{"id":"https://openalex.org/W4309344697","doi":"https://doi.org/10.1109/smc53654.2022.9945133","title":"Adapted Metrics for Measuring Competency and Resilience for Autonomous Robot Systems in Discrete Time Markov Chains","display_name":"Adapted Metrics for Measuring Competency and Resilience for Autonomous Robot Systems in Discrete Time Markov Chains","publication_year":2022,"publication_date":"2022-10-09","ids":{"openalex":"https://openalex.org/W4309344697","doi":"https://doi.org/10.1109/smc53654.2022.9945133"},"language":"en","primary_location":{"id":"doi:10.1109/smc53654.2022.9945133","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc53654.2022.9945133","pdf_url":null,"source":{"id":"https://openalex.org/S4363607746","display_name":"2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5066624236","display_name":"Xuan Cao","orcid":"https://orcid.org/0000-0002-7211-6999"},"institutions":[{"id":"https://openalex.org/I100005738","display_name":"Brigham Young University","ror":"https://ror.org/047rhhm47","country_code":"US","type":"education","lineage":["https://openalex.org/I100005738"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xuan Cao","raw_affiliation_strings":["Brigham Young University,Computer Science Department,Provo,USA","Computer Science Department, Brigham Young University, Provo, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Brigham Young University,Computer Science Department,Provo,USA","institution_ids":["https://openalex.org/I100005738"]},{"raw_affiliation_string":"Computer Science Department, Brigham Young University, Provo, USA","institution_ids":["https://openalex.org/I100005738"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077075999","display_name":"Puneet Jain","orcid":"https://orcid.org/0000-0002-6420-4999"},"institutions":[{"id":"https://openalex.org/I100005738","display_name":"Brigham Young University","ror":"https://ror.org/047rhhm47","country_code":"US","type":"education","lineage":["https://openalex.org/I100005738"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Puneet Jain","raw_affiliation_strings":["Brigham Young University,Computer Science Department,Provo,USA","Computer Science Department, Brigham Young University, Provo, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Brigham Young University,Computer Science Department,Provo,USA","institution_ids":["https://openalex.org/I100005738"]},{"raw_affiliation_string":"Computer Science Department, Brigham Young University, Provo, USA","institution_ids":["https://openalex.org/I100005738"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5077345598","display_name":"Michael A. Goodrich","orcid":"https://orcid.org/0000-0002-2489-5705"},"institutions":[{"id":"https://openalex.org/I100005738","display_name":"Brigham Young University","ror":"https://ror.org/047rhhm47","country_code":"US","type":"education","lineage":["https://openalex.org/I100005738"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Michael A. Goodrich","raw_affiliation_strings":["Brigham Young University,Computer Science Department,Provo,USA","Computer Science Department, Brigham Young University, Provo, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Brigham Young University,Computer Science Department,Provo,USA","institution_ids":["https://openalex.org/I100005738"]},{"raw_affiliation_string":"Computer Science Department, Brigham Young University, Provo, USA","institution_ids":["https://openalex.org/I100005738"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I100005738"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"1999","issue":null,"first_page":"71","last_page":"76"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11357","display_name":"Risk and Safety Analysis","score":0.9919999837875366,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11357","display_name":"Risk and Safety Analysis","score":0.9919999837875366,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12423","display_name":"Software Reliability and Analysis Research","score":0.989300012588501,"subfield":{"id":"https://openalex.org/subfields/1712","display_name":"Software"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9884999990463257,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.7831885814666748},{"id":"https://openalex.org/keywords/resilience","display_name":"Resilience (materials science)","score":0.6590330004692078},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6188411712646484},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.5704479217529297},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.5228691101074219},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3216838836669922},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.23493146896362305},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13525483012199402},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.08215352892875671}],"concepts":[{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.7831885814666748},{"id":"https://openalex.org/C2779585090","wikidata":"https://www.wikidata.org/wiki/Q3457762","display_name":"Resilience (materials science)","level":2,"score":0.6590330004692078},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6188411712646484},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.5704479217529297},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.5228691101074219},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3216838836669922},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.23493146896362305},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13525483012199402},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.08215352892875671},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smc53654.2022.9945133","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc53654.2022.9945133","pdf_url":null,"source":{"id":"https://openalex.org/S4363607746","display_name":"2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320337345","display_name":"Office of Naval Research","ror":"https://ror.org/00rk2pe57"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W50918127","https://openalex.org/W1480210583","https://openalex.org/W1486153679","https://openalex.org/W1496560314","https://openalex.org/W1921093919","https://openalex.org/W1967294943","https://openalex.org/W2026036605","https://openalex.org/W2097031582","https://openalex.org/W2123564449","https://openalex.org/W2128672031","https://openalex.org/W2151958719","https://openalex.org/W2168691483","https://openalex.org/W2180809782","https://openalex.org/W2399962951","https://openalex.org/W2766447205","https://openalex.org/W2780836902","https://openalex.org/W2811243424","https://openalex.org/W2839976261","https://openalex.org/W2963778636","https://openalex.org/W2964131705","https://openalex.org/W4206725542","https://openalex.org/W4226502480","https://openalex.org/W4232946018","https://openalex.org/W4248615736","https://openalex.org/W4251653877","https://openalex.org/W6629008258","https://openalex.org/W6636725810","https://openalex.org/W6678189424","https://openalex.org/W6795970717"],"related_works":["https://openalex.org/W1660242800","https://openalex.org/W2077211377","https://openalex.org/W1006270037","https://openalex.org/W3152686072","https://openalex.org/W2284759612","https://openalex.org/W2379651310","https://openalex.org/W2113019827","https://openalex.org/W1541249122","https://openalex.org/W2413828414","https://openalex.org/W2367222340"],"abstract_inverted_index":{"Autonomous":[0],"robot":[1,34],"systems":[2],"are":[3,82,106],"often":[4],"designed":[5],"to":[6,14,108,139],"achieve":[7],"specific":[8,16],"goals.":[9],"This":[10,92],"paper":[11,93],"restricts":[12],"attention":[13],"a":[15,22,26,33,51,58,109,117,123],"type":[17],"of":[18,45,53],"goal,":[19],"namely":[20],"reaching":[21,46],"desired":[23,48],"state":[24,49],"within":[25],"certain":[27],"time":[28,55,100,125],"bound.":[29],"For":[30],"such":[31],"goals,":[32],"system\u2019s":[35],"competency":[36,76],"and":[37,62,77,80,120],"resilience":[38],"can":[39],"be":[40,140],"defined":[41],"as":[42,50,122],"the":[43,47,54,95,133,145,150],"probability":[44],"function":[52],"bound":[56],"under":[57,63],"nominal":[59],"unperturbed":[60],"condition":[61],"known":[64],"perturbation":[65],"conditions,":[66],"respectively.":[67],"Two":[68],"metrics":[69,97,105,135],"taken":[70],"from":[71],"prior":[72],"work":[73],"for":[74,98],"measuring":[75],"resilience,":[78],"power":[79],"efficiency,":[81],"modified":[83,134],"so":[84],"that":[85,113,132],"they":[86],"do":[87],"not":[88],"require":[89],"subjective":[90],"parameters.":[91],"formalizes":[94],"adapted":[96,104],"discrete":[99,124],"Markov":[101,126],"chains.":[102],"The":[103,128],"applied":[107],"best-of-N":[110],"case":[111,129],"study":[112,130],"is":[114],"solved":[115],"by":[116,149],"graph-based":[118],"approach":[119],"modeled":[121],"chain.":[127],"demonstrates":[131],"allow":[136],"power-efficiency":[137],"trade-offs":[138],"more":[141],"easily":[142],"visualized":[143],"than":[144],"cluttered":[146],"visualizations":[147],"produced":[148],"original":[151],"metrics.":[152]},"counts_by_year":[{"year":2023,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}