Parallel Computation Is ESS
There are enormous amount of examples of Computation in nature, exemplified across multiple species in biology. One crucial aim for these computations across all life forms their ability to learn and thereby increase the chance of their survival. In the current paper a formal definition of autonomou...
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| creator | Mondal, Nabarun Ghosh, Partha P |
| description | There are enormous amount of examples of Computation in nature, exemplified
across multiple species in biology. One crucial aim for these computations
across all life forms their ability to learn and thereby increase the chance of
their survival. In the current paper a formal definition of autonomous learning
is proposed. From that definition we establish a Turing Machine model for
learning, where rule tables can be added or deleted, but can not be modified.
Sequential and parallel implementations of this model are discussed. It is
found that for general purpose learning based on this model, the
implementations capable of parallel execution would be evolutionarily stable.
This is proposed to be of the reasons why in Nature parallelism in computation
is found in abundance. |
| doi_str_mv | 10.48550/arxiv.1304.0160 |
| format | Article |
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across multiple species in biology. One crucial aim for these computations
across all life forms their ability to learn and thereby increase the chance of
their survival. In the current paper a formal definition of autonomous learning
is proposed. From that definition we establish a Turing Machine model for
learning, where rule tables can be added or deleted, but can not be modified.
Sequential and parallel implementations of this model are discussed. It is
found that for general purpose learning based on this model, the
implementations capable of parallel execution would be evolutionarily stable.
This is proposed to be of the reasons why in Nature parallelism in computation
is found in abundance.</description><identifier>DOI: 10.48550/arxiv.1304.0160</identifier><language>eng</language><subject>Computer Science - Artificial Intelligence ; Computer Science - Computer Science and Game Theory ; Computer Science - Learning</subject><creationdate>2013-03</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/1304.0160$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.1304.0160$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Mondal, Nabarun</creatorcontrib><creatorcontrib>Ghosh, Partha P</creatorcontrib><title>Parallel Computation Is ESS</title><description>There are enormous amount of examples of Computation in nature, exemplified
across multiple species in biology. One crucial aim for these computations
across all life forms their ability to learn and thereby increase the chance of
their survival. In the current paper a formal definition of autonomous learning
is proposed. From that definition we establish a Turing Machine model for
learning, where rule tables can be added or deleted, but can not be modified.
Sequential and parallel implementations of this model are discussed. It is
found that for general purpose learning based on this model, the
implementations capable of parallel execution would be evolutionarily stable.
This is proposed to be of the reasons why in Nature parallelism in computation
is found in abundance.</description><subject>Computer Science - Artificial Intelligence</subject><subject>Computer Science - Computer Science and Game Theory</subject><subject>Computer Science - Learning</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotzksLgkAUBeDZtIhqHwThH9DudR7pMsQeEBTUXq7NDAhjxmRR_z6tNudwNoePsSlCJBIpYUH-VT0j5CAiQAVDNjuSJ-eMC7Kmvj1aaqvmGuzuQX46jdnAkrubyb9H7LzOz9k23B82u2y1D0lJ6EIkKLVCiFNLYmkNKKmNxW5JLYyxFxBCk8aSY1KmeslRGc4pwTiOS-QjNv_dfnHFzVc1-XfRI4seyT-e_jUC</recordid><startdate>20130331</startdate><enddate>20130331</enddate><creator>Mondal, Nabarun</creator><creator>Ghosh, Partha P</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20130331</creationdate><title>Parallel Computation Is ESS</title><author>Mondal, Nabarun ; Ghosh, Partha P</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a650-a64815d61029fa47fe065def19fa5d4eefc044dad1b318b9d7316e33a81222b13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Computer Science - Artificial Intelligence</topic><topic>Computer Science - Computer Science and Game Theory</topic><topic>Computer Science - Learning</topic><toplevel>online_resources</toplevel><creatorcontrib>Mondal, Nabarun</creatorcontrib><creatorcontrib>Ghosh, Partha P</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Mondal, Nabarun</au><au>Ghosh, Partha P</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Parallel Computation Is ESS</atitle><date>2013-03-31</date><risdate>2013</risdate><abstract>There are enormous amount of examples of Computation in nature, exemplified
across multiple species in biology. One crucial aim for these computations
across all life forms their ability to learn and thereby increase the chance of
their survival. In the current paper a formal definition of autonomous learning
is proposed. From that definition we establish a Turing Machine model for
learning, where rule tables can be added or deleted, but can not be modified.
Sequential and parallel implementations of this model are discussed. It is
found that for general purpose learning based on this model, the
implementations capable of parallel execution would be evolutionarily stable.
This is proposed to be of the reasons why in Nature parallelism in computation
is found in abundance.</abstract><doi>10.48550/arxiv.1304.0160</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Artificial Intelligence Computer Science - Computer Science and Game Theory Computer Science - Learning |
| title | Parallel Computation Is ESS |
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