{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T10:14:28Z","timestamp":1783764868446,"version":"3.55.0"},"publisher-location":"Cham","reference-count":50,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031569562","type":"print"},{"value":"9783031569579","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-56957-9_5","type":"book-chapter","created":{"date-parts":[[2024,3,28]],"date-time":"2024-03-28T00:04:24Z","timestamp":1711584264000},"page":"73-89","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Naturally Interpretable Control Policies via\u00a0Graph-Based Genetic Programming"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3535-9748","authenticated-orcid":false,"given":"Giorgia","family":"Nadizar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5652-2113","authenticated-orcid":false,"given":"Eric","family":"Medvet","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2414-0051","authenticated-orcid":false,"given":"Dennis G.","family":"Wilson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,3,28]]},"reference":[{"key":"5_CR1","doi-asserted-by":"publisher","first-page":"52138","DOI":"10.1109\/ACCESS.2018.2870052","volume":"6","author":"A Adadi","year":"2018","unstructured":"Adadi, A., Berrada, M.: Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access 6, 52138\u201352160 (2018)","journal-title":"IEEE Access"},{"key":"5_CR2","doi-asserted-by":"crossref","unstructured":"Amaral, R., Ianta, A., Bayer, C., Smith, R.J., Heywood, M.I.: Benchmarking genetic programming in a multi-action reinforcement learning locomotion task. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 522\u2013525 (2022)","DOI":"10.1145\/3520304.3528766"},{"key":"5_CR3","unstructured":"Bradbury, J., et al.: Jax: composable transformations of python+ numpy programs (2018)"},{"key":"5_CR4","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-31030-5","volume-title":"Linear Genetic Programming","author":"M Brameier","year":"2007","unstructured":"Brameier, M., Banzhaf, W., Banzhaf, W.: Linear Genetic Programming, vol. 1. Springer, New York (2007). https:\/\/doi.org\/10.1007\/978-0-387-31030-5"},{"key":"5_CR5","unstructured":"Coulom, R.: Reinforcement learning using neural networks, with applications to motor control. Ph.D. thesis, Institut National Polytechnique de Grenoble-INPG (2002)"},{"key":"5_CR6","unstructured":"Custode, L.L., Iacca, G.: Evolutionary learning of interpretable decision trees. arXiv preprint arXiv:2012.07723 (2020)"},{"key":"5_CR7","doi-asserted-by":"crossref","unstructured":"Custode, L.L., Iacca, G.: Interpretable pipelines with evolutionary optimized modules for reinforcement learning tasks with visual inputs. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 224\u2013227 (2022)","DOI":"10.1145\/3520304.3528897"},{"issue":"2","key":"5_CR8","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1109\/4235.996017","volume":"6","author":"K Deb","year":"2002","unstructured":"Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Trans. Evol. Comput. 6(2), 182\u2013197 (2002)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"5_CR9","doi-asserted-by":"crossref","unstructured":"Ferigo, A., Custode, L.L., Iacca, G.: Quality diversity evolutionary learning of decision trees. arXiv preprint arXiv:2208.12758 (2022)","DOI":"10.1145\/3555776.3577591"},{"key":"5_CR10","doi-asserted-by":"crossref","unstructured":"Ferigo, A., Custode, L.L., Iacca, G.: Quality-diversity optimization of decision trees for interpretable reinforcement learning. Neural Comput. Appl. 1\u201312 (2023)","DOI":"10.1007\/s00521-023-09124-5"},{"key":"5_CR11","doi-asserted-by":"crossref","unstructured":"Fran\u00e7oso Dal Piccol Sotto, L., Kaufmann, P., Atkinson, T., Kalkreuth, R., Porto Basgalupp, M.: Graph representations in genetic programming. Genet. Program. Evolvable Mach. 22(4), 607\u2013636 (2021)","DOI":"10.1007\/s10710-021-09413-9"},{"key":"5_CR12","unstructured":"Freeman, C.D., Frey, E., Raichuk, A., Girgin, S., Mordatch, I., Bachem, O.: Brax-a differentiable physics engine for large scale rigid body simulation. arXiv preprint arXiv:2106.13281 (2021)"},{"key":"5_CR13","unstructured":"Glanois, C., Weng, P., Zimmer, M., Li, D., Yang, T., Hao, J., Liu, W.: A survey on interpretable reinforcement learning. arXiv preprint arXiv:2112.13112 (2021)"},{"key":"5_CR14","doi-asserted-by":"crossref","unstructured":"Glass, A., McGuinness, D.L., Wolverton, M.: Toward establishing trust in adaptive agents. In: Proceedings of the 13th International Conference on Intelligent User Interfaces, pp. 227\u2013236 (2008)","DOI":"10.1145\/1378773.1378804"},{"issue":"5","key":"5_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3236009","volume":"51","author":"R Guidotti","year":"2018","unstructured":"Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., Pedreschi, D.: A survey of methods for explaining black box models. ACM Comput. Surv. (CSUR) 51(5), 1\u201342 (2018)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"5_CR16","unstructured":"Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft actor-critic: off-policy maximum entropy deep reinforcement learning with a stochastic actor. In: International Conference on Machine Learning, pp. 1861\u20131870, PMLR (2018)"},{"key":"5_CR17","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1016\/j.engappai.2018.09.007","volume":"76","author":"D Hein","year":"2018","unstructured":"Hein, D., Udluft, S., Runkler, T.A.: Interpretable policies for reinforcement learning by genetic programming. Eng. Appl. Artif. Intell. 76, 158\u2013169 (2018)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"5_CR18","doi-asserted-by":"crossref","unstructured":"Henderson, P., Islam, R., Bachman, P., Pineau, J., Precup, D., Meger, D.: Deep reinforcement learning that matters. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)","DOI":"10.1609\/aaai.v32i1.11694"},{"key":"5_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1007\/3-540-45984-7_8","volume-title":"Genetic Programming","author":"W Kantschik","year":"2002","unstructured":"Kantschik, W., Banzhaf, W.: Linear-graph GP - a new GP structure. In: Foster, J.A., Lutton, E., Miller, J., Ryan, C., Tettamanzi, A. (eds.) EuroGP 2002. LNCS, vol. 2278, pp. 83\u201392. Springer, Heidelberg (2002). https:\/\/doi.org\/10.1007\/3-540-45984-7_8"},{"issue":"7976","key":"5_CR20","doi-asserted-by":"publisher","first-page":"982","DOI":"10.1038\/s41586-023-06419-4","volume":"620","author":"E Kaufmann","year":"2023","unstructured":"Kaufmann, E., Bauersfeld, L., Loquercio, A., M\u00fcller, M., Koltun, V., Scaramuzza, D.: Champion-level drone racing using deep reinforcement learning. Nature 620(7976), 982\u2013987 (2023)","journal-title":"Nature"},{"key":"5_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1007\/978-3-319-55696-3_5","volume-title":"Genetic Programming","author":"S Kelly","year":"2017","unstructured":"Kelly, S., Heywood, M.I.: Emergent tangled graph representations for Atari game playing agents. In: McDermott, J., Castelli, M., Sekanina, L., Haasdijk, E., Garc\u00eda-S\u00e1nchez, P. (eds.) EuroGP 2017. LNCS, vol. 10196, pp. 64\u201379. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-55696-3_5"},{"key":"5_CR22","doi-asserted-by":"crossref","unstructured":"Kelly, S., Heywood, M.I.: Multi-task learning in atari video games with emergent tangled program graphs. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 195\u2013202 (2017)","DOI":"10.1145\/3071178.3071303"},{"key":"5_CR23","doi-asserted-by":"crossref","unstructured":"Kelly, S., et al.: Discovering adaptable symbolic algorithms from scratch. arXiv preprint arXiv:2307.16890 (2023)","DOI":"10.1109\/IROS55552.2023.10341979"},{"key":"5_CR24","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1007\/s10710-021-09418-4","volume":"22","author":"S Kelly","year":"2021","unstructured":"Kelly, S., Voegerl, T., Banzhaf, W., Gondro, C.: Evolving hierarchical memory-prediction machines in multi-task reinforcement learning. Genet. Program Evolvable Mach. 22, 573\u2013605 (2021)","journal-title":"Genet. Program Evolvable Mach."},{"issue":"2","key":"5_CR25","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1007\/BF00175355","volume":"4","author":"JR Koza","year":"1994","unstructured":"Koza, J.R.: Genetic programming as a means for programming computers by natural selection. Stat. Comput. 4(2), 87\u2013112 (1994)","journal-title":"Stat. Comput."},{"key":"5_CR26","unstructured":"Koza, J.R., Rice, J.P.: Automatic programming of robots using genetic programming. In: AAAI, vol. 92, pp. 194\u2013207 (1992)"},{"key":"5_CR27","unstructured":"Landajuela, M., et al.: Discovering symbolic policies with deep reinforcement learning. In: International Conference on Machine Learning, pp. 5979\u20135989, PMLR (2021)"},{"issue":"3","key":"5_CR28","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1145\/3236386.3241340","volume":"16","author":"ZC Lipton","year":"2018","unstructured":"Lipton, Z.C.: The mythos of model interpretability: in machine learning, the concept of interpretability is both important and slippery. Queue 16(3), 31\u201357 (2018)","journal-title":"Queue"},{"key":"5_CR29","doi-asserted-by":"crossref","unstructured":"Liu, D., Virgolin, M., Alderliesten, T., Bosman, P.A.: Evolvability degeneration in multi-objective genetic programming for symbolic regression. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 973\u2013981 (2022)","DOI":"10.1145\/3512290.3528787"},{"key":"5_CR30","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1613\/jair.5699","volume":"61","author":"MC Machado","year":"2018","unstructured":"Machado, M.C., Bellemare, M.G., Talvitie, E., Veness, J., Hausknecht, M., Bowling, M.: Revisiting the arcade learning environment: evaluation protocols and open problems for general agents. J. Artif. Intell. Res. 61, 523\u2013562 (2018)","journal-title":"J. Artif. Intell. Res."},{"key":"5_CR31","series-title":"Genetic and Evolutionary Computation","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-99-8413-8_11","volume-title":"Genetic Programming Theory and Practice XX","author":"E Medvet","year":"2023","unstructured":"Medvet, E., Nadizar, G.: GP for continuous control: teacher or learner? The case of simulated modular soft robots. In: Winkler, S., Trujillo, L., Ofria, C., Hu, T. (eds.) Genetic Programming Theory and Practice XX. Genetic and Evolutionary Computation, Springer, Singapore (2023). https:\/\/doi.org\/10.1007\/978-981-99-8413-8_11"},{"key":"5_CR32","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1007\/s10710-019-09360-6","volume":"21","author":"JF Miller","year":"2020","unstructured":"Miller, J.F.: Cartesian genetic programming: its status and future. Genet. Program Evolvable Mach. 21, 129\u2013168 (2020)","journal-title":"Genet. Program Evolvable Mach."},{"key":"5_CR33","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1007\/978-3-540-46239-2_9","volume-title":"Genetic Programming","author":"JF Miller","year":"2000","unstructured":"Miller, J.F., Thomson, P.: Cartesian genetic programming. In: Poli, R., Banzhaf, W., Langdon, W.B., Miller, J., Nordin, P., Fogarty, T.C. (eds.) EuroGP 2000. LNCS, vol. 1802, pp. 121\u2013132. Springer, Heidelberg (2000). https:\/\/doi.org\/10.1007\/978-3-540-46239-2_9"},{"key":"5_CR34","doi-asserted-by":"crossref","unstructured":"Nadizar, G., Rovito, L., De Lorenzo, A., Medvet, E., Virgolin, M.: An analysis of the ingredients for learning interpretable symbolic regression models with human-in-the-loop and genetic programming. ACM Tran. Evol. Learn. (2024)","DOI":"10.1145\/3643688"},{"key":"5_CR35","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1007\/978-3-030-57321-8_5","volume-title":"Machine Learning and Knowledge Extraction","author":"E Puiutta","year":"2020","unstructured":"Puiutta, E., Veith, E.M.S.P.: Explainable reinforcement learning: a survey. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) CD-MAKE 2020. LNCS, vol. 12279, pp. 77\u201395. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-57321-8_5"},{"key":"5_CR36","volume-title":"Markov Decision Processes: Discrete Stochastic Dynamic Programming","author":"ML Puterman","year":"2014","unstructured":"Puterman, M.L.: Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, Hoboken (2014)"},{"issue":"5","key":"5_CR37","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","volume":"1","author":"C Rudin","year":"2019","unstructured":"Rudin, C.: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1(5), 206\u2013215 (2019)","journal-title":"Nat. Mach. Intell."},{"key":"5_CR38","doi-asserted-by":"publisher","first-page":"153171","DOI":"10.1109\/ACCESS.2021.3126658","volume":"9","author":"E Salvato","year":"2021","unstructured":"Salvato, E., Fenu, G., Medvet, E., Pellegrino, F.A.: Crossing the reality gap: a survey on sim-to-real transferability of robot controllers in reinforcement learning. IEEE Access 9, 153171\u2013153187 (2021)","journal-title":"IEEE Access"},{"key":"5_CR39","unstructured":"Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017)"},{"key":"5_CR40","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.neunet.2019.01.011","volume":"113","author":"O Sigaud","year":"2019","unstructured":"Sigaud, O., Stulp, F.: Policy search in continuous action domains: an overview. Neural Netw. 113, 28\u201340 (2019)","journal-title":"Neural Netw."},{"issue":"7587","key":"5_CR41","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1038\/nature16961","volume":"529","author":"D Silver","year":"2016","unstructured":"Silver, D., et al.: Mastering the game of go with deep neural networks and tree search. Nature 529(7587), 484\u2013489 (2016)","journal-title":"Nature"},{"key":"5_CR42","doi-asserted-by":"crossref","unstructured":"Todorov, E., Erez, T., Tassa, Y.: Mujoco: a physics engine for model-based control. In: 2012 IEEE\/RSJ International Conference on Intelligent Robots and Systems, pp. 5026\u20135033. IEEE (2012)","DOI":"10.1109\/IROS.2012.6386109"},{"key":"5_CR43","unstructured":"Verma, A., Murali, V., Singh, R., Kohli, P., Chaudhuri, S.: Programmatically interpretable reinforcement learning. In: International Conference on Machine Learning, pp. 5045\u20135054. PMLR (2018)"},{"key":"5_CR44","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1007\/978-3-031-02056-8_18","volume-title":"Genetic Programming","author":"M Videau","year":"2022","unstructured":"Videau, M., Leite, A., Teytaud, O., Schoenauer, M.: Multi-objective genetic programming for explainable reinforcement learning. In: Medvet, E., Pappa, G., Xue, B. (eds.) EuroGP 2022. LNCS, vol. 13223, pp. 278\u2013293. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-02056-8_18"},{"key":"5_CR45","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1007\/978-3-030-58115-2_6","volume-title":"Parallel Problem Solving from Nature \u2013 PPSN XVI","author":"M Virgolin","year":"2020","unstructured":"Virgolin, M., De Lorenzo, A., Medvet, E., Randone, F.: Learning a formula of interpretability to learn interpretable formulas. In: B\u00e4ck, T., et al. (eds.) PPSN 2020. LNCS, vol. 12270, pp. 79\u201393. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58115-2_6"},{"key":"5_CR46","doi-asserted-by":"crossref","unstructured":"Virgolin, M., De Lorenzo, A., Randone, F., Medvet, E., Wahde, M.: Model learning with personalized interpretability estimation (ml-pie). In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 1355\u20131364 (2021)","DOI":"10.1145\/3449726.3463166"},{"key":"5_CR47","doi-asserted-by":"publisher","first-page":"550030","DOI":"10.3389\/frai.2021.550030","volume":"4","author":"L Wells","year":"2021","unstructured":"Wells, L., Bednarz, T.: Explainable AI and reinforcement learning-a systematic review of current approaches and trends. Front. Artif. Intell. 4, 550030 (2021)","journal-title":"Front. Artif. Intell."},{"key":"5_CR48","doi-asserted-by":"crossref","unstructured":"Wilson, D.G., Cussat-Blanc, S., Luga, H., Miller, J.F.: Evolving simple programs for playing Atari games. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 229\u2013236 (2018)","DOI":"10.1145\/3205455.3205578"},{"key":"5_CR49","unstructured":"Wilson, D.G., Miller, J.F., Cussat-Blanc, S., Luga, H.: Positional cartesian genetic programming. arXiv preprint arXiv:1810.04119 (2018)"},{"key":"5_CR50","doi-asserted-by":"crossref","unstructured":"Zhou, R., Hu, T.: Evolutionary approaches to explainable machine learning. arXiv preprint arXiv:2306.14786 (2023)","DOI":"10.1007\/978-981-99-3814-8_16"}],"container-title":["Lecture Notes in Computer Science","Genetic Programming"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-56957-9_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,28]],"date-time":"2024-03-28T00:05:12Z","timestamp":1711584312000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-56957-9_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031569562","9783031569579"],"references-count":50,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-56957-9_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"28 March 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EuroGP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Genetic Programming (Part of EvoStar)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Aberystwyth","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 April 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 April 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eurogp2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.evostar.org\/2024\/eurogp\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-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":"24","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":"13","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":"0","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":"54% - 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":"3.666","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":"2.447","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)"}}]}}