CadiaPlayer: A Simulation-Based General Game Player
The aim of general game playing (GGP) is to create intelligent agents that can automatically learn how to play many different games at an expert level without any human intervention. The traditional design model for GGP agents has been to use a minimax-based game-tree search augmented with an automa...
Gespeichert in:
Veröffentlicht in: | IEEE transactions on computational intelligence and AI in games. 2009-03, Vol.1 (1), p.4-15 |
---|---|
Hauptverfasser: | , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 15 |
---|---|
container_issue | 1 |
container_start_page | 4 |
container_title | IEEE transactions on computational intelligence and AI in games. |
container_volume | 1 |
creator | Bjornsson, Y. Finnsson, H. |
description | The aim of general game playing (GGP) is to create intelligent agents that can automatically learn how to play many different games at an expert level without any human intervention. The traditional design model for GGP agents has been to use a minimax-based game-tree search augmented with an automatically learned heuristic evaluation function. The first successful GGP agents all followed that approach. In this paper, we describ e CadiaPlayer, a GGP agent employing a radically different approach: instead of a traditional game-tree search, it uses Monte Carlo simulations for its move decisions. Furthermore, we empirically evaluate different simulation-based approaches on a wide variety of games, introduce a domain-independent enhancement for automatically learning search-control knowledge to guide the simulation playouts, and show how to adapt the simulation searches to be more effective in single-agent games. CadiaPlayer has already proven its effectiveness by winning the 2007 and 2008 Association for the Advancement of Artificial Intelligence (AAAI) GGP competitions. |
doi_str_mv | 10.1109/TCIAIG.2009.2018702 |
format | Article |
fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_proquest_miscellaneous_889382010</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>4804731</ieee_id><sourcerecordid>889382010</sourcerecordid><originalsourceid>FETCH-LOGICAL-c373t-ee58a2b83fc5d02b458f52a49a869c8963db7899a50f2eff1a2f07b556ce4a483</originalsourceid><addsrcrecordid>eNpdkE1Lw0AQhhdRsNT-gl6CF0-puzv52PVWg8ZCQcEK3pZJMgsp-ai7zaH_3pQUD-5hdg7PO8w8jC0FXwnB9eMu26w3-UpyrsciVMrlFZsJHUHIE62u_3r1fcsW3u_5-AAgkcmMQYZVjR8Nnsg9Bevgs26HBo9134XP6KkKcurIYRPk2FIwcXfsxmLjaXH55-zr9WWXvYXb93yTrbdhCSkcQ6JYoSwU2DKuuCyiWNlYYqRRJbpUOoGqSJXWGHMryVqB0vK0iOOkpAgjBXP2MM09uP5nIH80be1LahrsqB-8UUqDGi_mI3n_j9z3g-vG5YyKUwAhQI8QTFDpeu8dWXNwdYvuZAQ3Z5NmMmnOJs3F5JhaTqmaiP4SkeJRCgJ-AREAbPo</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>857331139</pqid></control><display><type>article</type><title>CadiaPlayer: A Simulation-Based General Game Player</title><source>IEEE/IET Electronic Library</source><creator>Bjornsson, Y. ; Finnsson, H.</creator><creatorcontrib>Bjornsson, Y. ; Finnsson, H.</creatorcontrib><description>The aim of general game playing (GGP) is to create intelligent agents that can automatically learn how to play many different games at an expert level without any human intervention. The traditional design model for GGP agents has been to use a minimax-based game-tree search augmented with an automatically learned heuristic evaluation function. The first successful GGP agents all followed that approach. In this paper, we describ e CadiaPlayer, a GGP agent employing a radically different approach: instead of a traditional game-tree search, it uses Monte Carlo simulations for its move decisions. Furthermore, we empirically evaluate different simulation-based approaches on a wide variety of games, introduce a domain-independent enhancement for automatically learning search-control knowledge to guide the simulation playouts, and show how to adapt the simulation searches to be more effective in single-agent games. CadiaPlayer has already proven its effectiveness by winning the 2007 and 2008 Association for the Advancement of Artificial Intelligence (AAAI) GGP competitions.</description><identifier>ISSN: 1943-068X</identifier><identifier>ISSN: 2475-1502</identifier><identifier>EISSN: 1943-0698</identifier><identifier>EISSN: 2475-1510</identifier><identifier>DOI: 10.1109/TCIAIG.2009.2018702</identifier><identifier>CODEN: TCIARR</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Agents (artificial intelligence) ; Artificial intelligence ; Artificial intelligence (AI) ; Competitive intelligence ; Computational intelligence ; Computational modeling ; Computer simulation ; Decisions ; Games ; Humans ; Intelligent agent ; Learning ; Mathematical models ; Monte Carlo methods ; Read only memory ; Search methods ; Searching ; Simulation ; Studies ; Testing</subject><ispartof>IEEE transactions on computational intelligence and AI in games., 2009-03, Vol.1 (1), p.4-15</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2009</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c373t-ee58a2b83fc5d02b458f52a49a869c8963db7899a50f2eff1a2f07b556ce4a483</citedby><cites>FETCH-LOGICAL-c373t-ee58a2b83fc5d02b458f52a49a869c8963db7899a50f2eff1a2f07b556ce4a483</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4804731$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4804731$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Bjornsson, Y.</creatorcontrib><creatorcontrib>Finnsson, H.</creatorcontrib><title>CadiaPlayer: A Simulation-Based General Game Player</title><title>IEEE transactions on computational intelligence and AI in games.</title><addtitle>TCIAIG</addtitle><description>The aim of general game playing (GGP) is to create intelligent agents that can automatically learn how to play many different games at an expert level without any human intervention. The traditional design model for GGP agents has been to use a minimax-based game-tree search augmented with an automatically learned heuristic evaluation function. The first successful GGP agents all followed that approach. In this paper, we describ e CadiaPlayer, a GGP agent employing a radically different approach: instead of a traditional game-tree search, it uses Monte Carlo simulations for its move decisions. Furthermore, we empirically evaluate different simulation-based approaches on a wide variety of games, introduce a domain-independent enhancement for automatically learning search-control knowledge to guide the simulation playouts, and show how to adapt the simulation searches to be more effective in single-agent games. CadiaPlayer has already proven its effectiveness by winning the 2007 and 2008 Association for the Advancement of Artificial Intelligence (AAAI) GGP competitions.</description><subject>Agents (artificial intelligence)</subject><subject>Artificial intelligence</subject><subject>Artificial intelligence (AI)</subject><subject>Competitive intelligence</subject><subject>Computational intelligence</subject><subject>Computational modeling</subject><subject>Computer simulation</subject><subject>Decisions</subject><subject>Games</subject><subject>Humans</subject><subject>Intelligent agent</subject><subject>Learning</subject><subject>Mathematical models</subject><subject>Monte Carlo methods</subject><subject>Read only memory</subject><subject>Search methods</subject><subject>Searching</subject><subject>Simulation</subject><subject>Studies</subject><subject>Testing</subject><issn>1943-068X</issn><issn>2475-1502</issn><issn>1943-0698</issn><issn>2475-1510</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkE1Lw0AQhhdRsNT-gl6CF0-puzv52PVWg8ZCQcEK3pZJMgsp-ai7zaH_3pQUD-5hdg7PO8w8jC0FXwnB9eMu26w3-UpyrsciVMrlFZsJHUHIE62u_3r1fcsW3u_5-AAgkcmMQYZVjR8Nnsg9Bevgs26HBo9134XP6KkKcurIYRPk2FIwcXfsxmLjaXH55-zr9WWXvYXb93yTrbdhCSkcQ6JYoSwU2DKuuCyiWNlYYqRRJbpUOoGqSJXWGHMryVqB0vK0iOOkpAgjBXP2MM09uP5nIH80be1LahrsqB-8UUqDGi_mI3n_j9z3g-vG5YyKUwAhQI8QTFDpeu8dWXNwdYvuZAQ3Z5NmMmnOJs3F5JhaTqmaiP4SkeJRCgJ-AREAbPo</recordid><startdate>20090301</startdate><enddate>20090301</enddate><creator>Bjornsson, Y.</creator><creator>Finnsson, H.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20090301</creationdate><title>CadiaPlayer: A Simulation-Based General Game Player</title><author>Bjornsson, Y. ; Finnsson, H.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c373t-ee58a2b83fc5d02b458f52a49a869c8963db7899a50f2eff1a2f07b556ce4a483</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Agents (artificial intelligence)</topic><topic>Artificial intelligence</topic><topic>Artificial intelligence (AI)</topic><topic>Competitive intelligence</topic><topic>Computational intelligence</topic><topic>Computational modeling</topic><topic>Computer simulation</topic><topic>Decisions</topic><topic>Games</topic><topic>Humans</topic><topic>Intelligent agent</topic><topic>Learning</topic><topic>Mathematical models</topic><topic>Monte Carlo methods</topic><topic>Read only memory</topic><topic>Search methods</topic><topic>Searching</topic><topic>Simulation</topic><topic>Studies</topic><topic>Testing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bjornsson, Y.</creatorcontrib><creatorcontrib>Finnsson, H.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005–Present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998–Present</collection><collection>IEEE/IET Electronic Library</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEEE transactions on computational intelligence and AI in games.</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Bjornsson, Y.</au><au>Finnsson, H.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>CadiaPlayer: A Simulation-Based General Game Player</atitle><jtitle>IEEE transactions on computational intelligence and AI in games.</jtitle><stitle>TCIAIG</stitle><date>2009-03-01</date><risdate>2009</risdate><volume>1</volume><issue>1</issue><spage>4</spage><epage>15</epage><pages>4-15</pages><issn>1943-068X</issn><issn>2475-1502</issn><eissn>1943-0698</eissn><eissn>2475-1510</eissn><coden>TCIARR</coden><abstract>The aim of general game playing (GGP) is to create intelligent agents that can automatically learn how to play many different games at an expert level without any human intervention. The traditional design model for GGP agents has been to use a minimax-based game-tree search augmented with an automatically learned heuristic evaluation function. The first successful GGP agents all followed that approach. In this paper, we describ e CadiaPlayer, a GGP agent employing a radically different approach: instead of a traditional game-tree search, it uses Monte Carlo simulations for its move decisions. Furthermore, we empirically evaluate different simulation-based approaches on a wide variety of games, introduce a domain-independent enhancement for automatically learning search-control knowledge to guide the simulation playouts, and show how to adapt the simulation searches to be more effective in single-agent games. CadiaPlayer has already proven its effectiveness by winning the 2007 and 2008 Association for the Advancement of Artificial Intelligence (AAAI) GGP competitions.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/TCIAIG.2009.2018702</doi><tpages>12</tpages><oa>free_for_read</oa></addata></record> |
fulltext | fulltext_linktorsrc |
identifier | ISSN: 1943-068X |
ispartof | IEEE transactions on computational intelligence and AI in games., 2009-03, Vol.1 (1), p.4-15 |
issn | 1943-068X 2475-1502 1943-0698 2475-1510 |
language | eng |
recordid | cdi_proquest_miscellaneous_889382010 |
source | IEEE/IET Electronic Library |
subjects | Agents (artificial intelligence) Artificial intelligence Artificial intelligence (AI) Competitive intelligence Computational intelligence Computational modeling Computer simulation Decisions Games Humans Intelligent agent Learning Mathematical models Monte Carlo methods Read only memory Search methods Searching Simulation Studies Testing |
title | CadiaPlayer: A Simulation-Based General Game Player |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-12T07%3A35%3A57IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=CadiaPlayer:%20A%20Simulation-Based%20General%20Game%20Player&rft.jtitle=IEEE%20transactions%20on%20computational%20intelligence%20and%20AI%20in%20games.&rft.au=Bjornsson,%20Y.&rft.date=2009-03-01&rft.volume=1&rft.issue=1&rft.spage=4&rft.epage=15&rft.pages=4-15&rft.issn=1943-068X&rft.eissn=1943-0698&rft.coden=TCIARR&rft_id=info:doi/10.1109/TCIAIG.2009.2018702&rft_dat=%3Cproquest_RIE%3E889382010%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=857331139&rft_id=info:pmid/&rft_ieee_id=4804731&rfr_iscdi=true |