.: Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates. In: Proceedings of 2017 IEEE International Conference on Robotics and Automation (ICRA 2017), pp. 3389\u20133396 (2017)","DOI":"10.1109\/ICRA.2017.7989385"},{"key":"10_CR20","unstructured":"Gurobi Optimization, LLC: Gurobi Optimizer Reference Manual (2021)"},{"key":"10_CR21","unstructured":"Hasanbeig, M., Abate, A., Kroening, D.: Logically-constrained neural fitted q-iteration. In: AAMAS, pp. 2012\u20132014. IFAAMAS (2019)"},{"key":"10_CR22","unstructured":"Hasanbeig, M., Abate, A., Kroening, D.: Cautious reinforcement learning with logical constraints. In: AAMAS, pp. 483\u2013491. International Foundation for Autonomous Agents and Multiagent Systems (2020)"},{"key":"10_CR23","doi-asserted-by":"crossref","unstructured":"Hunt, N., Fulton, N., Magliacane, S., Hoang, T.N., Das, S., Solar-Lezama, A.: Verifiably safe exploration for end-to-end reinforcement learning. In: Proceedings of 24th International Conference on Hybrid Systems: Computation and Control (HSCC 2021) (2021)","DOI":"10.1145\/3447928.3456653"},{"key":"10_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1007\/978-3-030-31784-3_5","volume-title":"Automated Technology for Verification and Analysis","author":"M Jaeger","year":"2019","unstructured":"Jaeger, M., Jensen, P.G., Guldstrand Larsen, K., Legay, A., Sedwards, S., Taankvist, J.H.: Teaching stratego to play ball: optimal synthesis for continuous space MDPs. In: Chen, Y.-F., Cheng, C.-H., Esparza, J. (eds.) ATVA 2019. LNCS, vol. 11781, pp. 81\u201397. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-31784-3_5"},{"key":"10_CR25","unstructured":"Jansen, N., K\u00f6nighofer, B., Junges, S., Serban, A., Bloem, R.: Safe reinforcement learning using probabilistic shields. In: Proceedings of 31st International Conference on Concurrency Theory (CONCUR 2020), vol. 171, pp. 31\u2013316 (2020)"},{"key":"10_CR26","unstructured":"Jin, P., Zhang, M., Li, J., Han, L., Wen, X.: Learning on Abstract Domains: A New Approach for Verifiable Guarantee in Reinforcement Learning, June 2021"},{"issue":"3","key":"10_CR27","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1007\/s10703-010-0097-6","volume":"36","author":"M Kattenbelt","year":"2010","unstructured":"Kattenbelt, M., Kwiatkowska, M., Norman, G., Parker, D.: A game-based abstraction-refinement framework for Markov decision processes. Formal Methods Syst. Des. 36(3), 246\u2013280 (2010)","journal-title":"Formal Methods Syst. Des."},{"key":"10_CR28","doi-asserted-by":"crossref","unstructured":"Kazak, Y., Barrett, C.W., Katz, G., Schapira, M.: Verifying deep-RL-driven systems. In: Proceedings of the 2019 Workshop on Network Meets AI & ML, NetAI@SIGCOMM 2019, pp. 83\u201389. ACM (2019)","DOI":"10.1145\/3341216.3342218"},{"key":"10_CR29","doi-asserted-by":"crossref","unstructured":"Kemeny, J., Snell, J., Knapp, A.: Denumerable Markov Chains, 2nd edn. Springer (1976)","DOI":"10.1007\/978-1-4684-9455-6"},{"key":"10_CR30","doi-asserted-by":"crossref","unstructured":"Kendall, A., et al.: Learning to drive in a day. In: ICRA, pp. 8248\u20138254. IEEE (2019)","DOI":"10.1109\/ICRA.2019.8793742"},{"key":"10_CR31","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1007\/978-3-030-61362-4_16","volume-title":"Leveraging Applications of Formal Methods, Verification and Validation: Verification Principles","author":"B K\u00f6nighofer","year":"2020","unstructured":"K\u00f6nighofer, B., Lorber, F., Jansen, N., Bloem, R.: Shield synthesis for reinforcement learning. In: Margaria, T., Steffen, B. (eds.) ISoLA 2020. LNCS, vol. 12476, pp. 290\u2013306. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-61362-4_16"},{"key":"10_CR32","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1007\/978-3-642-22110-1_47","volume-title":"Computer Aided Verification","author":"M Kwiatkowska","year":"2011","unstructured":"Kwiatkowska, M., Norman, G., Parker, D.: PRISM 4.0: verification of probabilistic real-time systems. In: Gopalakrishnan, G., Qadeer, S. (eds.) CAV 2011. LNCS, vol. 6806, pp. 585\u2013591. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-22110-1_47"},{"issue":"8","key":"10_CR33","doi-asserted-by":"publisher","first-page":"2031","DOI":"10.1109\/TAC.2015.2398883","volume":"60","author":"M Lahijania","year":"2015","unstructured":"Lahijania, M., Andersson, S.B., Belta, C.: Formal verification and synthesis for discrete-time stochastic systems. IEEE Trans. Autom. Control 60(8), 2031\u20132045 (2015)","journal-title":"IEEE Trans. Autom. Control"},{"issue":"1","key":"10_CR34","first-page":"96","volume":"20","author":"J Langford","year":"2007","unstructured":"Langford, J., Zhang, T.: The epoch-greedy algorithm for contextual multi-armed bandits. Adv. Neural. Inf. Process. Syst. 20(1), 96\u20131 (2007)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"10_CR35","unstructured":"Liang, E., et al.: RLlib: abstractions for distributed reinforcement learning. In: Dy, J., Krause, A. (eds.) Proceedings of the 35th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 80, pp. 3053\u20133062. PMLR, 10\u201315 July 2018"},{"key":"10_CR36","doi-asserted-by":"crossref","unstructured":"Lun, Y.Z., Wheatley, J., D\u2019Innocenzo, A., Abate, A.: Approximate abstractions of Markov chains with interval decision processes. In: Proceedings of 6th IFAC Conference on Analysis and Design of Hybrid Systems (2018)","DOI":"10.1016\/j.ifacol.2018.08.016"},{"key":"10_CR37","doi-asserted-by":"crossref","unstructured":"Ma, H., Guan, Y., Li, S.E., Zhang, X., Zheng, S., Chen, J.: Feasible Actor-Critic: Constrained Reinforcement Learning for Ensuring Statewise Safety (2021)","DOI":"10.1155\/2021\/6658724"},{"key":"10_CR38","unstructured":"Mnih, V., et al.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of 33rd International Conference on Machine Learning, vol. 48, pp. 1928\u20131937. PMLR (2016)"},{"key":"10_CR39","volume-title":"An Introduction to Game Theory","author":"MJ Osborne","year":"2004","unstructured":"Osborne, M.J., et al.: An Introduction to Game Theory, vol. 3. Oxford University Press, New York (2004)"},{"key":"10_CR40","unstructured":"Papoudakis, G., Christianos, F., Albrecht, S.V.: Agent modelling under partial observability for deep reinforcement learning. In: Proceedings of the Neural Information Processing Systems (NeurIPS) (2021)"},{"key":"10_CR41","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"10_CR42","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1007\/978-3-540-30579-8_2","volume-title":"Verification, Model Checking, and Abstract Interpretation","author":"S Sankaranarayanan","year":"2005","unstructured":"Sankaranarayanan, S., Sipma, H.B., Manna, Z.: Scalable analysis of linear systems using mathematical programming. In: Cousot, R. (ed.) VMCAI 2005. LNCS, vol. 3385, pp. 25\u201341. Springer, Heidelberg (2005). https:\/\/doi.org\/10.1007\/978-3-540-30579-8_2"},{"key":"10_CR43","unstructured":"Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv:1707.06347 (2017)"},{"issue":"6","key":"10_CR44","doi-asserted-by":"publisher","first-page":"1296","DOI":"10.1287\/opre.32.6.1296","volume":"32","author":"RL Smith","year":"1984","unstructured":"Smith, R.L.: Efficient Monte Carlo procedures for generating points uniformly distributed over bounded regions. Oper. Res. 32(6), 1296\u20131308 (1984)","journal-title":"Oper. Res."},{"key":"10_CR45","unstructured":"Srinivasan, K., Eysenbach, B., Ha, S., Tan, J., Finn, C.: Learning to be Safe: Deep RL with a Safety Critic (2020)"},{"key":"10_CR46","unstructured":"Tjeng, V., Xiao, K., Tedrake, R.: Evaluating Robustness of Neural Networks with Mixed Integer Programming (2017)"},{"key":"10_CR47","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1007\/978-3-642-10439-8_35","volume-title":"AI 2009: Advances in Artificial Intelligence","author":"P Vamplew","year":"2009","unstructured":"Vamplew, P., Dazeley, R., Barker, E., Kelarev, A.: Constructing stochastic mixture policies for episodic multiobjective reinforcement learning tasks. In: Nicholson, A., Li, X. (eds.) AI 2009. LNCS (LNAI), vol. 5866, pp. 340\u2013349. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-642-10439-8_35"},{"key":"10_CR48","doi-asserted-by":"crossref","unstructured":"Wolff, E., Topcu, U., Murray, R.: Robust control of uncertain Markov decision processes with temporal logic specifications. In: Proceedings of 51th IEEE Conference on Decision and Control (CDC 2012), pp. 3372\u20133379 (2012)","DOI":"10.1109\/CDC.2012.6426174"},{"issue":"1","key":"10_CR49","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3477600","volume":"55","author":"C Yu","year":"2021","unstructured":"Yu, C., Liu, J., Nemati, S., Yin, G.: Reinforcement learning in healthcare: a survey. ACM Comput. Surv. 55(1), 1\u201336 (2021)","journal-title":"ACM Comput. Surv."},{"key":"10_CR50","unstructured":"Networkx - network analysis in python. https:\/\/networkx.github.io\/. Accessed 07 May 2020"},{"key":"10_CR51","unstructured":"Pytorch. https:\/\/pytorch.org\/. Accessed 07 May 2020"},{"key":"10_CR52","doi-asserted-by":"crossref","unstructured":"Zhu, H., Magill, S., Xiong, Z., Jagannathan, S.: An inductive synthesis framework for verifiable reinforcement learning. In: Proceedings of the ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI), pp. 686\u2013701. Association for Computing Machinery, June 2019","DOI":"10.1145\/3314221.3314638"}],"container-title":["Lecture Notes in Computer Science","NASA Formal Methods"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-06773-0_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T11:10:45Z","timestamp":1659352245000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-06773-0_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031067723","9783031067730"],"references-count":52,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-06773-0_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"20 May 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NFM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"NASA Formal Methods Symposium","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pasadena, CA","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 May 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 May 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"nfm2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/shemesh.larc.nasa.gov\/nfm2022\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"118","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"33","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"6","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"28% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"6.3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}