a methodological approach to model cbr-based systems · 2020. 9. 10. · adipiscing elit. etiam...

Journal of Computer and Communications, 2020, 08, 1-16

http://www.scirp.org/journal/jccISSN Online:2327-5227ISSN Print:2327-5219

A Methodological Approach to Model CBR-basedSystems

Eliseu M. Oliveira1, Rafael F. Reale12 and Joberto S. B. Martins1

1Universidade Salvador - UNIFACS, Salvador, Brazil2Instituto Federal da Bahia - IFBA, Valena, BrazilEmail: [email protected], [email protected], [email protected]

How to cite this paper: Oliveira,M. E.; Reale, R. F.; Martins, J.S. B. (2020) A Methodological Ap-proach to Model CBR-based Sys-tems, Journal of Computer andCommunications, 08, 1-16.https://dx.doi.org/10.4236/jcc.2020.89001.2020.89001

Received: July 22, 2020

Accepted: September 01, 2020

Published: September 04, 2020

Copyright c© 2020 by author(s) andScientific Research Publishing Inc.This work is licensed under the Cre-ative Commons Attribution Inter-national License (CC BY 4.0).http://creativecommons.org/licenses/by/4.0/

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Proiniaculis lectus.Sed mattis, erat sit amet gravidamalesuada, elit augue egestas diam,tempus scelerisque nunc nisl vi-tae libero. Sed consequat feugiatmassa. Nunc porta, eros in eleifendvarius, erat leo rutrum dui, nonconvallis lectus orci ut nibh. Sedlorem massa, nonummy quis, eges-tas id, condimentum at, nisl. Mae-cenas at nibh. Aliquam et augueat nunc pellentesque ullamcorper.Duis nisl nibh, laoreet suscipit, con-vallis ut, rutrum id, enim. Phasel-lus odio. Nulla nulla elit, molestienon, scelerisque at, vestibulum eu,nulla. Ut odio nisl, facilisis id, mol-lis et, scelerisque nec, enim. Ae-nean sem leo, pellentesque sit amet,scelerisque sit amet, vehicula pel-lentesque, sapien.Sed consequat tellus et tortor. Uttempor laoreet quam. Nullam idwisi a libero tristique semper. Nul-lam nisl massa, rutrum ut, eges-tas semper, mollis id, leo. Nullaac massa eu risus blandit mattis.Mauris ut nunc. In hac habitasseplatea dictumst. Aliquam eget tor-tor. Quisque dapibus pede in erat.Nunc enim. In dui nulla, com-modo at, consectetuer nec, male-suada nec, elit. Aliquam ornare tel-lus eu urna. Sed nec metus. Cumsociis natoque penatibus et mag-nis dis parturient montes, nasceturridiculus mus. Pellentesque habi-tant morbi tristique senectus et ne-tus et malesuada fames ac turpisegestas.Phasellus id magna. Duis male-suada interdum arcu. Integer me-tus. Morbi pulvinar pellentesquemi. Suspendisse sed est eu magnamolestie egestas. Quisque mi lorem,pulvinar eget, egestas quis, luctusat, ante. Proin auctor vehicula pu-rus. Fusce ac nisl aliquam ante hen-drerit pellentesque. Class aptenttaciti sociosqu ad litora torquentper conubia nostra, per inceptos hy-menaeos. Morbi wisi. Etiam arcumauris, facilisis sed, eleifend non,nonummy ut, pede. Cras ut lacustempor metus mollis placerat. Vi-vamus eu tortor vel metus interdummalesuada.Sed eleifend, eros sit amet fau-cibus elementum, urna sapien con-sectetuer mauris, quis egestas leojusto non risus. Morbi non felisac libero vulputate fringilla. Mau-ris libero eros, lacinia non, sodalesquis, dapibus porttitor, pede. Classaptent taciti sociosqu ad litoratorquent per conubia nostra, per in-ceptos hymenaeos. Morbi dapibusmauris condimentum nulla. Cumsociis natoque penatibus et mag-nis dis parturient montes, nasceturridiculus mus. Etiam sit amet erat.Nulla varius. Etiam tincidunt duivitae turpis. Donec leo. Morbivulputate convallis est. Integer ali-quet. Pellentesque aliquet sodalesurna.Nullam eleifend justo in nisl. In hachabitasse platea dictumst. Morbinonummy. Aliquam ut felis. Invelit leo, dictum vitae, posuere id,vulputate nec, ante. Maecenas vi-tae pede nec dui dignissim suscipit.Morbi magna. Vestibulum id puruseget velit laoreet laoreet. Praesentsed leo vel nibh convallis blandit.Ut rutrum. Donec nibh. Donec in-terdum. Fusce sed pede sit ametelit rhoncus ultrices. Nullam atenim vitae pede vehicula iaculis.Class aptent taciti sociosqu adlitora torquent per conubia nostra,per inceptos hymenaeos. Aeneannonummy turpis id odio. Inte-ger euismod imperdiet turpis. Utnec leo nec diam imperdiet lacinia.Etiam eget lacus eget mi ultriciesposuere. In placerat tristique tor-tor. Sed porta vestibulum me-tus. Nulla iaculis sollicitudin pede.Fusce luctus tellus in dolor. Cur-abitur auctor velit a sem. Morbisapien. Class aptent taciti sociosquad litora torquent per conubia nos-tra, per inceptos hymenaeos. Donecadipiscing urna vehicula nunc. Sedornare leo in leo. In rhoncus leo utdui. Aenean dolor quam, volutpatnec, fringilla id, consectetuer vel,pede.Nulla malesuada risus ut urna. Ae-nean pretium velit sit amet me-tus. Duis iaculis. In hac habitasseplatea dictumst. Nullam molestieturpis eget nisl. Duis a massaid pede dapibus ultricies. Sedeu leo. In at mauris sit amettortor bibendum varius. Phasel-lus justo risus, posuere in, sagittisac, varius vel, tortor. Quisque idenim. Phasellus consequat, liberopretium nonummy fringilla, tortorlacus vestibulum nunc, ut rhoncusligula neque id justo. Nullam ac-cumsan euismod nunc. Proin vi-tae ipsum ac metus dictum tem-pus. Nam ut wisi. Quisque tortorfelis, interdum ac, sodales a, sem-per a, sem. Curabitur in velit sitamet dui tristique sodales. Viva-mus mauris pede, lacinia eget, pel-lentesque quis, scelerisque eu, est.Aliquam risus. Quisque bibendumpede eu dolor.Donec tempus neque vitae est. Ae-nean egestas odio sed risus ul-lamcorper ullamcorper. Sed innulla a tortor tincidunt egestas.Nam sapien tortor, elementum sitamet, aliquam in, porttitor fau-cibus, enim. Nullam congue sus-cipit nibh. Quisque convallis.Praesent arcu nibh, vehicula eget,accumsan eu, tincidunt a, nibh.Suspendisse vulputate, tortor quisadipiscing viverra, lacus nibh dig-nissim tellus, eu suscipit risus antefringilla diam. Quisque a liberovel pede imperdiet aliquet. Pellen-tesque nunc nibh, eleifend a, con-sequat consequat, hendrerit nec,diam. Sed urna. Maecenas laoreeteleifend neque. Vivamus purusodio, eleifend non, iaculis a, ultri-ces sit amet, urna. Mauris faucibusodio vitae risus. In nisl. Praesentpurus. Integer iaculis, sem eu eges-tas lacinia, lacus pede scelerisqueaugue, in ullamcorper dolor eros aclacus. Nunc in libero.Fusce suscipit cursus sem. Viva-mus risus mi, egestas ac, imperdietvarius, faucibus quis, leo. Aeneantincidunt. Donec suscipit. Crasid justo quis nibh scelerisque dig-nissim. Aliquam sagittis elemen-tum dolor. Aenean consectetuerjusto in pede. Curabitur ullamcor-per ligula nec orci. Aliquam pu-rus turpis, aliquam id, ornare vitae,porttitor non, wisi. Maecenas luc-tus porta lorem. Donec vitae ligulaeu ante pretium varius. Proin tor-tor metus, convallis et, hendreritnon, scelerisque in, urna. Cras quislibero eu ligula bibendum tempor.Vivamus tellus quam, malesuadaeu, tempus sed, tempor sed, velit.Donec lacinia auctor libero.Praesent sed neque id pede mollisrutrum. Vestibulum iaculis risus.Pellentesque lacus. Ut quis nuncsed odio malesuada egestas. Duisa magna sit amet ligula tristiquepretium. Ut pharetra. Vestibulumimperdiet magna nec wisi. Maurisconvallis. Sed accumsan sollicitudinmassa. Sed id enim. Nunc pedeenim, lacinia ut, pulvinar quis, sus-cipit semper, elit. Cras accumsanerat vitae enim. Cras sollicitudin.Vestibulum rutrum blandit massa.Sed gravida lectus ut purus. Morbilaoreet magna. Pellentesque euwisi. Proin turpis. Integer sollici-tudin augue nec dui. Fusce lectus.Vivamus faucibus nulla nec lacus.Integer diam. Pellentesque sodales,enim feugiat cursus volutpat, semmauris dignissim mauris, quis con-sequat sem est fermentum ligula.Nullam justo lectus, condimentumsit amet, posuere a, fringilla mol-lis, felis. Morbi nulla nibh, pel-lentesque at, nonummy eu, sollic-itudin nec, ipsum. Cras neque.Nunc augue. Nullam vitae quamid quam pulvinar blandit. Nunc sitamet orci. Aliquam erat elit, phare-tra nec, aliquet a, gravida in, mi.Quisque urna enim, viverra quis,suscipit quis, tincidunt ut, sapien.Cras placerat consequat sem. Cur-abitur ac diam. Curabitur diamtortor, mollis et, viverra ac, tempusvel, metus.Curabitur ac lorem. Vivamus nonjusto in dui mattis posuere. Etiamaccumsan ligula id pede. Maecenastincidunt diam nec velit. Praesentconvallis sapien ac est. Aliquamullamcorper euismod nulla. Inte-ger mollis enim vel tortor. Nullasodales placerat nunc. Sed tem-pus rutrum wisi. Duis accumsangravida purus. Nunc nunc. Etiamfacilisis dui eu sem. Vestibulumsemper. Praesent eu eros. Vestibu-lum tellus nisl, dapibus id, vestibu-lum sit amet, placerat ac, mauris.Maecenas et elit ut erat placeratdictum. Nam feugiat, turpis et so-dales volutpat, wisi quam rhoncusneque, vitae aliquam ipsum sapienvel enim. Maecenas suscipit cursusmi.Quisque consectetuer. In suscipitmauris a dolor pellentesque con-sectetuer. Mauris convallis nequenon erat. In lacinia. Pellentesqueleo eros, sagittis quis, fermentumquis, tincidunt ut, sapien. Maece-nas sem. Curabitur eros odio, inter-dum eu, feugiat eu, porta ac, nisl.Curabitur nunc. Etiam fermentumconvallis velit. Pellentesque laoreetlacus. Quisque sed elit. Nam quistellus. Aliquam tellus arcu, adipisc-ing non, tincidunt eleifend, adipisc-ing quis, augue. Vivamus elemen-tum placerat enim. Suspendisse uttortor. Integer faucibus adipiscingfelis. Aenean consectetuer mattislectus. Morbi malesuada faucibusdolor. Nam lacus. Etiam arculibero, malesuada vitae, aliquam vi-tae, blandit tristique, nisl.Maecenas accumsan dapibussapien. Duis pretium iaculis arcu.Curabitur ut lacus. Aliquamvulputate. Suspendisse ut purussed sem tempor rhoncus. Utquam dui, fringilla at, dictum eget,ultricies quis, quam. Etiam semest, pharetra non, vulputate in,pretium at, ipsum. Nunc sempersagittis orci. Sed scelerisque sus-cipit diam. Ut volutpat, dolor atullamcorper tristique, eros purusmollis quam, sit amet ornare antenunc et enim.Phasellus fringilla, metus id feu-giat consectetuer, lacus wisi ul-trices tellus, quis lobortis nibhlorem quis tortor. Donec egestasornare nulla. Mauris mi tellus,porta faucibus, dictum vel, non-ummy in, est. Aliquam erat vo-lutpat. In tellus magna, portti-tor lacinia, molestie vitae, pellen-tesque eu, justo. Class aptent tac-iti sociosqu ad litora torquent perconubia nostra, per inceptos hy-menaeos. Sed orci nibh, scelerisquesit amet, suscipit sed, placerat vel,diam. Vestibulum nonummy vulpu-tate orci. Donec et velit ac arcu in-terdum semper. Morbi pede orci,cursus ac, elementum non, vehic-ula ut, lacus. Cras volutpat. Namvel wisi quis libero venenatis plac-erat. Aenean sed odio. Quisqueposuere purus ac orci. Vivamusodio. Vivamus varius, nulla sitamet semper viverra, odio mau-ris consequat lacus, at vestibulumneque arcu eu tortor. Donec iac-ulis tincidunt tellus. Aliquam eratvolutpat. Curabitur magna lorem,dignissim volutpat, viverra et, adip-iscing nec, dolor. Praesent lacusmauris, dapibus vitae, sollicitudinsit amet, nonummy eget, ligula.Cras egestas ipsum a nisl. Viva-mus varius dolor ut dolor. Fuscevel enim. Pellentesque accumsanligula et eros. Cras id lacus nontortor facilisis facilisis. Etiam nislelit, cursus sed, fringilla in, conguenec, urna. Cum sociis natoque pe-natibus et magnis dis parturientmontes, nascetur ridiculus mus. In-teger at turpis. Cum sociis na-toque penatibus et magnis dis par-turient montes, nascetur ridiculusmus. Duis fringilla, ligula sed portafringilla, ligula wisi commodo felis,ut adipiscing felis dui in enim. Sus-pendisse malesuada ultrices ante.Pellentesque scelerisque augue sitamet urna. Nulla volutpat ali-quet tortor. Cras aliquam, tellusat aliquet pellentesque, justo sapiencommodo leo, id rhoncus sapienquam at erat. Nulla commodo, wisieget sollicitudin pretium, orci orcialiquam orci, ut cursus turpis justoet lacus. Nulla vel tortor. Quisqueerat elit, viverra sit amet, sagittiseget, porta sit amet, lacus.In hac habitasse platea dictumst.Proin at est. Curabitur tempusvulputate elit. Pellentesque sem.Praesent eu sapien. Duis elitmagna, aliquet at, tempus sed, ve-hicula non, enim. Morbi viverraarcu nec purus. Vivamus fringilla,enim et commodo malesuada, tor-tor metus elementum ligula, nec ali-quet est sapien ut lectus. Aliquammi. Ut nec elit. Fusce euismod luc-tus tellus. Curabitur scelerisque.Nullam purus. Nam ultricies ac-cumsan magna. Morbi pulvinarlorem sit amet ipsum. Donecut justo vitae nibh mollis congue.Fusce quis diam. Praesent tempuseros ut quam.Donec in nisl. Fusce vitae est. Vi-vamus ante ante, mattis laoreet, po-suere eget, congue vel, nunc. Fuscesem. Nam vel orci eu eros viverraluctus. Pellentesque sit amet au-gue. Nunc sit amet ipsum et lacusvarius nonummy. Integer rutrumsem eget wisi. Aenean eu sapien.Quisque ornare dignissim mi. Duisa urna vel risus pharetra imperdiet.Suspendisse potenti.Morbi justo. Aenean nec dolor.In hac habitasse platea dictumst.Proin nonummy porttitor velit. Sedsit amet leo nec metus rhoncus var-ius. Cras ante. Vestibulum com-modo sem tincidunt massa. Namjusto. Aenean luctus, felis et condi-mentum lacinia, lectus enim pulv-inar purus, non porta velit nisl sederos. Suspendisse consequat. Mau-ris a dui et tortor mattis pretium.Sed nulla metus, volutpat id, ali-quam eget, ullamcorper ut, ipsum.Morbi eu nunc. Praesent pretium.Duis aliquam pulvinar ligula. Utblandit egestas justo. Quisque po-suere metus viverra pede.Vivamus sodales elementum neque.Vivamus dignissim accumsanneque. Sed at enim. Vestibulumnonummy interdum purus. Maurisornare velit id nibh pretium ul-tricies. Fusce tempor pellentesqueodio. Vivamus augue purus, laoreetin, scelerisque vel, commodo id,wisi. Duis enim. Nulla interdum,nunc eu semper eleifend, enim dolorpretium elit, ut commodo ligulanisl a est. Vivamus ante. Nulla leomassa, posuere nec, volutpat vitae,rhoncus eu, magna.Quisque facilisis auctor sapien. Pel-lentesque gravida hendrerit lec-tus. Mauris rutrum sodales sapien.Fusce hendrerit sem vel lorem. In-teger pellentesque massa vel au-gue. Integer elit tortor, feugiatquis, sagittis et, ornare non, la-cus. Vestibulum posuere pellen-tesque eros. Quisque venenatis ip-sum dictum nulla. Aliquam quisquam non metus eleifend interdum.Nam eget sapien ac mauris male-suada adipiscing. Etiam eleifendneque sed quam. Nulla facilisi.Proin a ligula. Sed id dui eunibh egestas tincidunt. Suspendissearcu.Maecenas dui. Aliquam volutpatauctor lorem. Cras placerat est vi-tae lectus. Curabitur massa lec-tus, rutrum euismod, dignissim ut,dapibus a, odio. Ut eros erat,vulputate ut, interdum non, portaeu, erat. Cras fermentum, felis inporta congue, velit leo facilisis odio,vitae consectetuer lorem quam vi-tae orci. Sed ultrices, pede euplacerat auctor, ante ligula rutrumtellus, vel posuere nibh lacus necnibh. Maecenas laoreet dolor atenim. Donec molestie dolor nec me-tus. Vestibulum libero. Sed quiserat. Sed tristique. Duis pede leo,fermentum quis, consectetuer eget,vulputate sit amet, erat.Donec vitae velit. Suspendisseporta fermentum mauris. Ut velnunc non mauris pharetra varius.Duis consequat libero quis urna.Maecenas at ante. Vivamus var-ius, wisi sed egestas tristique, odiowisi luctus nulla, lobortis dictumdolor ligula in lacus. Vivamus ali-quam, urna sed interdum porttitor,metus orci interdum odio, sit ameteuismod lectus felis et leo. Prae-sent ac wisi. Nam suscipit vestibu-lum sem. Praesent eu ipsum vi-tae pede cursus venenatis. Duis sedodio. Vestibulum eleifend. Nulla utmassa. Proin rutrum mattis sapien.Curabitur dictum gravida ante.Phasellus placerat vulputate quam.Maecenas at tellus. Pellentesqueneque diam, dignissim ac, venenatisvitae, consequat ut, lacus. Namnibh. Vestibulum fringilla arcumollis arcu. Sed et turpis. Donecsem tellus, volutpat et, varius eu,commodo sed, lectus. Lorem ip-sum dolor sit amet, consectetueradipiscing elit. Quisque enim arcu,suscipit nec, tempus at, imperdietvel, metus. Morbi volutpat purusat erat. Donec dignissim, sem idsemper tempus, nibh massa eleifendturpis, sed pellentesque wisi purussed libero. Nullam lobortis tor-tor vel risus. Pellentesque conse-quat nulla eu tellus. Donec velit.Aliquam fermentum, wisi ac rhon-cus iaculis, tellus nunc malesuadaorci, quis volutpat dui magna id mi.Nunc vel ante. Duis vitae lacus.Cras nec ipsum.Morbi nunc. Aliquam consectetuervarius nulla. Phasellus eros. Crasdapibus porttitor risus. Maece-nas ultrices mi sed diam. Prae-sent gravida velit at elit vehiculaporttitor. Phasellus nisl mi, sagit-tis ac, pulvinar id, gravida sit amet,erat. Vestibulum est. Lorem ipsumdolor sit amet, consectetuer adip-iscing elit. Curabitur id sem ele-mentum leo rutrum hendrerit. Utat mi. Donec tincidunt faucibusmassa. Sed turpis quam, sollici-tudin a, hendrerit eget, pretium ut,nisl. Duis hendrerit ligula. Nuncpulvinar congue urna.Nunc velit. Nullam elit sapien,eleifend eu, commodo nec, sem-per sit amet, elit. Nulla lectusrisus, condimentum ut, laoreet eget,viverra nec, odio. Proin lobor-tis. Curabitur dictum arcu velwisi. Cras id nulla venenatis tor-tor congue ultrices. Pellentesqueeget pede. Sed eleifend sagittiselit. Nam sed tellus sit amet lec-tus ullamcorper tristique. Maurisenim sem, tristique eu, accumsanat, scelerisque vulputate, neque.Quisque lacus. Donec et ipsum sitamet elit nonummy aliquet. Sedviverra nisl at sem. Nam diam.Mauris ut dolor. Curabitur ornaretortor cursus velit.Morbi tincidunt posuere arcu. Crasvenenatis est vitae dolor. Viva-mus scelerisque semper mi. Donecipsum arcu, consequat scelerisque,viverra id, dictum at, metus.Lorem ipsum dolor sit amet, con-sectetuer adipiscing elit. Ut pedesem, tempus ut, porttitor biben-dum, molestie eu, elit. Suspendissepotenti. Sed id lectus sit ametpurus faucibus vehicula. Praesentsed sem non dui pharetra interdum.Nam viverra ultrices magna.Aenean laoreet aliquam orci.Nunc interdum elementum urna.Quisque erat. Nullam temporneque. Maecenas velit nibh,scelerisque a, consequat ut, viverrain, enim. Duis magna. Donecodio neque, tristique et, tincidunteu, rhoncus ac, nunc. Maurismalesuada malesuada elit. Etiamlacus mauris, pretium vel, blanditin, ultricies id, libero. Phasellusbibendum erat ut diam. In congueimperdiet lectus.Aenean scelerisque. Fusce pretiumporttitor lorem. In hac habitasseplatea dictumst. Nulla sit amet nislat sapien egestas pretium. Nuncnon tellus. Vivamus aliquet. Namadipiscing euismod dolor. Ali-quam erat volutpat. Nulla ut ip-sum. Quisque tincidunt auctor au-gue. Nunc imperdiet ipsum egetelit. Aliquam quam leo, con-sectetuer non, ornare sit amet, tris-tique quis, felis. Vestibulum anteipsum primis in faucibus orci luc-tus et ultrices posuere cubilia Cu-rae; Pellentesque interdum quam sitamet mi. Pellentesque mauris dui,dictum a, adipiscing ac, fermentumsit amet, lorem.Ut quis wisi. Praesent quis massa.Vivamus egestas risus eget lacus.Nunc tincidunt, risus quis biben-dum facilisis, lorem purus rutrumneque, nec porta tortor urna quisorci. Aenean aliquet, libero sempervolutpat luctus, pede erat laciniaaugue, quis rutrum sem ipsum sitamet pede. Vestibulum aliquet,nibh sed iaculis sagittis, odio do-lor blandit augue, eget mollis urnatellus id tellus. Aenean aliquet ali-quam nunc. Nulla ultricies justoeget orci. Phasellus tristique fer-mentum leo. Sed massa metus,sagittis ut, semper ut, pharetra vel,erat. Aliquam quam turpis, eges-tas vel, elementum in, egestas sitamet, lorem. Duis convallis, wisi sitamet mollis molestie, libero maurisporta dui, vitae aliquam arcu turpisac sem. Aliquam aliquet dapibusmetus.Vivamus commodo eros eleifenddui. Vestibulum in leo eu erat tris-tique mattis. Cras at elit. Cras pel-lentesque. Nullam id lacus sit ametlibero aliquet hendrerit. Proin plac-erat, mi non elementum laoreet,eros elit tincidunt magna, a rhoncussem arcu id odio. Nulla eget leo aleo egestas facilisis. Curabitur quisvelit. Phasellus aliquam, tortor necornare rhoncus, purus urna posuerevelit, et commodo risus tellus quistellus. Vivamus leo turpis, tem-pus sit amet, tristique vitae, laoreetquis, odio. Proin scelerisque biben-dum ipsum. Etiam nisl. Praesentvel dolor. Pellentesque vel magna.Curabitur urna. Vivamus congueurna in velit. Etiam ullamcorper el-ementum dui. Praesent non urna.Sed placerat quam non mi. Pellen-tesque diam magna, ultricies eget,ultrices placerat, adipiscing rutrum,sem.

Abstract

Artificial intelligence (AI) has been used in various areas to supportsystem optimization and find solutions where the complexity makes itchallenging to use algorithmic and heuristics. Case-based Reasoning(CBR) is an AI technique intensively exploited in domains like man-agement, medicine, design, construction, retail and smart grid. CBRis a technique for problem-solving and captures new knowledge by us-ing past experiences. One of the main CBR deployment challenges isthe target system modeling process. This paper presents a straight-forward methodological approach to model CBR-based applications us-ing the concepts of abstract and concrete models. Splitting themodeling process with two models facilitates the allocation of exper-tise between the application domain and the CBR technology. Themethodological approach intends to facilitate the CBR modeling pro-cess and to foster CBR use in various areas outside computer science.

Keywords

Artificial Intelligence; Case-based Reasoning; CBR Modeling; Bandwidth Allo-cation Model.

1. Introduction

Artificial Intelligence (AI) and Machine Learning (ML) techniques are be-ing extensively used in an ever-increasing number of areas and systems.They provide benefits on adopting them, such as efficient optimizationmethods and the possibility to solve rather complex multi-objective andmulti-constrained problems that were difficult or eventually impossible to

DOI: 10.4236/jcc.2020.89001 Sep. 04, 2020

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http://www.scirp.org/journal/jcchttps://dx.doi.org/10.4236/jcc.2020.89001 .2020.89001https://dx.doi.org/10.4236/jcc.2020.89001 .2020.89001http://creativecommons.org/licenses/by/4.0/http://creativecommons.org/licenses/by/4.0/10.4236/jcc.2020.89001.2020.89001

E. M. Oliveira et al

solve with current algorithmic or heuristics solutions [1].

ML-assisted applications are a trend, and many researchers and devel-opers are rushing to apply ML and recover their inherent potential benefits[2] [3].

However, using ML techniques to solve any problem do require someprevious background and expertise. For example, it is vital to choose theML technique that better suits the target application in terms of availablecomputational capability and expected target results. In sequence to anadequate ML technique choice, it is typically necessary to model the prob-lem under the premises of the chosen technique. The modeling processmay include, as an example, an MDP-based markovian process (MarkovDecision Process) like Q-Learning or SARSA formulation for Reinforce-ment Learning or the definition of a neural network structure for NeuralNetworks (NN) [4] [5].

Case-based Reasoning (CBR) [6] is a technique for problem-solving andfor capturing new knowledge (learning) based on the stored knowledge ofpast experiences. CBR paradigm has a base of past experiences, called acase-base, and attempts to solve new problems by recovering similar solu-tions in this database and adapting them to new problems. CBR, to someextent, mimics the human behavior in activities like management and di-agnostics in which the previous knowledge and experience is the driver inlooking for the solutions for new near-equivalent problems [7] [8].

CBR was proposed more than a decade ago as an AI technique [6], issimple to use, has minimal learning requirements, and does not typicallyrequire intensive computational resources [9]. More recently, CBR receivesthe attention from the artificial intelligence community and is gaining trackin domain areas like medicine, expert systems, retail, smart grid, construc-tion, manufacturing, design, agriculture and management [10] [11] [12] [13][14] [15].

Like other AI techniques, CBR requires the target system to be mod-eled to allow a similarity search in its database. This process is not clearlydetailed or methodologically described in the literature. Our approach pro-vides a way to methodologically model the target system. In our approach,the CBR modeling for problem-solving requires a specialized abstract modelof the target system and, derived from it, a concrete CBR representationof the variables and parameters involved in the process.

The objective of this work is, in summary, to propose a methodologi-cal approach to model the CBR process based on a mapping between theabstract and concrete representations of the CBR process variables andparameters. The proposed method aims to facilitate the CBR modelingprocess and contribute to promoting the widespread use of CBR. We alsoexpect the contribution can be relevant to CBR application areas wherethe computer science expertise of the professionals involved is less frequentor even unavailable.

The remaining of this paper is organized as follows. Section 2 presentsan overview of the CBR fundamentals and section 3 discusses the relatedworks. Sections 4 and 5 present the CBR modeling methodology and sec-tion 7 follows with an example of how to use the approach for the cognitivemanagement of bandwidth in network links. In section 8, the final consid-erations are presented.

DOI: 10.4236/jcc.2020.89001 Sep. 4, 2020 2 Journal of Computer and Communications


2. Case-based Reasoning Fundamental Aspects

In summary, in CBR, a new problem is solved through the knowledge andinformation available or acquired by a previous similar problem adapted tocreate a new solution.

The essential components used to solve a problem with CBR are acase, the case-database of past cases or past experiences, and the similarityfunction.

The case is the way we represent the experience we have about thetarget problem. All previous cases representing the acquired knowledgeare stored in a database, called case-database. A case is represented by apair problem and solution that are the fundamental aspects present in allCBR systems:

• Problem: It contains all the information regarding the past event thatyou want to remember. That is, it describes all the essential datafor the representation of knowledge in the specific domain. Thesedata can include contextualization data, application objectives, de-scriptions of what happened, qualitative data, and quantitative data,among others.

• Solution: It presents the necessary information to solve the problemrelated to the past event. This solution can be any information oraction that totally or partially solves the problem presented in thecase description. The representation of the solution must always takeinto account the application domain.

The execution of the CBR process includes 4 phases using its essentialcomponents, namely (Figure 1) [16] [17]:

• Recover : Performs the search for similar cases in the case-database.This comparison is made using the similarity function. The similarityfunction is responsible for comparing the base cases with the actualproblem and should return the most similar cases found.

• Reuse: The phase in which the description of the current problemis composed with the solution of the case recovered in the previousphase. Then the solution found is applied to the environment inquestion.

• Revision: In the review phase, the specialist in the technological do-main must assess whether the solution employed brings the expectedresults. The specialist has the opportunity to make fine adjustmentsto optimize, adjust, or adapt the recommended solution.

• Retention: After making the necessary adaptations, the specialistmust confirm the new case as a valid case and consequently save it inthe case base for later reuse.

From the methodological point of view, the utilization CBR to solve aparticular problem involves the following modeling and operational steps:

• Case and Knowledge Representation: The case components and itsstructure embed the experience and the knowledge for a particularproblem. As such, case descriptions document real experiences, andthe CBR system may acquire new knowledge by retaining new cases.

Journal of Computer and Communications 3 DOI: 10.4236/jcc.2020.89001 Sep. 4, 2020


Figure 1: CBR 4R Cycle [16]

• Similarity Measure: A similarity function calculates the similaritymeasure, and it defines to what extent a case in the case-database issimilar to the case being processed.

• Adaptation: In CBR, the operational adaptation step takes a similarcase and adapts it to the current situation. In general, adaptationuses a defined mechanism, expert knowledge, or a mix of both.

• Learning : It is an operational step that allows the CBR system tomemorize its successful and unsuccessful solutions, which means ef-fectively to acquire new knowledge.

The foundation of the CBR that supports its capability to adapt, learn,and retain knowledge are the case representation and the choice of similaritymeasures. Accurately modeling these two elements of the CBR operationis essential.

In this paper, we present a methodological approach that addresses therepresentation of cases and the choice of similarity measures for the problemof resource management with CBR.

The proposed methodological approach is composed of an AbstractModel (AM) and a Concrete Model (CM). The abstract model is a high-level representation that structures the knowledge corresponding to thescope of actuation. The concrete model maps the abstract model’s repre-sentation to the set of parameters used in the CBR execution process toacquire knowledge for a set of particular cases.

3. Related Work

Watson presents a discussion about the methodological approach used byCBR in [18]. Watson argues that CBR can be better described as a method-ology for problem-solving and, as such, differs from other artificial intelli-gence techniques like neural networks, and genetic algorithms that use moreformal mathematical methods. Watson’s paper does not discuss or proposea methodological approach to support CBR deployments.



The case representation formalism is discussed in Martines [19]. The au-thors discuss how experiences can be represented by using since simple fea-ture vectors to representational formalisms like object-oriented, predicate-based and semantic nets, among others.

Althof discusses in [20] the need to develop CBR applications as a sys-tematic engineering activity. The paper addresses the software developmentlife-cycle targeting the development of software products that support CBRdevelopment.

A discussion about CBR modeling is presented in Krite [21]. The paperdescribes how CBR can be used to compare, reuse, and adapt inductivemodels that represent complex systems. The paper does not address specificcases and knowledge representations.

4. Abstract Model

The proposed approach to model a CBR-based system for problem-solvinghas two steps:

• Abstract Model (AM) definition; and

• Concrete Model (CM) mapping.

The definition of the AM requires expertise in the application domaininvolved. The mapping to the CM from the AM requires a minimum CBRexpertise.

The abstract model is a high-level representation to structure the knowl-edge and, as such, to define the scope of action of the problem-solving sys-tem. It requires specialist knowledge of the application domain to whicha system using CBR is applied. This model aims to represent knowledgeabout the scope of actions for the CBR system.

The abstract model is composed by a Technological Domain (TD)with general and specific objectives, attributes, measurements, actions, andpremises.

4.1. Technological Domain (TD)

The technological domain defines, in general, the scope of the targetproblem-solving or, in other words, what the problem is and how to repre-sent it [16] [17].

The representation of the TD in the proposed model is as follows:

• System and problem description and objectives;

• Static, contextual and dynamic attributes of the system;

• Measurements; and

• Actions.

This set of components adequately describes the target system for gen-eral resource management problems like virtual machine management, dat-acenter management, and network management to cite some applicationexamples.



4.2. TD Component - System and Problem Definition, Gen-eral and Specific Objectives

The system and problem description is a formal or textual description ofCBR problem and system.

The general objective is a high-level definition and delimitation of thetarget management problem. It is associated to the CBR resource manage-ment task.

The specific objectives detail the general objective of the CBR manage-ment system’s multiple possible outcomes. In effect, this corresponds to thedefinition of a multi-objective problem, possibly under a multi-requirementscenario.

The specific objectives specify a subset of the context, measurement, andaction attributes used in the CBR decision-making and learning processes.

4.3. TD Component - Attributes

The static attributes describe static characteristics of the target system andhave, in most cases, documentary value. The CBR system operation doesnot index the static attributes, so they do not interfere in the problem-solving process.

The contextual attributes indicate the context of the problem and cor-respond to definitions and parameters that do not change frequently. Fromthe CBR system perspective, contextual attribute modification may implyin restarting the learning process with CBR.

The dynamic attributes are the set of variables and parameters thatindicate the target system’s global state. In the problem-solving context,dynamic attributes impact the target management process and are mea-surable.

4.4. TD Component - Measurements, Tolerance and Actions

The measurements are, in general, the set of variables actual values acquiredby a monitoring system that are relevant to the management process. Themeasurements, together with the context attributes, provide a snapshot ofthe system current state.

The measurements are instances of the system variables and aims tohave a snapshot of the systems and quantify the specific objectives. Ineffect, the management of accepted values for measurable variables quantify,in general, the specific objectives.

Tolerance represents the accepted value for the scope of this method-ology. So, in most cases, the specific objectives are represented by ranges,upper or lower limits for defined managed variables.

The actions are the operation set that is used to react upon the identi-fication of a problem. The set of actions defined is executed on the systemand are related to the defined specific objectives.

4.5. Premisses

In the CBR abstract model, the premises are the set of problems to whicha solution is known. Premises are optional and aim to facilitate knowledgeacquisition by the CBR system. It is essential to highlight that the CBRsystem is capable of learning from scratch without any given premise.



The premises can be confirmed or not, through the acquisition of newknowledge. Wrong premises will negatively impact the learning processsince the premise will be discarded to allow the acquisition of new knowl-edge.

5. Concrete Model

The concrete model corresponds to the mapping of the abstract modeldefinitions in CBR cases, similarity function, parameters, variables, andweighting priorities of the deployed CBR system.

The mapping from the abstract model to the concrete model is achievedin the following way:

• TD components like attributes, measurements, and tolerances aremapped in the CBR cases description.

• General and specific objectives are mapped in similarity function andevaluation function.

• Actions are mapped in solutions for the CBR cases.

• Premises are mapped in the first cases for the CBR system.

The proposed sequence of steps to map from AM to CM model is asfollows:

1. Mapping of attributes and measurements necessary to achieve thespecific objectives;

2. CBR case description definition using the mapped set of attributesand measurements;

3. Tolerance level definition for the CBR case components;

4. Mapping of the actions for the solution of CBR cases;

5. Similarity function definition;

6. Evaluation function definition; and

7. CBR 4R cycle operation process.

The last step, the CBR 4R cycle operation process, is described in thispaper as a non-exhaustive sequence of how to do it procedures and hintsthat can be used in the CBR 4R cycle.

5.1. The CBR Case

In CBR terminology a case is a problem situation composed by a set ofparameters describing the problem domain and the associated solution forthe problem (Figure 2) [22].

A generic CBR case is:

Cj = (pj , aj) (1)



Figure 2: A CBR case

where,

pj = {SAj , CAj ,M j , T j} (2)

aj = {aj1, aj2, ..., ajx} (3)

SAj = {SAj1, SAj2, ..., SAjn} (4)

CAj = {CAj1, CAj2, ..., CAjk} (5)

Mj = {M j1,M j2, ...,M jz} (6)

Tj = {T j1, T j2, ..., T jy} (7)

The case Cj is composed by the set of parameters pj and the associ-ated set of actions aj . The set of parameters pj includes all the relevantstatic attributes (SAj), context attributes (CAj), measurements (Mj) andtolerances (Tj) for the problem situation being described.

The case solution is composed by the set of relevant actions aj to achievethe defined objectives. These actions allow operations or sets of operationsthat are used to react to a particular problem in the system.

5.2. Similarity Function (SF)

Similarity is a crucial aspect of CBR. A similarity function is used to re-trieve similar cases from the case-database when a new case or unsolved



case arrives at the system. In summary, a new case or unsolved case is thedescription of a new problem to be solved.

Examples of similarity functions include the identification of similaritybased on rules, correlation testing, K-nearest-neighbor (KNN) techniques,and the cosine similarity measure, among others [19] [11] [18] [16] [23].

The similarity function, regardless of the option, requires the definitionof the evaluation indexes and their weights for the given real-world problem.The definition of the similarity measures is an actual challenging researchproblem and has a significant dependency on the target problem [24].

From the methodological point of view, the similarity function ismapped from the specific objectives and is composed of a set of attributesand measurements with weights that define their priority in the definitionof the similarity.

The similarity of one case to others can, from the methodological pointof view, be defined by averaging the distinct similarities of part of the case,for example, a case with three indexed attributes (xi, xj , xl) will be similarto another case (yi, yj , yl) if the attributes i, j and l of the cases x andy are similar to each other. This partial similarity, by attribute, is calledlocal similarity.

The local similarity is calculated according to the type of attribute thatdefines the case, and a specific function can be used for each type of data.The functions used for calculate local similarity, as an example, are ladderfunction, linear function, equality function, maximum function, intersectionfunction, and contrast function [16].

The global similarity determines how similar one case is to the otherusing the values of local similarities to which weights can also be assigned.

Attributes and measurements have direct and indirect relation with thespecific objectives. Consequently, two issues arrive in terms of methodolog-ically mapping of the similarity function parameters: i) to consider indirectsimilarities; and ii) to consider similar cases in distinct contexts. Indirectrelations must be indexed, and a similar case in distinct contexts must bedifferentiated by choosing attribute weights adequately.

5.3. Evaluation Function (EV)

The evaluation function is an optional facility that may be included in theCBR operation process. It is not conventionally inserted in the CBR cyclebut might be helpful in the CBR operation cycle. The EV is mapped fromthe objectives, attributes, and tolerances of the concrete model.

The fundamental idea of using an evaluation function is to interpret thestate of the measurements, comparing them with the tolerances defined forthe CBR system. It provides a kind of on-the-fly evaluation of the CBRsystem behavior.

This function can be used for two purposes: i) to generate periodicwarnings and diagnoses of symptoms and alerts detected in the CBR sys-tem; and ii) to check if a solution applied to the CBR system meets theobjectives (general and specific) and tolerances defined.



6. CBR Operation Process - Hints and How to DoIt

After the mapping from the abstract model to the concrete model with thedefinition of objectives, attributes, measurements, actions, and cases, theCBR 4R cycle starts (Recover, Reuse, Revision and Retain).

For each of these steps, some procedures shall be executed by the CBRsystem. These procedures depend on the actual problem-solving issue fo-cused on the CBR system, but there are commonalities. We explore someof these commonalities to provide a set of hints and possible how to doactions for the operation of CBR systems.

6.1. Recover

The Cycle 4R has as its starting point the recovery phase that retrieves asimilar case and evaluates it. This task can be triggered in two differentways: reactively or proactively.

In reactive mode, an alarm requests analysis triggered by a current prob-lem, to obtain a solution or optimize the system. In proactive mode, theCBR system is activated to check the system’s situation and occasionallypropose improvements proactively or a solution.

When no case returns from the case-database, two procedures are sug-gested:

• Use the method assisted by the manager, where he provides a newsolution.

• Use an automated method where the solution is automaticallymapped considering the defined objectives or an arbitrary solutionis attributed to the current problem. The arbitrary solution attribu-tion corresponds to a brute force learning method.

6.2. Revision

In the review phase, CBR assesses the efficiency of the proposed solution.For the review, it is necessary to wait a specific time until the actions takeeffect, and the attributes and measurements of the new state of the systemare updated.

In this step, the evaluation function is used to check if the new solutionpresents improved performance. Without improvements, the new case isconsidered unsuccessful. The new case, positive or negative, is, by conven-tion, stored in the CBR database.

After applying the solution, the current and previous attributes andmeasurements, cannot differ much from the previous state. With a con-siderable variation, it is not possible to identify whether the system hasimproved due to the solution adopted or simply because the state of the re-sources has changed. As adopted in other AI techniques, to use a discountis recommended in these cases.

Another recommendation is the creation of a configurable equivalencethreshold for the cases. The aim is to avoid many very similar cases pop-ulating the case-database and contribute to its excessive growth, whichresults in a performance problem.



False-positive cases can be negated in the next reuse. For false-negativecases, we suggest a period of validity, both for positive and negative cases,simulating the human forgetfulness of old facts that are rarely used.

7. Using the Abstract and Concrete Models for anCBR-based Application - Cognitive Managementof Bandwidth in Network Links

We exemplify now how abstract and concrete models can facilitate thedefinition of the objectives, attributes, measurements, and tolerances tomodel a CBR-based application.

The target CBR-based application (BAMCBR) aims to manage band-width in links of an MPLS (MultiProtocol Label Switching Network). Thelink management is executed by a Bandwidth Allocation Model (BAM)that dynamically receives requests for link setup and grants or denies theserequests based on the link bandwidth availability in the network [25] [26].The cognitive management consists of CBR deciding when should the BAMmodel be changed among a set of options based on network parameters sta-tus. Oliveira in [27] has a detailed description of this cognitive managementapplication, and, in this paper, we focus on illustrating how AM and CMcan be used to model the CBR application.

It follows the Abstract Model definition and Concrete Model mappings.

7.1. BAMCBR Abstract Model

The first step in building the abstract (AM) model is the representation ofthe technological domain with the definition of its objectives, attributes,measurements, and tolerances.

7.1.1 BAMCBR Tecnological Domain (TD):

The CBR system’s technological domain is the cognitive management of anMPLS/DS-TE type computer network with bandwidth allocation models(BAMCBR Tool) [27].

7.1.2 BAMCBR General and Specific Objectives:

The general objective of the BAMCBR is to decide when should the BAMmodel be changed among a set of BAM model options available based onnetwork link-state performance parameters and input traffic.

The specific objectives drive the BAM model reconfiguration decisionprocess and are the following:

1. To maximize link throughput

2. To minimize link preemption; and

3. To minimize link devolution.

It is essential to highlight that only with the expertise about theapplication domain (BAM model operation [26]) it is possible to knowthat throughput maximization, preemption, and devolution minimizationare management objectives achievable by the reconfiguration of the BAMmodel [28].



7.1.3 BAMCBR Static Atributes:

The static attributes are the BAM model static configuration parameters.In the BAMCBR they are the total managed link bandwidth and the con-figured bandwidth allocate per class of traffic in the link (BC - BandwidthConstraint [26]).

7.1.4 BAMCBR Contextual Atributes:

The contextual attributes define the management context, and the modifi-cation of these attributes, although less frequent, does reflect the manager’sperception of what he wants from the CBR system. In BAMCBR, theadopted BAM and the tolerances for throughput, preemption, and devolu-tion are the main contextual parameters. Any change on these attributesimplies in restarting the CBR learning process.

7.1.5 BAMCBR Measurement Attributes:

The measurement attributes are the link variables that indicate the link’sperformance and state in a given moment. These are measurable dynamicvariables belonging to the target CBR system (network link). Examples ofmeasurement attributes used by the BAMCBR tool are link preemption,link devolution, packet loss, LSP (Label Switched Path) request blocking,and link utilization [27].

7.1.6 BAMCBR Tolerances:

The tolerances represent the accepted values range for attributes in general.Their definition requires expertise in the domain area and they refines thespecific objectives. In the BAMCBR, as an example, the managementaccepts a link utilization of 10% with a 10% tolerance.

7.1.7 BAMCBR Actions:

The actions correspond to the set of operations used to solve a problem fora certain case. In BAMCBR, the action is to reconfigure the current BAMmodel among the available options: MAM (Maximum Allocation Model),RDM (Russian Dolls Model) and ATCS (AllocTC-Sharing) models [28].

7.1.8 BAMCBR Premises:

The premises are the set of problems or states to which a solution is known.For example, when the current BAM is MAM or RDM, and the utilizationis less or equal to 50 %, the BAM might be reconfigured to ATCS [28].

7.2. BAMCBR Concrete Model

The concrete model (CM) maps the abstract model in the parameters andvariables used by the CBR system.

7.2.1 BAMCBR Case Description:

The case description includes the attributes, measurements and tolerancespreviously defined in the abstract model. In BAMCBR, it includes BAM



models, measurement variables like preemption, utilization, loss, blockingand devolution, and the action options for BAM model change (Figure 3).

Figure 3: Caso BAMCBR

7.2.2 BAMCBR Case Solution:

The actions set for BAMCBR is to dynamically switch between ATCS,RDM and MAM models [28].

7.2.3 BAMCBR Evaluation Function:

The evaluation function verifies the actual state of the managed systemby comparing the actual case with previous cases using measurements andtolerances defined. For example, it must also be able to verify if a solutionapplied to the network is closer to the objective than the solution previouslyadopted. BAMCBR uses the WkNN function to evaluate the current stateof the network in relation to other previous states.

Weights in the evaluation function reflect management expertise. Forexample, BAMCBR considers that devolution generates a negative impactmore significant than preemption, which, in turn, generates a more signifi-cant impact than blocking. As such, the weights for devolution, preemption,and blocking are 3, 2, and 1, respectively.

7.2.4 BAMCBR Similarity Function:

The functions for local and global similarity defined for the BAMCBR toolare indicated in Table 1.

The cut-off threshold is used for recovering similar solutions within sim-ilarity equal or superior to the indicated limit. The equivalence thresh-old limit is used to avoid storing multiple nearly identical solutions at the



Table 1: BAMCBR Similarity Functions

Attribute/Measurement Similarity Function Weight

BAM Model Equal Function 40Throughput Linear Function 30

Blocking Linear Function 30Devolution Linear Function 20Preemption Linear Function 20

Global Similarity Function: WkNNCut-off Threshold 96%

Equivalence Threshold 98.5%

database and, thus, populated it excessively. In other words, solutions withsimilarity equal or superior to the equivalence threshold limit are not storedin the CBR database.

8. Final Considerations

The methodological approach to model CBR-based applications uses thedefinition of an abstract model (AM) that is subsequently mapped in aconcrete model (CM).

The abstract model represents the domain to which CBR is applied andits definition needs essentially the knowledge of an expert in the applicationdomain. The concrete model corresponds to the CBR parameters whosemapping from the abstract model requires CBR expertise. The splittingin two models facilitates the model process and, in addition, allows theallocation of domain and CBR specialists to the distinct phases of themodeling process.

As such, this proposal’s inherent advantage is that it allows a taskdivision between specialists in the domain and specialist in CBR. This fa-cilitates the modeling process and has the potential to foster the utilizationof CBR in an even large number of areas where computer science expertiseis less frequent.

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1 Introduction2 Case-based Reasoning Fundamental Aspects3 Related Work4 Abstract Model4.1 Technological Domain (TD)4.2 TD Component - System and Problem Definition, General and Specific Objectives4.3 TD Component - Attributes4.4 TD Component - Measurements, Tolerance and Actions4.5 Premisses

5 Concrete Model5.1 The CBR Case5.2 Similarity Function (SF)5.3 Evaluation Function (EV)

6 CBR Operation Process - Hints and How to Do It6.1 Recover6.2 Revision

7 Using the Abstract and Concrete Models for an CBR-based Application - Cognitive Management of Bandwidth in Network Links7.1 BAMCBR Abstract Model7.1.1 BAMCBR Tecnological Domain (TD):7.1.2 BAMCBR General and Specific Objectives:7.1.3 BAMCBR Static Atributes:7.1.4 BAMCBR Contextual Atributes:7.1.5 BAMCBR Measurement Attributes:7.1.6 BAMCBR Tolerances:7.1.7 BAMCBR Actions:7.1.8 BAMCBR Premises:

7.2 BAMCBR Concrete Model7.2.1 BAMCBR Case Description:7.2.2 BAMCBR Case Solution:7.2.3 BAMCBR Evaluation Function:7.2.4 BAMCBR Similarity Function:

8 Final Considerations

a methodological approach to model cbr-based systems · 2020. 9. 10. · adipiscing elit. etiam...

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