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A notable example of active learning technique for regular languages is Angluin\u2019s\n            <jats:inline-formula>\n              <jats:alternatives>\n                <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:msup>\n                    <mml:mi>L<\/mml:mi>\n                    <mml:mo>\u2217<\/mml:mo>\n                  <\/mml:msup>\n                <\/mml:math>\n              <\/jats:alternatives>\n            <\/jats:inline-formula>\n            -algorithm. The\n            <jats:inline-formula>\n              <jats:alternatives>\n                <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:msup>\n                    <mml:mi>L<\/mml:mi>\n                    <mml:mo>\u2217<\/mml:mo>\n                  <\/mml:msup>\n                <\/mml:math>\n              <\/jats:alternatives>\n            <\/jats:inline-formula>\n            -algorithm describes the strategy of a student who learns the minimal deterministic finite automaton of an unknown regular language\n            <jats:inline-formula>\n              <jats:alternatives>\n                <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mi>L<\/mml:mi>\n                <\/mml:math>\n              <\/jats:alternatives>\n            <\/jats:inline-formula>\n            by asking a succinct number of queries to a teacher who knows\n            <jats:inline-formula>\n              <jats:alternatives>\n                <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mi>L<\/mml:mi>\n                <\/mml:math>\n              <\/jats:alternatives>\n            <\/jats:inline-formula>\n            .\n          <\/jats:p>\n          <jats:p>\n            In this work, we study\n            <jats:inline-formula>\n              <jats:alternatives>\n                <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:msup>\n                    <mml:mi>L<\/mml:mi>\n                    <mml:mo>\u2217<\/mml:mo>\n                  <\/mml:msup>\n                <\/mml:math>\n              <\/jats:alternatives>\n            <\/jats:inline-formula>\n            -based learning of deterministic Markov decision processes, a class of Markov decision processes where an observation following an action uniquely determines a successor state. For this purpose, we first assume an ideal setting with a teacher who provides perfect information to the student. Then, we relax this assumption and present a novel learning algorithm that collects information by sampling execution traces of the system via testing.\n          <\/jats:p>\n          <jats:p>Experiments performed on an implementation of our sampling-based algorithm suggest that our method achieves better accuracy than state-of-the-art passive learning techniques using the same amount of test obser vations. In contrast to existing learning algorithms which assume a predefined number of states, our algorithm learns the complete model structure including the state space.<\/jats:p>","DOI":"10.1007\/s00165-021-00536-5","type":"journal-article","created":{"date-parts":[[2021,3,31]],"date-time":"2021-03-31T16:03:17Z","timestamp":1617206597000},"page":"575-615","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["L\u2217-based learning of Markov decision processes (extended version)"],"prefix":"10.1145","volume":"33","author":[{"given":"Martin","family":"Tappler","sequence":"first","affiliation":[{"name":"Institute of Software Technology, Graz University of Technology, Graz, Austria"},{"name":"Schaffhausen Institute of Technology, Schaffhausen, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3484-5584","authenticated-orcid":false,"given":"Bernhard K.","family":"Aichernig","sequence":"additional","affiliation":[{"name":"Institute of Software Technology, Graz University of Technology, Graz, Austria"}]},{"given":"Giovanni","family":"Bacci","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Aalborg University, Aalborg, Denmark"}]},{"given":"Maria","family":"Eichlseder","sequence":"additional","affiliation":[{"name":"Institute of Applied Information Processing and Communications, Graz University of Technology, Graz, Austria"}]},{"given":"Kim G.","family":"Larsen","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Aalborg University, Aalborg, Denmark"}]}],"member":"320","reference":[{"key":"e_1_2_1_2_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/0196-6774(90)90021-6"},{"key":"e_1_2_1_2_2_2","first-page":"74","volume-title":"Machine learning for dynamic software analysis: potentials and limits\u2013international Dagstuhl seminar 16172, Dagstuhl Castle, Germany, April 24\u201327, 2016, revised papers","author":"Aichernig BK","year":"2018"},{"key":"e_1_2_1_2_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/0890-5401(87)90052-6"},{"key":"e_1_2_1_2_4_2","doi-asserted-by":"crossref","unstructured":"Aichernig BK Tappler M (2017) Learning from faults: mutation testing in active automata learning. 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