摘要
一位新闻记者兼机器人与机器学习的工作人员新闻编辑每日新闻-人工智能的新研究是一篇报道的主题。根据NewsRx记者在匹兹堡的新闻报道，研究表明，“生成人工智能在许多领域都产生了重大影响，包括决策的计算认知模型，尽管这些应用之间还没有理论上的联系。这项工作将生成人工智能的应用分类到决策认知模型中。”这项研究的财政支持来自陆军研究办公室。新闻记者引用了卡内基梅隆大学的一句话：“这种分类是用来比较现有文献，并为设计消融研究提供见解，以评估我们在三种实验范式下提出的模型。这些用于模型比较的实验涉及基于视觉信息和自然语言的人类学习和决策建模。”本文的应用比较以实例学习理论为基础，经验决策理论：一种经验决策理论，由此产生了许多模型，并应用于多个领域和应用中。我们所进行的消融中表现最好的模型L使用生成模型来创建记忆再现和预测参与者的行为。该模型的结果证明了生成模型在形成记忆和预测行为方面在决策建模研究中的重要性在这项工作中，我们提出了一个整合生成和认知模型的模型，使用各种Stimu LI、应用和培训方法。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Artificial Intelligenc e is the subject of a report. According to news reporting from Pittsburgh, Penns ylvania, by NewsRx journalists, research stated, “Generative Artificial Intellig ence has made significant impacts in many fields, including computational cognit ive modeling of decision making, although these applications have not yet been t heoretically related to each other. This work introduces a categorization of app lications of Generative Artificial Intelligence to cognitive models of decision making.” Financial support for this research came from Army Research Office. The news correspondents obtained a quote from the research from Carnegie Mellon University, “This categorization is used to compare the existing literature and to provide insight into the design of an ablation study to evaluate our proposed model in three experimental paradigms. These experiments used for model compari son involve modeling human learning and decision making based on both visual inf ormation and natural language, in tasks that vary in realism and complexity. Thi s comparison of applications takes as its basis Instance-Based Learning Theory, a theory of experiential decision making from which many models have emerged and been applied to a variety of domains and applications. The best performing mode l from the ablation we performed used a generative model to both create memory r epresentations as well as predict participant actions. The results of this compa rison demonstrates the importance of generative models in both forming memories and predicting actions in decision-modeling research. In this work, we present a model that integrates generative and cognitive models, using a variety of stimu li, applications, and training methods.”
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