دانلود مقاله انگلیسی رایگان:یک رویکرد جدید برای حل مشکلات برنامه ریزی هوش مصنوعی در تحولات نمودار - 2020
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  • A novel approach to solve AI planning problems in graph transformations A novel approach to solve AI planning problems in graph transformations
    A novel approach to solve AI planning problems in graph transformations

    سال انتشار:

    2020


    عنوان انگلیسی مقاله:

    A novel approach to solve AI planning problems in graph transformations


    ترجمه فارسی عنوان مقاله:

    یک رویکرد جدید برای حل مشکلات برنامه ریزی هوش مصنوعی در تحولات نمودار


    منبع:

    Sciencedirect - Elsevier - Engineering Applications of Artificial Intelligence, 92 (2020) 103684. doi:10.1016/j.engappai.2020.103684


    نویسنده:

    Einollah Pira


    چکیده انگلیسی:

    The aim of AI planning is to solve the problems with no exact solution available. These problems usually have a big search space, and planning may not find plans with the least actions and in the shortest time. Recent researches show that using suitable heuristics can help to find desired plans. In planning problems specified formally through graph transformation system (GTS), there are dependencies between applied rules (actions) in the search space. This fact motivates us to solve the planning problem for a small goal (instead of the main goal), extract dependencies from the searched space, and use these dependencies to solve the planning problem for the main goal. In GTS based systems, the nodes of a state (really is a graph) can be grouped due to their type. To create a small (refined) goal, we use a refinement technique to remove the predefined percent of nodes from each group of the main goal. Bayesian Optimization Algorithm (BOA) is then used to solve the planning problem for the refined goal. BOA is an Estimation of Distribution Algorithm (EDA) in which Bayesian networks are used to evolve the solution populations. Actually, a Bayesian network is learned from the current population, and then this network is employed to generate the next population. Since the last Bayesian network learned in BOA has the knowledge about dependencies between applied rules, this network can be used to solve the planning problem for the main goal. Experimental results on four well-known planning domains confirm that the proposed approach finds plans with the least actions and in the lower time compared with the state-of-the-art approaches.
    Keywords: Bayesian Optimization Algorithm | AI planning | Graph transformation system | Bayesian network | Refinement


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 14
    حجم فایل: 1376 کیلوبایت

    قیمت: رایگان


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