بکارگیری حافظه ای محدود برای نگهداری برترین کنش اخیردر سیستم های طبقه بندی کننده یادگیر XCS در مسائل هزارتو
الموضوعات :
علی یوسفی
1
(دانشجوی دکتری هوش مصنوعی و رباتیکز گروه آموزشی کامپیوتر، واحد علوم و تحقیقات ، دانشگاه آزاد اسلامی، تهران،ایران)
کامبیز بدیع
2
(عضو هیات علمی پژوهشگاه ارتباطات و فناوری اطلاعات)
محمد مهدی عبادزاده
3
(استاد دانشکده مهندسی کامپیوتر و فناوری اطلاعات دانشگاه صنعتی امیر کبیر ، تهران ایران)
آرش شریفی
4
(گروه مهندسی کامپیوتر، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران)
الکلمات المفتاحية: سیستم هاي طبقه بند یادگیر, الگوریتم XCS , حافظه ي محدود, مسائل هزارتو,
ملخص المقالة :
امروزه، سیستمهاي طبقهبندي کننده یادگیر درکاربردهاي متنوع در رباتیک مانند رباتهاي حسي، رباتهاي انساننما، سامانه هاي امداد و جات هوشمند وکنترل ربانهاي فیزیکي در محیطهاي گسسته و پیوسته، مورد توجه قرار گرفته است. معمولا از ترکیب یک الگوریتم تکاملي یا روشهاي شهودي با یک فرایند یادگیري براي جستجو در فضاي قوانین موجود در انتساب کنش مناسب یک دستهبند استفاده مي شود. چالش مهم براي بالا بردن سرعت و دقت در رسیدن به هدف در مسائل هزار تو، بکارگیري و انتخاب کنشي است که محرک بجاي برخورد تکراري به موانع اطراف، در مسیر درست قرار گیرد. بدین منظور در این مقاله یک الگوریتم طبقه بندي کننده یادگیر هوشمند سیستمهاي طبقه بند یادگیر مبتني بر دقت) XCS ( مبتني بر حافظه محدود بکار گرفته شده است که با توجه به ورودي و کنشهاي اعمال شده به محیط و عکس العمل محرک، قوانین بهینه شناسایي شده و در اولویت انتخاب با احتمال بیشتري در مراحل بعدي، به عنوان مجموعه دستهبند جدید به الگوریتم سیستمهاي طبقه بند یادگیر مبتني بر دقت (XCS) اضافه گردد. از جمله دستاوردهاي این روش مي توان به کاهش تعداد مراحل لازم و افزایش سرعت در رسیدن محرک به هدف در مقایسه با الگوریتم سیستمهاي طبقه بند یادگیر مبتني بر دقت (XCS) پایه داشت .
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