{"tmp":{"image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"date_updated":"2023-10-17T11:42:00Z","month":"10","article_processing_charge":"No","day":"23","file":[{"date_updated":"2020-07-14T12:45:08Z","file_id":"4943","file_size":6455007,"date_created":"2018-12-12T10:12:25Z","checksum":"945d99631a96e0315acb26dc8541dcf9","relation":"main_file","content_type":"application/pdf","access_level":"open_access","creator":"system","file_name":"IST-2016-464-v1+1_entropy-17-07266.pdf"}],"type":"journal_article","abstract":[{"lang":"eng","text":"Quantifying behaviors of robots which were generated autonomously from task-independent objective functions is an important prerequisite for objective comparisons of algorithms and movements of animals. The temporal sequence of such a behavior can be considered as a time series and hence complexity measures developed for time series are natural candidates for its quantification. The predictive information and the excess entropy are such complexity measures. They measure the amount of information the past contains about the future and thus quantify the nonrandom structure in the temporal sequence. However, when using these measures for systems with continuous states one has to deal with the fact that their values will depend on the resolution with which the systems states are observed. For deterministic systems both measures will diverge with increasing resolution. We therefore propose a new decomposition of the excess entropy in resolution dependent and resolution independent parts and discuss how they depend on the dimensionality of the dynamics, correlations and the noise level. For the practical estimation we propose to use estimates based on the correlation integral instead of the direct estimation of the mutual information based on next neighbor statistics because the latter allows less control of the scale dependencies. Using our algorithm we are able to show how autonomous learning generates behavior of increasing complexity with increasing learning duration."}],"department":[{"_id":"ChLa"},{"_id":"GaTk"}],"issue":"10","date_published":"2015-10-23T00:00:00Z","publication_status":"published","scopus_import":"1","publisher":"MDPI","date_created":"2018-12-11T11:53:17Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa":1,"oa_version":"Published Version","status":"public","title":"Quantifying emergent behavior of autonomous robots","quality_controlled":"1","doi":"10.3390/e17107266","page":"7266 - 7297","author":[{"full_name":"Martius, Georg S","id":"3A276B68-F248-11E8-B48F-1D18A9856A87","last_name":"Martius","first_name":"Georg S"},{"full_name":"Olbrich, Eckehard","last_name":"Olbrich","first_name":"Eckehard"}],"has_accepted_license":"1","publication":"Entropy","pubrep_id":"464","language":[{"iso":"eng"}],"_id":"1655","ec_funded":1,"citation":{"short":"G.S. Martius, E. Olbrich, Entropy 17 (2015) 7266–7297.","ista":"Martius GS, Olbrich E. 2015. Quantifying emergent behavior of autonomous robots. Entropy. 17(10), 7266–7297.","chicago":"Martius, Georg S, and Eckehard Olbrich. “Quantifying Emergent Behavior of Autonomous Robots.” <i>Entropy</i>. MDPI, 2015. <a href=\"https://doi.org/10.3390/e17107266\">https://doi.org/10.3390/e17107266</a>.","apa":"Martius, G. S., &#38; Olbrich, E. (2015). Quantifying emergent behavior of autonomous robots. <i>Entropy</i>. MDPI. <a href=\"https://doi.org/10.3390/e17107266\">https://doi.org/10.3390/e17107266</a>","ama":"Martius GS, Olbrich E. Quantifying emergent behavior of autonomous robots. <i>Entropy</i>. 2015;17(10):7266-7297. doi:<a href=\"https://doi.org/10.3390/e17107266\">10.3390/e17107266</a>","ieee":"G. S. Martius and E. Olbrich, “Quantifying emergent behavior of autonomous robots,” <i>Entropy</i>, vol. 17, no. 10. MDPI, pp. 7266–7297, 2015.","mla":"Martius, Georg S., and Eckehard Olbrich. “Quantifying Emergent Behavior of Autonomous Robots.” <i>Entropy</i>, vol. 17, no. 10, MDPI, 2015, pp. 7266–97, doi:<a href=\"https://doi.org/10.3390/e17107266\">10.3390/e17107266</a>."},"year":"2015","volume":17,"publist_id":"5495","intvolume":"        17","ddc":["000"],"project":[{"_id":"25681D80-B435-11E9-9278-68D0E5697425","name":"International IST Postdoc Fellowship Programme","grant_number":"291734","call_identifier":"FP7"}],"file_date_updated":"2020-07-14T12:45:08Z","acknowledgement":"This work was supported by the DFG priority program 1527 (Autonomous Learning) and by the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 318723 (MatheMACS) and from the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme (FP7/2007-2013) under REA grant agreement no. 291734."}