مدل‌سازی و تحلیل بازی معمای زندانی تکراری به کمک شبکه عصبی مصنوعی پادانتشار گراسبرگ
محورهای موضوعی : مهندسی برق و کامپیوتر
غلامعلی منتظر
1
(دانشگاه تربیت مدرس)
نجمه رستگار رامشه
2
(دانشگاه تربيت مدرس)
عليرضا عسكرزاده
3
(دانشگاه تحصیلات تکمیلی صنعتی و فنّاوری پیشرفته کرمان)
کلید واژه: بازی معمای زندانی راهبرد TFT شبکه پادانتشار گراسبرگ شبکه عصبی مصنوعی,
چکیده مقاله :
در اکثر اوقات، تصمیم‌گیری مؤثر در موقعیت‌های راهبردی همچون مسایل رقابتی به نگاشت غیر خطی بین محرک و پاسخ نیاز دارد. شبکه‌های عصبی مصنوعی می‌توانند در مدل‌سازی و حل این مسایل رهیافت مناسبی باشند. بازی معمای زندانی از معروف‌ترین بازی‌های مطرح‌شده در نظریه بازی‌ها است كه به كمك آن بسياري از مسایل رقابتي تحليل مي‌شود و تصميم‌گيري را تسهيل مي‌كند. در این مقاله سعی بر آن است که بازی معمای زندانی تکراری به کمک شبکه عصبی مصنوعی مدل‌سازی و تحلیل شود و به همین دلیل شبکه عصبی پادانتشار گراسبرگ برای انجام این بازی طراحی شده است. نتایج، نشان‌دهنده توانمندی این روش در مدل‌سازی کامل بازی است. نتایج حاصل از به‌کارگیری این ایده با دو روش دیگر (راهبرد TFT و مدل‌سازی با شبکه پرسپترون) نشان از کارایی محرز روش جدید است.
Most of the time effective decisions in strategic situations such as competitive issues require a non-linear mapping between stimulus and response. Artificial neural networks can be an appropriate way for modeling and solving these kinds of problems. Prison Dilemma Game is a well-known game that is proposed in game theory. This paper tries to describe how using neural network, the iterated prisoner’s dilemma game can be modeled and analyzed. To do this a Grossberg Counter-Propagation Neural Network (GCP-NN) has been designed to play this game. Results show the capability of this method in complete modeling game. The results present the efficiency of the new method in comparison with the two conventional methods: Tit For Tat (TFT) strategy and Perceptron modeled game.
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