Automating Speedrun Routing: Overview and Vision
Speedrunning in general means to play a video game fast, i.e. using all means at one's disposal to achieve a given goal in the least amount of time possible. To do so, a speedrun must be planned in advance, or routed, as referred to by the community. This paper focuses on discovering challenges...
Gespeichert in:
Veröffentlicht in: | arXiv.org 2022-04 |
---|---|
Hauptverfasser: | , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | |
container_start_page | |
container_title | arXiv.org |
container_volume | |
creator | Groß, Matthias Zühlke, Dietlind Naujoks, Boris |
description | Speedrunning in general means to play a video game fast, i.e. using all means at one's disposal to achieve a given goal in the least amount of time possible. To do so, a speedrun must be planned in advance, or routed, as referred to by the community. This paper focuses on discovering challenges and defining models needed when trying to approach the problem of routing algorithmically. To do so, this paper is split in two parts. The first part provides an overview of relevant speedrunning literature, extracting vital information and formulating criticism. Important categorizations are pointed out and a nomenclature is built to support professional discussion. The second part of this paper then refers to the actual speedrun routing optimization problem. Different concepts of graph representations are presented and their potential is discussed. Visions both for problem modeling as well as solving are presented and assessed regarding suitability and expected challenges. Finally, a first assessment of the applicability of existing optimization methods to the defined problem is made, including metaheuristics/EA and Deep Learning methods. |
doi_str_mv | 10.48550/arxiv.2106.01182 |
format | Article |
fullrecord | <record><control><sourceid>proquest_arxiv</sourceid><recordid>TN_cdi_arxiv_primary_2106_01182</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2536672029</sourcerecordid><originalsourceid>FETCH-LOGICAL-a529-6bf07d3f28e629ddef4cc5a5e9a6527e304665aba74ac7bdf83e104e82edaebd3</originalsourceid><addsrcrecordid>eNotj0tLw0AUhQdBsNT-AFcGXCfeufPIxF0paoVCQYvbMMncyBSbxMlD_femrasDh4_D-Ri74ZBIoxTc2_DjxwQ56AQ4N3jBZigEj41EvGKLrtsDAOoUlRIzBsuhbw629_VH9NYSuTDU0WszHIuHaDtSGD19R7Z20bvvfFNfs8vKfna0-M852z097lbreLN9flktN7FVmMW6qCB1okJDGjPnqJJlqayizGqFKQmQWitb2FTaMi1cZQRxkGSQnKXCiTm7Pc-edPI2-IMNv_lRKz9pTcTdmWhD8zVQ1-f7Zgj19ClHJfQkCJiJP0r3T_8</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2536672029</pqid></control><display><type>article</type><title>Automating Speedrun Routing: Overview and Vision</title><source>arXiv.org</source><source>Free E- Journals</source><creator>Groß, Matthias ; Zühlke, Dietlind ; Naujoks, Boris</creator><creatorcontrib>Groß, Matthias ; Zühlke, Dietlind ; Naujoks, Boris</creatorcontrib><description>Speedrunning in general means to play a video game fast, i.e. using all means at one's disposal to achieve a given goal in the least amount of time possible. To do so, a speedrun must be planned in advance, or routed, as referred to by the community. This paper focuses on discovering challenges and defining models needed when trying to approach the problem of routing algorithmically. To do so, this paper is split in two parts. The first part provides an overview of relevant speedrunning literature, extracting vital information and formulating criticism. Important categorizations are pointed out and a nomenclature is built to support professional discussion. The second part of this paper then refers to the actual speedrun routing optimization problem. Different concepts of graph representations are presented and their potential is discussed. Visions both for problem modeling as well as solving are presented and assessed regarding suitability and expected challenges. Finally, a first assessment of the applicability of existing optimization methods to the defined problem is made, including metaheuristics/EA and Deep Learning methods.</description><identifier>EISSN: 2331-8422</identifier><identifier>DOI: 10.48550/arxiv.2106.01182</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Computer & video games ; Computer Science - Neural and Evolutionary Computing ; Graph representations ; Graphical representations ; Optimization</subject><ispartof>arXiv.org, 2022-04</ispartof><rights>2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>http://creativecommons.org/licenses/by/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,781,785,886,27927</link.rule.ids><backlink>$$Uhttps://doi.org/10.1007/978-3-031-02462-7_30$$DView published paper (Access to full text may be restricted)$$Hfree_for_read</backlink><backlink>$$Uhttps://doi.org/10.48550/arXiv.2106.01182$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Groß, Matthias</creatorcontrib><creatorcontrib>Zühlke, Dietlind</creatorcontrib><creatorcontrib>Naujoks, Boris</creatorcontrib><title>Automating Speedrun Routing: Overview and Vision</title><title>arXiv.org</title><description>Speedrunning in general means to play a video game fast, i.e. using all means at one's disposal to achieve a given goal in the least amount of time possible. To do so, a speedrun must be planned in advance, or routed, as referred to by the community. This paper focuses on discovering challenges and defining models needed when trying to approach the problem of routing algorithmically. To do so, this paper is split in two parts. The first part provides an overview of relevant speedrunning literature, extracting vital information and formulating criticism. Important categorizations are pointed out and a nomenclature is built to support professional discussion. The second part of this paper then refers to the actual speedrun routing optimization problem. Different concepts of graph representations are presented and their potential is discussed. Visions both for problem modeling as well as solving are presented and assessed regarding suitability and expected challenges. Finally, a first assessment of the applicability of existing optimization methods to the defined problem is made, including metaheuristics/EA and Deep Learning methods.</description><subject>Computer & video games</subject><subject>Computer Science - Neural and Evolutionary Computing</subject><subject>Graph representations</subject><subject>Graphical representations</subject><subject>Optimization</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GOX</sourceid><recordid>eNotj0tLw0AUhQdBsNT-AFcGXCfeufPIxF0paoVCQYvbMMncyBSbxMlD_femrasDh4_D-Ri74ZBIoxTc2_DjxwQ56AQ4N3jBZigEj41EvGKLrtsDAOoUlRIzBsuhbw629_VH9NYSuTDU0WszHIuHaDtSGD19R7Z20bvvfFNfs8vKfna0-M852z097lbreLN9flktN7FVmMW6qCB1okJDGjPnqJJlqayizGqFKQmQWitb2FTaMi1cZQRxkGSQnKXCiTm7Pc-edPI2-IMNv_lRKz9pTcTdmWhD8zVQ1-f7Zgj19ClHJfQkCJiJP0r3T_8</recordid><startdate>20220421</startdate><enddate>20220421</enddate><creator>Groß, Matthias</creator><creator>Zühlke, Dietlind</creator><creator>Naujoks, Boris</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20220421</creationdate><title>Automating Speedrun Routing: Overview and Vision</title><author>Groß, Matthias ; Zühlke, Dietlind ; Naujoks, Boris</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a529-6bf07d3f28e629ddef4cc5a5e9a6527e304665aba74ac7bdf83e104e82edaebd3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Computer & video games</topic><topic>Computer Science - Neural and Evolutionary Computing</topic><topic>Graph representations</topic><topic>Graphical representations</topic><topic>Optimization</topic><toplevel>online_resources</toplevel><creatorcontrib>Groß, Matthias</creatorcontrib><creatorcontrib>Zühlke, Dietlind</creatorcontrib><creatorcontrib>Naujoks, Boris</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>arXiv Computer Science</collection><collection>arXiv.org</collection><jtitle>arXiv.org</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Groß, Matthias</au><au>Zühlke, Dietlind</au><au>Naujoks, Boris</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Automating Speedrun Routing: Overview and Vision</atitle><jtitle>arXiv.org</jtitle><date>2022-04-21</date><risdate>2022</risdate><eissn>2331-8422</eissn><abstract>Speedrunning in general means to play a video game fast, i.e. using all means at one's disposal to achieve a given goal in the least amount of time possible. To do so, a speedrun must be planned in advance, or routed, as referred to by the community. This paper focuses on discovering challenges and defining models needed when trying to approach the problem of routing algorithmically. To do so, this paper is split in two parts. The first part provides an overview of relevant speedrunning literature, extracting vital information and formulating criticism. Important categorizations are pointed out and a nomenclature is built to support professional discussion. The second part of this paper then refers to the actual speedrun routing optimization problem. Different concepts of graph representations are presented and their potential is discussed. Visions both for problem modeling as well as solving are presented and assessed regarding suitability and expected challenges. Finally, a first assessment of the applicability of existing optimization methods to the defined problem is made, including metaheuristics/EA and Deep Learning methods.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><doi>10.48550/arxiv.2106.01182</doi><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | EISSN: 2331-8422 |
ispartof | arXiv.org, 2022-04 |
issn | 2331-8422 |
language | eng |
recordid | cdi_arxiv_primary_2106_01182 |
source | arXiv.org; Free E- Journals |
subjects | Computer & video games Computer Science - Neural and Evolutionary Computing Graph representations Graphical representations Optimization |
title | Automating Speedrun Routing: Overview and Vision |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-18T11%3A45%3A28IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_arxiv&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Automating%20Speedrun%20Routing:%20Overview%20and%20Vision&rft.jtitle=arXiv.org&rft.au=Gro%C3%9F,%20Matthias&rft.date=2022-04-21&rft.eissn=2331-8422&rft_id=info:doi/10.48550/arxiv.2106.01182&rft_dat=%3Cproquest_arxiv%3E2536672029%3C/proquest_arxiv%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2536672029&rft_id=info:pmid/&rfr_iscdi=true |