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Behavior selection method for intelligent artificial creatures using the degree of consideration-based mechanism of thought

机译:基于考虑程度的思维机制的智能人工生物行为选择方法

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Artificial creatures need an intelligent behavior selection method to be used as an intermediate interface for natural interaction with users. For this purpose, the mechanisms of thought were proposed based on the probability and the degree of consideration. However, they were time-consuming when applying to an intelligent artificial creature with large numbers of wills, contexts and behaviors. Moreover, since the context-based evaluation only considers the behaviors short-listed by the will-based evaluation, some generated behaviors are inappropriate over the perceived contexts. To solve these problems, this paper proposes a novel behavior selection method for the intelligent artificial creatures using the degree of consideration-based mechanism of thought (DoC-MoT). The behaviors are short-listed by current dominant wills and perceived contexts and then they are globally evaluated by the fuzzy integral of the partial evaluation values of behaviors over artificial creature''s wills and external contexts, with respect to the fuzzy measure values representing its degrees of consideration. The effectiveness of the proposed behavior selection method is demonstrated by experiments carried out with a synthetic character “DD” in the 3D virtual environment. The results show that the generated behaviors were appropriate both to the current wills and the perceived contexts. Moreover, the computation time to select a behavior was decreased in the proposed method than the behavior selection methods using the probability-based MoT and DoC-MoT without the behavior short-listing.
机译:人造生物需要一种智能的行为选择方法,以用作与用户自然互动的中间界面。为此,根据可能性和考虑程度提出了思考机制。但是,当将它们应用于具有大量遗嘱,环境和行为的智能人造生物时,它们非常耗时。此外,由于基于上下文的评估仅考虑了基于意愿评估的短名单中的行为,因此某些生成的行为在感知的上下文中是不合适的。为了解决这些问题,本文提出了一种基于考虑程度的思想机制(DoC-MoT)的智能人造生物行为选择方法。通过当前的主导意愿和感知环境将这些行为入围,然后通过对代表其行为的模糊度量值对人造生物的意愿和外部环境进行的行为的部分评估值的模糊积分对它们进行全局评估考虑程度。通过在3D虚拟环境中使用合成字符“ DD”进行的实验证明了所提出的行为选择方法的有效性。结果表明,所产生的行为既适合当前的意愿,又适合所感知的环境。此外,与不使用行为短名单的基于概率的MoT和DoC-MoT的行为选择方法相比,该方法减少了选择行为的计算时间。

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