Improved Effective Management of the Uncertainty in Army Decision Making using Cognitive Agents, Classification based on Fuzzy Association Rules and Genetic Rule Selection
Decision making (DM) is an important problem in most of the armyoperations. One of the challenging issues in this area is uncertainty in warswith uncertain information which causes many destructive effects on theresults of strategies in battlefields. In the Heravi et al. article’s, published inthe y...
| Main Authors: | , , |
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| Format: | Article |
| Language: | fas |
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Allameh Tabataba'i University Press
2016-09-01
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| Series: | Muṭāli̒āt-i Mudīriyyat-i Ṣan̒atī |
| Subjects: | |
| Online Access: | https://jims.atu.ac.ir/article_5720_f52ea15bf363d1cc5ba75144a7dbb4d7.pdf |
| _version_ | 1797367683556048896 |
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| author | Mojtaba Heravi Tabassom Azimi galeh Hessam Zandhessami |
| author_facet | Mojtaba Heravi Tabassom Azimi galeh Hessam Zandhessami |
| author_sort | Mojtaba Heravi |
| collection | DOAJ |
| description | Decision making (DM) is an important problem in most of the armyoperations. One of the challenging issues in this area is uncertainty in warswith uncertain information which causes many destructive effects on theresults of strategies in battlefields. In the Heravi et al. article’s, published inthe year 2013, utilizing a combination of Cognitive Agent (CA) andClassification based on Fuzzy Association Rules (CFAR) as the mosteffective and widely used methods, was able to relatively reduce thisproblem and tried to reduce uncertainty. But still in critical condition, can’tdeny the need to act quickly and remove most invalid and inefficient rulesextracted in the effective decisions.This paper aims to utilize the capabilities of Genetic Algorithm (GA) in amore realistic selection rules as a meta-heuristic way to combinecomplementary methods to minimize the uncertainty in DM. In comparisonwith previous method, experimental results achieved, clearly show that thiscombination in addition to the advantages of the previous method, due to thefurther reduction of production rules for DM, are more understandable andaccurate and has more rational risk acceptance. |
| first_indexed | 2024-03-08T17:21:43Z |
| format | Article |
| id | doaj.art-581a3a176eca480c8d0e32253d0f4596 |
| institution | Directory Open Access Journal |
| issn | 2251-8029 2476-602X |
| language | fas |
| last_indexed | 2024-03-08T17:21:43Z |
| publishDate | 2016-09-01 |
| publisher | Allameh Tabataba'i University Press |
| record_format | Article |
| series | Muṭāli̒āt-i Mudīriyyat-i Ṣan̒atī |
| spelling | doaj.art-581a3a176eca480c8d0e32253d0f45962024-01-03T04:44:35ZfasAllameh Tabataba'i University PressMuṭāli̒āt-i Mudīriyyat-i Ṣan̒atī2251-80292476-602X2016-09-01144219923710.22054/jims.2016.57205720Improved Effective Management of the Uncertainty in Army Decision Making using Cognitive Agents, Classification based on Fuzzy Association Rules and Genetic Rule SelectionMojtaba Heravi0Tabassom Azimi galeh1Hessam Zandhessami2کارشناسی ارشد مهندسی دانش و علوم تصمیم دانشگاه ازاد اسلامی واحد قزوینکارشناس ارشد مدیریت بازرگانی - بازار یابی شرکت توزیع نیروی برق اهوازاستادیار گروه مدیریت صنعتی دانشگاه آزاد اسلامی واحد قزوینDecision making (DM) is an important problem in most of the armyoperations. One of the challenging issues in this area is uncertainty in warswith uncertain information which causes many destructive effects on theresults of strategies in battlefields. In the Heravi et al. article’s, published inthe year 2013, utilizing a combination of Cognitive Agent (CA) andClassification based on Fuzzy Association Rules (CFAR) as the mosteffective and widely used methods, was able to relatively reduce thisproblem and tried to reduce uncertainty. But still in critical condition, can’tdeny the need to act quickly and remove most invalid and inefficient rulesextracted in the effective decisions.This paper aims to utilize the capabilities of Genetic Algorithm (GA) in amore realistic selection rules as a meta-heuristic way to combinecomplementary methods to minimize the uncertainty in DM. In comparisonwith previous method, experimental results achieved, clearly show that thiscombination in addition to the advantages of the previous method, due to thefurther reduction of production rules for DM, are more understandable andaccurate and has more rational risk acceptance.https://jims.atu.ac.ir/article_5720_f52ea15bf363d1cc5ba75144a7dbb4d7.pdfdecision makinguncertainty managementasymmetric warfarecognitive agentclassification based on fuzzy association rulesgenetic rule selection |
| spellingShingle | Mojtaba Heravi Tabassom Azimi galeh Hessam Zandhessami Improved Effective Management of the Uncertainty in Army Decision Making using Cognitive Agents, Classification based on Fuzzy Association Rules and Genetic Rule Selection Muṭāli̒āt-i Mudīriyyat-i Ṣan̒atī decision making uncertainty management asymmetric warfare cognitive agent classification based on fuzzy association rules genetic rule selection |
| title | Improved Effective Management of the Uncertainty in
Army Decision Making using Cognitive Agents,
Classification based on Fuzzy Association Rules and
Genetic Rule Selection |
| title_full | Improved Effective Management of the Uncertainty in
Army Decision Making using Cognitive Agents,
Classification based on Fuzzy Association Rules and
Genetic Rule Selection |
| title_fullStr | Improved Effective Management of the Uncertainty in
Army Decision Making using Cognitive Agents,
Classification based on Fuzzy Association Rules and
Genetic Rule Selection |
| title_full_unstemmed | Improved Effective Management of the Uncertainty in
Army Decision Making using Cognitive Agents,
Classification based on Fuzzy Association Rules and
Genetic Rule Selection |
| title_short | Improved Effective Management of the Uncertainty in
Army Decision Making using Cognitive Agents,
Classification based on Fuzzy Association Rules and
Genetic Rule Selection |
| title_sort | improved effective management of the uncertainty in army decision making using cognitive agents classification based on fuzzy association rules and genetic rule selection |
| topic | decision making uncertainty management asymmetric warfare cognitive agent classification based on fuzzy association rules genetic rule selection |
| url | https://jims.atu.ac.ir/article_5720_f52ea15bf363d1cc5ba75144a7dbb4d7.pdf |
| work_keys_str_mv | AT mojtabaheravi improvedeffectivemanagementoftheuncertaintyinarmydecisionmakingusingcognitiveagentsclassificationbasedonfuzzyassociationrulesandgeneticruleselection AT tabassomazimigaleh improvedeffectivemanagementoftheuncertaintyinarmydecisionmakingusingcognitiveagentsclassificationbasedonfuzzyassociationrulesandgeneticruleselection AT hessamzandhessami improvedeffectivemanagementoftheuncertaintyinarmydecisionmakingusingcognitiveagentsclassificationbasedonfuzzyassociationrulesandgeneticruleselection |