بهبود مدیریت موثر عدم قطعیت در تصمیم گیری های نظامی با استفاده از عامل های شناختی، دسته بندی براساس قوانین وابستگی فازی و انتخاب ژنتیکی قوانین

نوع مقاله: مقاله پژوهشی

نویسندگان

1 کارشناسی ارشد مهندسی دانش و علوم تصمیم دانشگاه ازاد اسلامی واحد قزوین

2 کارشناس ارشد مدیریت بازرگانی - بازار یابی شرکت توزیع نیروی برق اهواز

3 استادیار گروه مدیریت صنعتی دانشگاه آزاد اسلامی واحد قزوین

چکیده

تصمیم گیری یکی از مهمترین موضوعات مورد بررسی در تحقیقات نظامی بشمار می رود. یکی از چالش های موجود در
این بحث وجود عدم قطعیت در محیط های جنگی می باشد که تاثیرات مخربی بر کیفیت و دقت تصمیم گیری می گذارد.
در مقاله هروی و همکارانش، چاپ شده در سال 2193 ، بکارگیری ترکیبی از دو موضوع عامل های شناختی و دسته بندی
براساس قوانین وابستگی فازی به عنوان زمینه های موثر و پرکاربرد، توانسته بود تا حدودی این مسئله را کمرنگ کرده و
سعی در کاهش عدم قطعیت داشته باشد. ولی هم چنان در شرایط حساس و بحرانی، نیاز به سرعت عمل بیشتر با حذف
قوانین نامعتبر و ناکارای استخراج شده در اتخاذ تصمیمهای موثرتر قابل انکار نیست.
هدف این مقاله، بهره گیری از ظرفیت های الگوریتم ژنتیک در انتخاب قوانین واقعبینانهتر به عنوان یک روش فراابتکاری
در تکمیل روش قبلی بصورت ترکیبی، برای کاهش هرچه بیشتر عدم قطعیت در تصمیم گیری ها می باشد. نتایج تجربی
بدست آمده در مقایسه با روش پیشین، به روشنی نشان می دهد که این ترکیب علاوهبر مزیتهای روش قبل، بدلیل کاهش
هرچه بیشتر قوانین تولید شده برای اتخاذ تصمیم، قابل فهم تر، دقیقتر و ریسک پذیری عاقلانهتری دارد.

کلیدواژه‌ها


عنوان مقاله [English]

Improved Effective Management of the Uncertainty in Army Decision Making using Cognitive Agents, Classification based on Fuzzy Association Rules and Genetic Rule Selection

نویسندگان [English]

  • Mojtaba Heravi 1
  • Tabassom Azimi galeh 2
  • Hessam Zandhessami 3
چکیده [English]

Decision making (DM) is an important problem in most of the army
operations. One of the challenging issues in this area is uncertainty in wars
with uncertain information which causes many destructive effects on the
results of strategies in battlefields. In the Heravi et al. article’s, published in
the year 2013, utilizing a combination of Cognitive Agent (CA) and
Classification based on Fuzzy Association Rules (CFAR) as the most
effective and widely used methods, was able to relatively reduce this
problem and tried to reduce uncertainty. But still in critical condition, can’t
deny the need to act quickly and remove most invalid and inefficient rules
extracted in the effective decisions.
This paper aims to utilize the capabilities of Genetic Algorithm (GA) in a
more realistic selection rules as a meta-heuristic way to combine
complementary methods to minimize the uncertainty in DM. In comparison
with previous method, experimental results achieved, clearly show that this
combination in addition to the advantages of the previous method, due to the
further reduction of production rules for DM, are more understandable and
accurate and has more rational risk acceptance.

کلیدواژه‌ها [English]

  • Decision making
  • Uncertainty Management
  • Asymmetric Warfare
  • Cognitive Agent
  • Classification based on Fuzzy Association Rules
  • Genetic Rule Selection
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