{"id":"https://openalex.org/W2984817382","doi":"https://doi.org/10.1145/3347146.3359358","title":"Bayesian Surprise in Indoor Environments","display_name":"Bayesian Surprise in Indoor Environments","publication_year":2019,"publication_date":"2019-11-05","ids":{"openalex":"https://openalex.org/W2984817382","doi":"https://doi.org/10.1145/3347146.3359358","mag":"2984817382"},"language":"en","primary_location":{"id":"doi:10.1145/3347146.3359358","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3347146.3359358","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2004.05381","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Sebastian Feld","orcid":null},"institutions":[{"id":"https://openalex.org/I8204097","display_name":"Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen","ror":"https://ror.org/05591te55","country_code":"DE","type":"education","lineage":["https://openalex.org/I8204097"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Sebastian Feld","raw_affiliation_strings":["Mobile and Distributed Systems, Group LMU Munich"],"affiliations":[{"raw_affiliation_string":"Mobile and Distributed Systems, Group LMU Munich","institution_ids":["https://openalex.org/I8204097"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Andreas Sedlmeier","orcid":null},"institutions":[{"id":"https://openalex.org/I8204097","display_name":"Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen","ror":"https://ror.org/05591te55","country_code":"DE","type":"education","lineage":["https://openalex.org/I8204097"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Andreas Sedlmeier","raw_affiliation_strings":["Mobile and Distributed Systems, Group LMU Munich"],"affiliations":[{"raw_affiliation_string":"Mobile and Distributed Systems, Group LMU Munich","institution_ids":["https://openalex.org/I8204097"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Markus Friedrich","orcid":null},"institutions":[{"id":"https://openalex.org/I8204097","display_name":"Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen","ror":"https://ror.org/05591te55","country_code":"DE","type":"education","lineage":["https://openalex.org/I8204097"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Markus Friedrich","raw_affiliation_strings":["Mobile and Distributed Systems, Group LMU Munich"],"affiliations":[{"raw_affiliation_string":"Mobile and Distributed Systems, Group LMU Munich","institution_ids":["https://openalex.org/I8204097"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jan Franz","orcid":null},"institutions":[{"id":"https://openalex.org/I8204097","display_name":"Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen","ror":"https://ror.org/05591te55","country_code":"DE","type":"education","lineage":["https://openalex.org/I8204097"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Jan Franz","raw_affiliation_strings":["Mobile and Distributed Systems, Group LMU Munich"],"affiliations":[{"raw_affiliation_string":"Mobile and Distributed Systems, Group LMU Munich","institution_ids":["https://openalex.org/I8204097"]}]},{"author_position":"last","author":{"id":null,"display_name":"Lenz Belzner","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lenz Belzner","raw_affiliation_strings":["MaibornWolff Munich"],"affiliations":[{"raw_affiliation_string":"MaibornWolff Munich","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I8204097"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.12576081,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"129","last_page":"138"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10799","display_name":"Data Visualization and Analytics","score":0.8870999813079834,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10799","display_name":"Data Visualization and Analytics","score":0.8870999813079834,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.8531000018119812,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.8349000215530396,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/surprise","display_name":"Surprise","score":0.8744999766349792},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5989000201225281},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5943999886512756},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.535099983215332},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5037000179290771},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.46880000829696655},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.4449999928474426},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.44449999928474426}],"concepts":[{"id":"https://openalex.org/C2780343955","wikidata":"https://www.wikidata.org/wiki/Q333173","display_name":"Surprise","level":2,"score":0.8744999766349792},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7251999974250793},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6376000046730042},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5989000201225281},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5943999886512756},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.535099983215332},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5037000179290771},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.46880000829696655},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.4449999928474426},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.44449999928474426},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39329999685287476},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.38609999418258667},{"id":"https://openalex.org/C64754055","wikidata":"https://www.wikidata.org/wiki/Q7574053","display_name":"Spatial contextual awareness","level":2,"score":0.33869999647140503},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.3192000091075897},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3131999969482422},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.31290000677108765},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.3127000033855438},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.31139999628067017},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.2994000017642975},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.29679998755455017},{"id":"https://openalex.org/C145804949","wikidata":"https://www.wikidata.org/wiki/Q478123","display_name":"Situation awareness","level":2,"score":0.29440000653266907},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.2897000014781952},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.27549999952316284},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.2720000147819519},{"id":"https://openalex.org/C82142266","wikidata":"https://www.wikidata.org/wiki/Q3456604","display_name":"Dynamic Bayesian network","level":3,"score":0.25200000405311584}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3347146.3359358","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3347146.3359358","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2004.05381","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2004.05381","pdf_url":"https://arxiv.org/pdf/2004.05381","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2004.05381","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2004.05381","pdf_url":"https://arxiv.org/pdf/2004.05381","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W1532367786","https://openalex.org/W1965555277","https://openalex.org/W2014967702","https://openalex.org/W2019515298","https://openalex.org/W2039546655","https://openalex.org/W2141430629","https://openalex.org/W2899608114"],"related_works":[],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"a":[3,24,62,83,92,105,130,137,141,151,176,211,214],"novel":[4],"method":[5],"to":[6,43,67,128,161,172,204],"identify":[7],"unexpected":[8],"structures":[9],"in":[10,50,97,104,181,196],"2D":[11,142],"floor":[12,221],"plans":[13],"using":[14],"the":[15,32,38,51,88,115,154,201,220],"concept":[16],"of":[17,31,34,40,53,81,85,109,117,123,133,150,179,187,210,219],"Bayesian":[18,41,102],"Surprise.":[19],"Taking":[20],"into":[21,72],"account":[22],"that":[23,135],"person's":[25],"expectation":[26,46],"is":[27,61],"an":[28,124],"important":[29,165,208],"aspect":[30],"perception":[33,90],"space,":[35],"we":[36,120,170],"exploit":[37],"theory":[39],"Surprise":[42,103],"robustly":[44],"model":[45],"and":[47,157],"thus":[48],"surprise":[49,174],"context":[52,180,202],"building":[54],"structures.":[55],"We":[56,99],"use":[57,101,173,200,216],"Isovist":[58],"Analysis,":[59],"which":[60],"popular":[63],"space":[64,107],"syntax":[65],"technique,":[66],"turn":[68],"qualitative":[69],"object":[70],"attributes":[71],"quantitative":[73],"environmental":[74],"information.":[75],"Since":[76],"isovists":[77,86],"are":[78],"location-specific":[79],"patterns":[80],"visibility,":[82],"sequence":[84],"describes":[87],"spatial":[89],"during":[91],"movement":[93],"along":[94],"multiple":[95],"points":[96],"space.":[98],"then":[100],"feature":[106],"consisting":[108],"these":[110],"isovist":[111],"readings.":[112],"To":[113],"demonstrate":[114],"suitability":[116],"our":[118],"approach,":[119],"take":[121],"\"snapshots\"":[122],"agent's":[125],"local":[126],"environment":[127],"provide":[129],"short":[131],"list":[132],"images":[134],"characterize":[136,153],"traversed":[138,155],"trajectory":[139],"through":[140],"indoor":[143,159,182],"environment.":[144],"Those":[145],"fingerprints":[146],"represent":[147],"surprising":[148],"regions":[149,209],"tour,":[152],"map":[156,212],"enable":[158],"LBS":[160],"focus":[162,205],"more":[163,206],"on":[164,207],"regions.":[166],"Given":[167],"this":[168],"idea,":[169],"propose":[171],"as":[175,190],"new":[177],"dimension":[178],"location-based":[183],"services":[184],"(LBS).":[185],"Agents":[186],"LBS,":[188],"such":[189],"mobile":[191],"robots":[192],"or":[193,217],"non-player":[194],"characters":[195],"computer":[197],"games,":[198],"may":[199],"\"surprise\"":[203],"for":[213],"better":[215],"understanding":[218],"plan.":[222]},"counts_by_year":[],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2019-11-22T00:00:00"}
