Artificial Adaptive Systems in Medicine: New Theories and Models for New Applications in the Real World

The General Philosophy of Artificial Adaptive Systems

Author(s): Massimo Buscema

Pp: 1-4 (4)

DOI: 10.2174/978160805042010901010001

* (Excluding Mailing and Handling)

Abstract

This chapter has the objective of describing the structure and placing in a taxonomy the Artificial Adaptive Systems (AAS). These systems form part of the vast world of Artificial Intelligence (AI) nowadays called more properly Artificial Sciences (AS). Artificial Sciences means those sciences for which an understanding of natural and/or cultural processes is achieved by the recreation of those processes through automatic models. In particular, Natural Computation tries to construct automatic models of complex processes, using the local interaction of elementary micro-processes, simulating the original process functioning. Such models organize themselves in space and time and connect in a non-linear way to the global process they are part of, trying to reproduce the complexity through the dynamic creation of specific and independent local rules that transform themselves in relation to the dynamics of the process. Natural Computation constitutes the alternative to Classical Computation (CC). This one, in fact, has great difficulty in facing natural/cultural processes, especially when it tries to impose external rules to understand and reproduce them, trying to formalize these processes in an artificial model. In Natural Computation ambit, Artificial Adaptive Systems are theories which generative algebras are able to create artificial models simulating natural phenomenon. The learning and growing process of the models is isomorphic to the natural process evolution, that is, it’s itself an artificial model comparable with the origin of the natural process. We are dealing with theories adopting the “time of development” of the model as a formal model of “time of process” itself. Artificial Adaptive Systems comprise Evolutive Systems and Learning Systems. Artificial Neural Networks are the more diffused and best-known Learning Systems models in Natural Computation.


Keywords: Artificial Adaptive Systems, Atrificial Neural Networks, Genetic Algorithms, Evolutionary Algorithms, Natural Computation.

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      • 16. vendor/laravel/framework/src/Illuminate/Routing/ControllerDispatcher.php:43
      • 17. vendor/laravel/framework/src/Illuminate/Routing/Route.php:260
    • (select j.user_access_id,j.volume_id,j.user_id,j.from_ip,j.to_ip,j.type,j.track,"Trial Access" as access_base_text, CASE j.type WHEN 111 THEN true ELSE false END as terms_popup, "S" as content_download_type, null as restricted_user_access_key, null as restricted_user_access_type, CASE j.track WHEN 0 THEN false ELSE true END as tracksAccessByVolume,"chapter" as access_level, j.chapter_id from `user_access_chapter_trail` as `j` inner join `user_access_info` as `info` on `j`.`user_access_id` = `info`.`user_access_id` where ((j.from_ip <= 309679543 and j.to_ip >=309679543 )) and ((j.from_date <= 1739834416 and j.to_date >= 1739834416 and j.perpetual= 0) or (j.from_date is null and j.to_date is null and j.perpetual= 1)) and j.chapter_id = 827) union (select j.user_access_id,j.volume_id,j.user_id,j.from_ip,j.to_ip,j.type,j.track,"Subscribed" as access_base_text, false as terms_popup, "S" as content_download_type, null as restricted_user_access_key, null as restricted_user_access_type, CASE j.track WHEN 0 THEN false ELSE true END as tracksAccessByVolume,"chapter" as access_level, j.chapter_id from `user_access_chapter_corporate` as `j` inner join `user_access_info` as `info` on `j`.`user_access_id` = `info`.`user_access_id` where ((j.from_ip <= 309679543 and j.to_ip >=309679543 )) and ((j.from_date <= 1739834416 and j.to_date >= 1739834416 and j.perpetual= 0) or (j.from_date is null and j.to_date is null and j.perpetual= 1)) and j.chapter_id = 827) union (select j.user_access_id,j.volume_id,j.user_id,j.from_ip,j.to_ip,j.type,j.track,"Token Based Access" as access_base_text, false as terms_popup, "R" as content_download_type, j.uc_id as restricted_user_access_key, "v" as restricted_user_access_type, CASE j.track WHEN 0 THEN false ELSE true END as tracksAccessByVolume,"chapter" as access_level, j.chapter_id from `user_access_chapter_token` as `j` inner join `user_access_info` as `info` on `j`.`user_access_id` = `info`.`user_access_id` where ((j.from_ip <= 309679543 and j.to_ip >=309679543 )) and ((j.from_date <= 1739834416 and j.to_date >= 1739834416 and j.perpetual= 0) or (j.from_date is null and j.to_date is null and j.perpetual= 1)) and j.chapter_id = 827)
      620μsalphaeurekaselec_live_10_06_2022UserAccess.php#1266
      Backtrace
      • 13. app/Models/UserAccess/UserAccess.php:1266
      • 14. app/Http/Controllers/ChapterController.php:1549
      • 15. vendor/laravel/framework/src/Illuminate/Routing/Controller.php:54
      • 16. vendor/laravel/framework/src/Illuminate/Routing/ControllerDispatcher.php:43
      • 17. vendor/laravel/framework/src/Illuminate/Routing/Route.php:260
    • select b.banner_link, b.banner_name, b.banner_script, b.banner_remarks, b.banner_file_name, b.banner_placement, bd.content_type, bd.content_id from `tbl_banner` as `b` inner join `tbl_banner_detail` as `bd` on `bd`.`banner_id` = `b`.`banner_id` inner join `users` as `u` on `u`.`id` = `b`.`updated_by` where `b`.`banner_ebook` = 1 and `bd`.`content_type` = 'B' and `bd`.`content_id` = 852 and `b`.`banner_from_date` <= 1739834416 and `b`.`banner_to_date` >= 1739834416 and `b`.`banner_status` = 'A' order by `b`.`banner_id` desc
      1.07msalphaeurekaselec_live_10_06_2022Banner.php#95
      Bindings
      • 0: 1
      • 1: B
      • 2: 852
      • 3: 1739834416
      • 4: 1739834416
      • 5: A
      Backtrace
      • 13. app/Models/Banner.php:95
      • 14. app/Http/helpers.php:404
      • 17. vendor/laravel/framework/src/Illuminate/Filesystem/Filesystem.php:124
      • 18. vendor/laravel/framework/src/Illuminate/View/Engines/PhpEngine.php:58
      • 19. vendor/laravel/framework/src/Illuminate/View/Engines/CompilerEngine.php:73
    • select count(*) as aggregate from `uc_cart_products` where cart_id='eefdd9c4a606d718b5efb59fcb9442ad'
      200μsalphaeurekaselec_live_10_06_2022Cart.php#271
      Backtrace
      • 15. app/Models/Cart.php:271
      • 16. view::layouts._header:260
      • 18. vendor/laravel/framework/src/Illuminate/Filesystem/Filesystem.php:124
      • 19. vendor/laravel/framework/src/Illuminate/View/Engines/PhpEngine.php:58
      • 20. vendor/laravel/framework/src/Illuminate/View/Engines/CompilerEngine.php:73
    App\Models\Keywords
    5Keywords.php#?
    App\Models\Chapter
    1Chapter.php#?
    App\Models\Ebook
    1Ebook.php#?
        _token
        jFUrqw4A1i0UJJWSaN49ixrUCxQWXeM5r5e9YbgA
        uc_cart_id
        eefdd9c4a606d718b5efb59fcb9442ad
        _previous
        array:1 [ "url" => "https://alpha.eurekaselect.com/chapter/827" ]
        _flash
        array:2 [ "old" => [] "new" => [] ]
        PHPDEBUGBAR_STACK_DATA
        []
        path_info
        /chapter/827
        status_code
        200
        
        status_text
        OK
        format
        html
        content_type
        text/html; charset=UTF-8
        request_query
        []
        
        request_request
        []
        
        request_headers
        0 of 0
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        request_cookies
        0 of 0
        array:4 [ "fpestid" => null "wayfindertarget" => null "XSRF-TOKEN" => "jFUrqw4A1i0UJJWSaN49ixrUCxQWXeM5r5e9YbgA" "alphaeurekaselectnet_session" => "u4L73E0hdEHBGaXcSwB35BIyCmDYNibZ3sPUhcbz" ]
        response_headers
        0 of 0
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        session_attributes
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        array:5 [ "_token" => "jFUrqw4A1i0UJJWSaN49ixrUCxQWXeM5r5e9YbgA" "uc_cart_id" => "eefdd9c4a606d718b5efb59fcb9442ad" "_previous" => array:1 [ "url" => "https://alpha.eurekaselect.com/chapter/827" ] "_flash" => array:2 [ "old" => [] "new" => [] ] "PHPDEBUGBAR_STACK_DATA" => [] ]
        ClearShow all
        Date ↕MethodURLData
        #12025-02-17 23:20:16GET/chapter/827112197