{"id":"https://openalex.org/W7167795187","doi":"https://doi.org/10.48550/arxiv.2607.07498","title":"Reward-Adaptive Iterative Discovery: A Case Study on Automated Game Testing for NHL26","display_name":"Reward-Adaptive Iterative Discovery: A Case Study on Automated Game Testing for NHL26","publication_year":2026,"publication_date":"2026-07-08","ids":{"openalex":"https://openalex.org/W7167795187","doi":"https://doi.org/10.48550/arxiv.2607.07498"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.07498","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.07498","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2607.07498","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5003390011","display_name":"Florian Fuchs","orcid":"https://orcid.org/0000-0002-1072-3718"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fuchs, Florian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140309236","display_name":"Jessy Gosselin-Grant","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gosselin-Grant, Jessy","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140357005","display_name":"Boris Skuin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Skuin, Boris","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107851465","display_name":"M. Petteni","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Petteni, Michele","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021772177","display_name":"Alessandro Sestini","orcid":"https://orcid.org/0000-0001-5496-5770"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sestini, Alessandro","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028726425","display_name":"Joakim Bergdahl","orcid":"https://orcid.org/0000-0001-5720-2533"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bergdahl, Joakim","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035916003","display_name":"amir baghi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Baghi, Amir","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5088043381","display_name":"Linus Gissl\u00e9n","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gissl\u00e9n, Linus","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11574","display_name":"Artificial Intelligence in Games","score":0.8116999864578247,"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"}},"topics":[{"id":"https://openalex.org/T11574","display_name":"Artificial Intelligence in Games","score":0.8116999864578247,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.03240000084042549,"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/T10743","display_name":"Software Testing and Debugging Techniques","score":0.02710000053048134,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.769599974155426},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.7304999828338623},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.5576000213623047},{"id":"https://openalex.org/keywords/iterative-and-incremental-development","display_name":"Iterative and incremental development","score":0.4855000078678131},{"id":"https://openalex.org/keywords/train","display_name":"Train","score":0.4510999917984009},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.43290001153945923},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.4156000018119812},{"id":"https://openalex.org/keywords/test","display_name":"Test (biology)","score":0.40070000290870667}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.769599974155426},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7473999857902527},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.7304999828338623},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5759999752044678},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5720999836921692},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.5576000213623047},{"id":"https://openalex.org/C143587482","wikidata":"https://www.wikidata.org/wiki/Q1543216","display_name":"Iterative and incremental development","level":2,"score":0.4855000078678131},{"id":"https://openalex.org/C190839683","wikidata":"https://www.wikidata.org/wiki/Q2448197","display_name":"Train","level":2,"score":0.4510999917984009},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.43290001153945923},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.4156000018119812},{"id":"https://openalex.org/C2777267654","wikidata":"https://www.wikidata.org/wiki/Q3519023","display_name":"Test (biology)","level":2,"score":0.40070000290870667},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.37389999628067017},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.35850000381469727},{"id":"https://openalex.org/C2778029271","wikidata":"https://www.wikidata.org/wiki/Q5421931","display_name":"Extension (predicate logic)","level":2,"score":0.310699999332428},{"id":"https://openalex.org/C2776542497","wikidata":"https://www.wikidata.org/wiki/Q5266672","display_name":"Development (topology)","level":2,"score":0.2971999943256378},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.295199990272522},{"id":"https://openalex.org/C117619785","wikidata":"https://www.wikidata.org/wiki/Q6094414","display_name":"Iterative learning control","level":3,"score":0.27649998664855957},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.27300000190734863},{"id":"https://openalex.org/C128942645","wikidata":"https://www.wikidata.org/wiki/Q1568346","display_name":"Test case","level":3,"score":0.2728999853134155},{"id":"https://openalex.org/C3018412434","wikidata":"https://www.wikidata.org/wiki/Q7889","display_name":"Video game","level":2,"score":0.2676999866962433},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25949999690055847},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.25029999017715454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.07498","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.07498","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2607.07498","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.07498","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.4957292973995209,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Testing":[0],"is":[1],"a":[2,10,21,25,73,89,109,114,139],"major":[3],"effort":[4,51],"for":[5,37,45],"the":[6,29,42,50,54,64],"gaming":[7],"industry,":[8],"requiring":[9],"significant":[11],"part":[12],"of":[13,28,52,91,119,135],"development":[14,26,65],"budget":[15],"and":[16],"people":[17],"power.":[18],"We":[19,112],"present":[20],"case":[22],"study":[23],"on":[24,117],"version":[27],"ice":[30],"hockey":[31,148],"game":[32,59],"EA":[33],"SPORTS":[34],"NHL":[35],"26,":[36],"which":[38],"human":[39],"playtesters":[40,159],"test":[41],"goalie":[43,55],"AI":[44,56],"behavioral":[46],"exploits.":[47],"To":[48],"reduce":[49],"re-testing":[53],"after":[57],"every":[58],"or":[60],"behavior":[61],"modification":[62],"in":[63,162],"phase,":[66],"we":[67,142],"propose":[68],"Reward-Adaptive":[69],"Iterative":[70],"Discovery":[71],"(RAID),":[72],"novel":[74],"approach":[75,86],"to":[76,106,108,145,156],"automatically":[77],"find":[78,101,126,146],"exploits":[79],"using":[80],"an":[81],"iterative":[82],"Reinforcement":[83],"Learning":[84],"(RL)":[85],"that":[87,124,152,158],"trains":[88],"population":[90],"goal":[92],"scoring":[93,149],"agents.":[94],"While":[95],"previous":[96],"approaches":[97],"can":[98],"already":[99],"successfully":[100],"exploits,":[102],"RL":[103,121],"algorithms":[104],"tend":[105],"overfit":[107],"single":[110,140],"solution.":[111],"introduce":[113],"simple":[115],"extension":[116],"top":[118],"existing":[120],"algorithms,":[122],"such":[123],"they":[125],"multiple":[127],"diverse":[128],"high-quality":[129],"solutions.":[130],"For":[131],"our":[132],"first":[133],"deployment":[134],"this":[136],"approach,":[137],"within":[138],"experiment":[141],"were":[143,153],"able":[144],"six":[147],"exploit":[150],"strategies":[151],"qualitatively":[154],"similar":[155],"those":[157],"had":[160],"found":[161],"hours-long":[163],"manual":[164],"testing":[165],"sessions.":[166]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-10T00:00:00"}
