There has been an increased interest in the creation of AI social agents who possess complex behaviors that allow them to perform social interactions. Behavior trees provide a plan model execution that has been widely used to build complex behaviors for AI social agents. Behavior trees can be represented in the form of a memory structure known as cognitive scripts, which would allow them to evolve through further development over multiple exposure to repeated enactment of a particular behavior or similar ones. Behavior trees that share the same context will then be able to learn from each other resulting in new behavior trees with richer experience. The main challenge appears in the expensive cost of retrieving contextually similar behavior trees (scripts) from a repertoire of scripts to allow for that learning process to occur. This paper introduces a novel application of map-and-reduce technique to retrieve cognitive with low computational time and memory allocation. The paper focuses on the design of a corpus of cognitive scripts, as a knowledge engineering key challenge, and the application of map-and-reduce with semantic information to retrieve contextually similar cognitive scripts. The results are compared to other techniques used to retrieve cognitive scripts in the literature, such as Pharaoh which uses the least common parent (LCP) technique in its core. The results show that the map-and-reduce technique can be successfully used to retrieve cognitive scripts with high retrieval accuracy of 92.6%, in addition to being cost effective.

人们对创建具有复杂行为的人工智能社交代理的兴趣越来越大，这些行为允许他们进行社交互动。行为树提供了一种计划模型执行，已广泛用于为 AI 社交代理构建复杂行为。行为树可以以称为认知脚本的记忆结构的形式表示，这将允许它们通过多次暴露于特定行为或类似行为的重复制定而进一步发展。共享相同上下文的行为树将能够相互学习，从而产生具有更丰富经验的新行为树。主要挑战出现在从脚本库中检索上下文相似的行为树（脚本）以允许该学习过程发生的昂贵成本。本文介绍了一种新的 map-and-reduce 技术应用来检索具有低计算时间和内存分配的认知。本文重点关注认知脚本语料库的设计，作为知识工程的关键挑战，以及应用带有语义信息的 map-and-reduce 来检索上下文相似的认知脚本。将结果与文献中用于检索认知脚本的其他技术进行了比较，例如 Pharaoh 在其核心中使用了最不常见的父 (LCP) 技术。结果表明，map-and-reduce 技术可以成功地用于检索认知脚本，检索准确率高达 92.6%，并且具有成本效益。本文着重于认知脚本语料库的设计，作为知识工程的关键挑战，以及应用带有语义信息的 map-and-reduce 来检索上下文相似的认知脚本。将结果与文献中用于检索认知脚本的其他技术进行了比较，例如 Pharaoh 在其核心中使用了最不常见的父 (LCP) 技术。结果表明，map-and-reduce 技术可以成功地用于检索认知脚本，检索准确率高达 92.6%，并且具有成本效益。本文重点关注认知脚本语料库的设计，作为知识工程的关键挑战，以及应用带有语义信息的 map-and-reduce 来检索上下文相似的认知脚本。将结果与文献中用于检索认知脚本的其他技术进行了比较，例如 Pharaoh 在其核心中使用了最不常见的父 (LCP) 技术。结果表明，map-and-reduce 技术可以成功地用于检索认知脚本，检索准确率高达 92.6%，并且具有成本效益。将结果与文献中用于检索认知脚本的其他技术进行了比较，例如 Pharaoh 在其核心中使用了最不常见的父 (LCP) 技术。结果表明，map-and-reduce 技术可以成功地用于检索认知脚本，检索准确率高达 92.6%，并且具有成本效益。将结果与文献中用于检索认知脚本的其他技术进行了比较，例如 Pharaoh 在其核心中使用了最不常见的父 (LCP) 技术。结果表明，map-and-reduce 技术可以成功地用于检索认知脚本，检索准确率高达 92.6%，并且具有成本效益。