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Operant conditioning automata model and a bionic autonomous learning process
This paper presents an operant conditioning automata model (hereinafter referred to ¿OCM¿), and designs a bionic autonomous learning method which can be used to describe and simulate a bionic autonomous learning process. The model can be considered as an active learning permitting to select a better...
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| creator | Xiaogang Ruan Yuanyuan Gao Hongjun Song |
| description | This paper presents an operant conditioning automata model (hereinafter referred to ¿OCM¿), and designs a bionic autonomous learning method which can be used to describe and simulate a bionic autonomous learning process. The model can be considered as an active learning permitting to select a better action according to psychology behavior propensity, and the aim is to learn to find the optimal action finally. During the learning process, the system selects an action randomly according to the probability distribution of action selection, which is updated by the behavior propensity from the environment. We apply our model on skinner-pigeon experiment. In simulation, we confirmed that this model could successfully simulate operant conditioning. |
| doi_str_mv | 10.1109/ICICISYS.2009.5358370 |
| format | conference_proceeding |
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In simulation, we confirmed that this model could successfully simulate operant conditioning.</description><subject>Animals</subject><subject>Artificial intelligence</subject><subject>Bionic autonomous learning process</subject><subject>Humanoid robots</subject><subject>Intelligent robots</subject><subject>Learning automata</subject><subject>Mathematical model</subject><subject>Operant conditioning automata model</subject><subject>Paper technology</subject><subject>Probability distribution</subject><subject>Probability distribution of action selection</subject><subject>Psychology</subject><subject>Robotics and automation</subject><subject>Skinner-pigeon experiment</subject><subject>styling</subject><isbn>9781424447541</isbn><isbn>1424447542</isbn><isbn>9781424447381</isbn><isbn>1424447380</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNpNUMFKw0AQXZGCWvsFIuwPNO7u7DY7RylqC4EeqgdPZZKdSqTJhmx68O9Naw_OHIbHezzmPSEetcq0Vvi0Xo67_dxmRinMHDgPuboSM8y9tsZam4PX1_-xs3oi7k5yVE6juhGzlL7VONaBNnArik3HPbWDrGIb6qGObd1-SToOsaGBZBMDHyS1QZIsT2R15trYxGOSB6b-rO_6WHFK92Kyp0Pi2eVOxcfry_tyNS82b-vlczGvde6Geb4Ag8HqgFSRQUYYHwbDin0VPCogUJwHWowZADWaynOJtvTl3u4VOpiKhz_fmpl3XV831P_sLoXAL9V2UuQ</recordid><startdate>200911</startdate><enddate>200911</enddate><creator>Xiaogang Ruan</creator><creator>Yuanyuan Gao</creator><creator>Hongjun Song</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200911</creationdate><title>Operant conditioning automata model and a bionic autonomous learning process</title><author>Xiaogang Ruan ; Yuanyuan Gao ; Hongjun Song</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-76329d41d9aca29e9378132e0e8cd8903a30e7da624439192c8eb94b8bf4f0953</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Animals</topic><topic>Artificial intelligence</topic><topic>Bionic autonomous learning process</topic><topic>Humanoid robots</topic><topic>Intelligent robots</topic><topic>Learning automata</topic><topic>Mathematical model</topic><topic>Operant conditioning automata model</topic><topic>Paper technology</topic><topic>Probability distribution</topic><topic>Probability distribution of action selection</topic><topic>Psychology</topic><topic>Robotics and automation</topic><topic>Skinner-pigeon experiment</topic><topic>styling</topic><toplevel>online_resources</toplevel><creatorcontrib>Xiaogang Ruan</creatorcontrib><creatorcontrib>Yuanyuan Gao</creatorcontrib><creatorcontrib>Hongjun Song</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEL</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Xiaogang Ruan</au><au>Yuanyuan Gao</au><au>Hongjun Song</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Operant conditioning automata model and a bionic autonomous learning process</atitle><btitle>2009 IEEE International Conference on Intelligent Computing and Intelligent Systems</btitle><stitle>ICICISYS</stitle><date>2009-11</date><risdate>2009</risdate><volume>2</volume><spage>200</spage><epage>204</epage><pages>200-204</pages><isbn>9781424447541</isbn><isbn>1424447542</isbn><eisbn>9781424447381</eisbn><eisbn>1424447380</eisbn><abstract>This paper presents an operant conditioning automata model (hereinafter referred to ¿OCM¿), and designs a bionic autonomous learning method which can be used to describe and simulate a bionic autonomous learning process. The model can be considered as an active learning permitting to select a better action according to psychology behavior propensity, and the aim is to learn to find the optimal action finally. During the learning process, the system selects an action randomly according to the probability distribution of action selection, which is updated by the behavior propensity from the environment. We apply our model on skinner-pigeon experiment. In simulation, we confirmed that this model could successfully simulate operant conditioning.</abstract><pub>IEEE</pub><doi>10.1109/ICICISYS.2009.5358370</doi><tpages>5</tpages></addata></record> |
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| identifier | ISBN: 9781424447541 |
| ispartof | 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, 2009, Vol.2, p.200-204 |
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| language | eng |
| recordid | cdi_ieee_primary_5358370 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Animals Artificial intelligence Bionic autonomous learning process Humanoid robots Intelligent robots Learning automata Mathematical model Operant conditioning automata model Paper technology Probability distribution Probability distribution of action selection Psychology Robotics and automation Skinner-pigeon experiment styling |
| title | Operant conditioning automata model and a bionic autonomous learning process |
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