{"id":"https://openalex.org/W2552186722","doi":"https://doi.org/10.1109/fuzz-ieee.2016.7737798","title":"Reinforcement learning in the guarding a territory game","display_name":"Reinforcement learning in the guarding a territory game","publication_year":2016,"publication_date":"2016-07-01","ids":{"openalex":"https://openalex.org/W2552186722","doi":"https://doi.org/10.1109/fuzz-ieee.2016.7737798","mag":"2552186722"},"language":"en","primary_location":{"id":"doi:10.1109/fuzz-ieee.2016.7737798","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fuzz-ieee.2016.7737798","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","raw_type":"proceedings-article"},"type":"article","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/A5039475016","display_name":"Chidozie Vincent Analikwu","orcid":null},"institutions":[{"id":"https://openalex.org/I67031392","display_name":"Carleton University","ror":"https://ror.org/02qtvee93","country_code":"CA","type":"education","lineage":["https://openalex.org/I67031392"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Chidozie V. Analikwu","raw_affiliation_strings":["Department of Systems and Computer Engineering, Carleton University, Ottawa, Ontario, Canada"],"affiliations":[{"raw_affiliation_string":"Department of Systems and Computer Engineering, Carleton University, Ottawa, Ontario, Canada","institution_ids":["https://openalex.org/I67031392"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018347314","display_name":"Howard M. Schwartz","orcid":"https://orcid.org/0000-0002-4489-5892"},"institutions":[{"id":"https://openalex.org/I67031392","display_name":"Carleton University","ror":"https://ror.org/02qtvee93","country_code":"CA","type":"education","lineage":["https://openalex.org/I67031392"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Howard M. Schwartz","raw_affiliation_strings":["Department of Systems and Computer Engineering, Carleton University, Ottawa, Ontario, Canada"],"affiliations":[{"raw_affiliation_string":"Department of Systems and Computer Engineering, Carleton University, Ottawa, Ontario, Canada","institution_ids":["https://openalex.org/I67031392"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5039475016"],"corresponding_institution_ids":["https://openalex.org/I67031392"],"apc_list":null,"apc_paid":null,"fwci":1.7139,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.89374556,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"11","issue":null,"first_page":"1007","last_page":"1014"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9955000281333923,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9955000281333923,"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/T10100","display_name":"Metaheuristic Optimization Algorithms Research","score":0.9904999732971191,"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/T11975","display_name":"Evolutionary Algorithms and Applications","score":0.9851999878883362,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/guard","display_name":"Guard (computer science)","score":0.8033231496810913},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.7913199663162231},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.653633177280426},{"id":"https://openalex.org/keywords/game-theory","display_name":"Game theory","score":0.5178852081298828},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.51545649766922},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4926667809486389},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4505805969238281},{"id":"https://openalex.org/keywords/repeated-game","display_name":"Repeated game","score":0.42126280069351196},{"id":"https://openalex.org/keywords/non-cooperative-game","display_name":"Non-cooperative game","score":0.4116974472999573},{"id":"https://openalex.org/keywords/mathematical-economics","display_name":"Mathematical economics","score":0.2875913977622986},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13884082436561584}],"concepts":[{"id":"https://openalex.org/C141141315","wikidata":"https://www.wikidata.org/wiki/Q2379942","display_name":"Guard (computer science)","level":2,"score":0.8033231496810913},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7913199663162231},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.653633177280426},{"id":"https://openalex.org/C177142836","wikidata":"https://www.wikidata.org/wiki/Q44455","display_name":"Game theory","level":2,"score":0.5178852081298828},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.51545649766922},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4926667809486389},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4505805969238281},{"id":"https://openalex.org/C202556891","wikidata":"https://www.wikidata.org/wiki/Q1584646","display_name":"Repeated game","level":3,"score":0.42126280069351196},{"id":"https://openalex.org/C47175762","wikidata":"https://www.wikidata.org/wiki/Q13422573","display_name":"Non-cooperative game","level":3,"score":0.4116974472999573},{"id":"https://openalex.org/C144237770","wikidata":"https://www.wikidata.org/wiki/Q747534","display_name":"Mathematical economics","level":1,"score":0.2875913977622986},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13884082436561584},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/fuzz-ieee.2016.7737798","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fuzz-ieee.2016.7737798","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W568318363","https://openalex.org/W1499408472","https://openalex.org/W1581842928","https://openalex.org/W1902261929","https://openalex.org/W2033605227","https://openalex.org/W2067191654","https://openalex.org/W2077610764","https://openalex.org/W2079325629","https://openalex.org/W2105277270","https://openalex.org/W2113265921","https://openalex.org/W2117583043","https://openalex.org/W2121863487","https://openalex.org/W2134595317","https://openalex.org/W2223927396","https://openalex.org/W2324901285","https://openalex.org/W2580211036","https://openalex.org/W2916358692","https://openalex.org/W4214717370","https://openalex.org/W4231805100","https://openalex.org/W4233776596","https://openalex.org/W6680111671","https://openalex.org/W6688894980"],"related_works":["https://openalex.org/W2276575217","https://openalex.org/W190963404","https://openalex.org/W2141950114","https://openalex.org/W2110235590","https://openalex.org/W2102742283","https://openalex.org/W2134321370","https://openalex.org/W4214679513","https://openalex.org/W4377081010","https://openalex.org/W1548919438","https://openalex.org/W4372324527"],"abstract_inverted_index":{"In":[0,41,80],"this":[1],"paper,":[2],"we":[3,44],"investigate":[4],"the":[5,12,15,26,34,46,54,58,61,73,89,93,99,103,109,114,120,124,128,134,138,146,149],"use":[6,118],"of":[7,17,68,102,119,123,137,148],"reinforcement":[8],"learning":[9,87,135],"to":[10,48,97,132],"train":[11],"players":[13,32,62,74,84],"in":[14,25,33,88],"game":[16,22,125],"guarding":[18,104],"a":[19,39,65,105],"territory.":[20],"The":[21],"is":[23,111],"played":[24],"continuous":[27],"domain.":[28],"There":[29],"are":[30,85],"two":[31],"game:":[35],"an":[36],"invader":[37],"and":[38,144],"guard.":[40],"our":[42],"formulation,":[43],"set":[45],"guard":[47,110],"be":[49],"30":[50],"percent":[51],"faster":[52,112],"than":[53,113],"invader.":[55,115],"We":[56,91,116,140],"make":[57,117],"assumption":[59],"that":[60],"have":[63],"no":[64],"priori":[66],"knowledge":[67],"their":[69],"optimal":[70,100,121],"behavior.":[71],"Therefore,":[72],"will":[75],"obtain":[76],"these":[77],"after":[78],"learning.":[79],"other":[81],"words,":[82],"both":[83],"simultaneously":[86],"game.":[90],"introduce":[92],"Apollonius":[94,129],"circle":[95,130],"approach":[96,131],"determine":[98],"solution":[101,122],"territory":[106],"game,":[107],"when":[108],"determined":[126],"using":[127],"evaluate":[133],"performance":[136],"players.":[139],"present":[141],"simulation":[142],"results":[143],"discuss":[145],"effectiveness":[147],"approach.":[150]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":2},{"year":2017,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
