{"id":"https://openalex.org/W7133345729","doi":"https://doi.org/10.48550/arxiv.2603.00974","title":"Intent-Context Synergy Reinforcement Learning for Autonomous UAV Decision-Making in Air Combat","display_name":"Intent-Context Synergy Reinforcement Learning for Autonomous UAV Decision-Making in Air Combat","publication_year":2026,"publication_date":"2026-03-01","ids":{"openalex":"https://openalex.org/W7133345729","doi":"https://doi.org/10.48550/arxiv.2603.00974"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.00974","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.00974","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.2603.00974","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5128007922","display_name":"Jiahao Fu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fu, Jiahao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5127916941","display_name":"Feng Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Feng","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/T11133","display_name":"UAV Applications and Optimization","score":0.25769999623298645,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11133","display_name":"UAV Applications and Optimization","score":0.25769999623298645,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.2272000014781952,"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/T12158","display_name":"Guidance and Control Systems","score":0.16599999368190765,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/reinforcement-learning","display_name":"Reinforcement learning","score":0.8725000023841858},{"id":"https://openalex.org/keywords/markov-decision-process","display_name":"Markov decision process","score":0.450300008058548},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.4307999908924103},{"id":"https://openalex.org/keywords/core","display_name":"Core (optical fiber)","score":0.38519999384880066},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.3612000048160553},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.34540000557899475},{"id":"https://openalex.org/keywords/controller","display_name":"Controller (irrigation)","score":0.3357999920845032}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8725000023841858},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7073000073432922},{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.450300008058548},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4408000111579895},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.4307999908924103},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.38519999384880066},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.3612000048160553},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.34540000557899475},{"id":"https://openalex.org/C203479927","wikidata":"https://www.wikidata.org/wiki/Q5165939","display_name":"Controller (irrigation)","level":2,"score":0.3357999920845032},{"id":"https://openalex.org/C17098449","wikidata":"https://www.wikidata.org/wiki/Q176814","display_name":"Partially observable Markov decision process","level":4,"score":0.3093999922275543},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.3091999888420105},{"id":"https://openalex.org/C13687954","wikidata":"https://www.wikidata.org/wiki/Q4826847","display_name":"Autonomous agent","level":2,"score":0.301800012588501},{"id":"https://openalex.org/C2776544517","wikidata":"https://www.wikidata.org/wiki/Q189447","display_name":"Unexpected events","level":2,"score":0.28360000252723694},{"id":"https://openalex.org/C2988296547","wikidata":"https://www.wikidata.org/wiki/Q471145","display_name":"Air combat","level":2,"score":0.2800999879837036},{"id":"https://openalex.org/C133731056","wikidata":"https://www.wikidata.org/wiki/Q4917288","display_name":"Control engineering","level":1,"score":0.26330000162124634},{"id":"https://openalex.org/C177142836","wikidata":"https://www.wikidata.org/wiki/Q44455","display_name":"Game theory","level":2,"score":0.2590999901294708},{"id":"https://openalex.org/C47932503","wikidata":"https://www.wikidata.org/wiki/Q5395689","display_name":"Error-driven learning","level":3,"score":0.2578999996185303},{"id":"https://openalex.org/C196340769","wikidata":"https://www.wikidata.org/wiki/Q7698910","display_name":"Temporal difference learning","level":3,"score":0.2565000057220459}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.00974","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.00974","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.2603.00974","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.00974","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":[{"display_name":"Peace, Justice and strong institutions","score":0.7136983275413513,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Autonomous":[0],"UAV":[1,142],"infiltration":[2],"in":[3,44,123,191,197],"dynamic":[4,129],"contested":[5],"environments":[6],"remains":[7],"a":[8,114,124,172],"significant":[9],"challenge":[10],"due":[11],"to":[12,40,87,143,184],"the":[13,20,74,81,100,141,146,180],"partially":[14],"observable":[15],"nature":[16],"of":[17,23,77,117,176],"threats":[18],"and":[19,38,109,162,178,194],"conflicting":[21],"objectives":[22],"mission":[24,101,173],"efficiency":[25],"versus":[26],"survivability.":[27],"Traditional":[28],"Reinforcement":[29,56],"Learning":[30,57],"(RL)":[31],"approaches":[32],"often":[33],"suffer":[34],"from":[35,84],"myopic":[36],"decision-making":[37],"struggle":[39],"balance":[41],"these":[42,48,138],"trade-offs":[43],"real-time.":[45],"To":[46],"address":[47],"limitations,":[49],"this":[50],"paper":[51],"proposes":[52],"an":[53],"Intent-Context":[54],"Synergy":[55,96],"(ICS-RL)":[58],"framework.":[59],"The":[60,168],"framework":[61],"introduces":[62],"two":[63],"core":[64],"innovations:":[65],"(1)":[66],"An":[67],"LSTM-based":[68],"Intent":[69],"Prediction":[70],"Module":[71],"that":[72,98,155],"forecasts":[73],"future":[75],"trajectories":[76],"hostile":[78,110],"units,":[79],"transforming":[80],"decision":[82],"paradigm":[83],"reactive":[85],"avoidance":[86],"proactive":[88],"planning":[89],"via":[90],"state":[91],"augmentation;":[92],"(2)":[93],"A":[94,128],"Context-Analysis":[95],"Mechanism":[97],"decomposes":[99],"into":[102],"hierarchical":[103],"sub-tasks":[104],"(safe":[105],"cruise,":[106],"stealth":[107],"planning,":[108],"breakthrough).":[111],"We":[112],"design":[113],"heterogeneous":[115],"ensemble":[116],"Dueling":[118],"DQN":[119],"agents,":[120,139],"each":[121],"specialized":[122],"specific":[125],"tactical":[126],"context.":[127],"switching":[130],"controller":[131],"based":[132],"on":[133],"Max-Advantage":[134],"values":[135],"seamlessly":[136],"integrates":[137],"allowing":[140],"adaptively":[144],"select":[145],"optimal":[147],"policy":[148],"without":[149],"hard-coded":[150],"rules.":[151],"Extensive":[152],"simulations":[153],"demonstrate":[154],"ICS-RL":[156],"significantly":[157],"outperforms":[158],"baselines":[159],"(Standard":[160],"DDQN)":[161],"traditional":[163],"methods":[164],"(PSO,":[165],"Game":[166],"Theory).":[167],"proposed":[169],"method":[170],"achieves":[171],"success":[174],"rate":[175],"88\\%":[177],"reduces":[179],"average":[181],"exposure":[182],"frequency":[183],"0.24":[185],"per":[186],"episode,":[187],"validating":[188],"its":[189],"superiority":[190],"ensuring":[192],"robust":[193],"stealthy":[195],"penetration":[196],"high-dynamic":[198],"scenarios.":[199]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-03-04T00:00:00"}