مروری بر سيستم های يادگيری مبتنی بر کنجکاوی در هوش مصنوعی
محورهای موضوعی : مجله فناوری اطلاعات در طراحی مهندسی
سعید جمالی
1
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سعید ستایشی
2
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سجاد تقوایی
3
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محسن جهانشاهی
4
1 - گروه مهندسی کامپیوتر و فناوری اطلاعات، دانشکده فنی و مهندسی، واحد تهران مرکزی، دانشگاه آزاد اسلامی، تهران، ایران
2 - دانشکده مهندسی فیزیک و انرژی، دانشگاه صنعتی امیرکبیر، تهران
3 - دانشکده مهندسی مکانیک، دانشگاه شیراز، شیراز، ایران
4 - گروه مهندسی کامپیوتر و فناوری اطلاعات، دانشکده فنی و مهندسی، واحد تهران مرکزی، دانشگاه آزاد اسلامی، تهران، ایران
کلید واژه: کنجکاوی, هوش مصنوعی, یادگیری ماشین, یادگیری تقویتی, متغیرهای هم سنجش, پوشش فضا,
چکیده مقاله :
یکی از جنبه های کلیدی که می تواند هوش مصنوعی را به سطح بالاتری از توانایی برساند، کنجکاوی است. همانند انسان ها، در هوش مصنوعی نیز، کنجکاوی می تواند به عنوان یک مکانیسم کلیدی برای بهبود یادگیری فعال و اکتشاف در محیط های پیچیده و ناشناخته عمل کند. در این مقاله مروری، تلاش ها برای مدلسازی و شبیه سازی کنجکاوی در ماشین ها به منظور ایجاد سیستم هایی که بتوانند به طور خودکار و مستقل رفتارهای اکتشافی از خود نشان دهند مورد بررسی قرار گرفته است. در این پژوهش، با بررسی مطالعات روانشناختی در مورد کنجکاوی و مدل های محاسباتی موجود در هوش مصنوعی، به دنبال درک عمیقتری از مفهوم کنجکاوی و چگونگی شبیه سازی آن در ماشین ها هستیم. همچنین، به بررسی مزایا و محدودیت های رویکردهای موجود پرداخته ایم. نتایج این پژوهش نشان می دهد که کنجکاوی می تواند به عنوان یک عامل مهم در تسریع یادگیری، افزایش توانایی تعمیم‌پذیری مدل ها و بهبود عملکرد در وظایف چالش‌برانگیز عمل کند. همچنین با معرفی یک متغیر هم سنجش جدید به نام «پوشش فضا»، به دنبال تحقیقات جدیدی برای این نوع مدلسازی کنجکاوی در هوش مصنوعی هستیم. در نهایت، در کنار برشمردن برخی کاربردها، با ارائه پیشنهاداتی برای تحقیقات آینده، تلاش کردهایم تا مسیری را برای توسعه سیستم های هوش مصنوعی کنجکاوتر و قدرتمندتر هموار کنیم.
One key aspect that can elevate artificial intelligence to a higher level of capability is curiosity. Similar to humans, in artificial intelligence, curiosity can serve as a key mechanism for improving active learning and exploration in complex and unknown environments. This review paper examines efforts to model and simulate curiosity in machines in order to create systems that can automatically and independently exhibit exploratory behaviors. By investigating psychological studies on curiosity and existing computational models in artificial intelligence, this research seeks a deeper understanding of the concept of curiosity and how it can be simulated in machines. Additionally, we have examined the advantages and limitations of existing approaches. The results of this research show that curiosity can serve as an important factor in accelerating learning, increasing the generalizability of models, and improving performance in challenging tasks. Furthermore, by introducing a new metric called "space coverage," we propose new avenues for research in this type of curiosity modeling in artificial intelligence. Finally, along with enumerating some applications, we have attempted to pave the way for the development of more curious and powerful artificial intelligence systems by providing suggestions for future research.
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