EAPSI: Identifying Relations between Computer-Generated and Manually Annotated Interpretations of Activities for Planning and Plan Recognition Tasks

虽然人类使用单词来感知活动并通过某些属性对相似的活动进行聚类，但使用无监督学习算法的计算机不一定以相同的方式识别它们，从而生成不同的解释。这项研究的目标是在人类和机器的活动定义之间建立类比，以便人工智能规划和计划识别方法不需要针对每组定义进行调整。通常，要么人类定义活动的方式过于模糊，机器无法进行准确计算，要么计算机定义活动导致人们无法理解潜在的推理。为每个实体开发一个解释器不仅可以在共同解决问题时使用户和设备之间的交互更加顺畅，而且还有助于弥合人机差距。该项目将使用东京大学 Alex Fukunaga 博士感兴趣的研究领域的技术，他研究人工智能的许多方面，特别是有关自主规划、搜索和优化的方面。这项工作扩展了使用主题模型进行无监督活动识别的先前研究，提出了一种由约束优化和启发式搜索组成的两步​​方法。第一阶段使用约束优化将计算机识别的活动序列与给定的人类对同一序列的注释进行对齐。然后，第二阶段类似地将计算机识别的活动序列与通过人类定义的分层任务网络上的启发式搜索得出的序列注释进行对齐。为了开发这些方法，该项目将包括形式化约束和搜索问题、对其公式进行编码以及测试两个定义集之间已识别映射的结果。该测试将通过将 PI 先前开发的用于机器解释动作的无监督活动识别方法与使用人类感知动作表示的常用计划识别方法相结合来执行。该 NSF EAPSI 奖是与日本科学振兴会合作资助的。

While humans perceive activities using words and cluster similar activities by some properties, computers using unsupervised learning algorithms do not necessarily identify them the same way and thus generate different interpretations. The goal of this research is to develop an analogy between human and machine definitions of activities so that artificial intelligence planning and plan recognition methods do not need to be adjusted for each set of definitions. Usually, either humans define the activities in a way which is too vague for machines to make accurate computations or computers define activities such that people cannot understand the underlying reasoning. Developing an interpreter for each entity will not only smooth the interaction between users and devices when solving problems together, but also contribute to bridging the human-computer gap. This project will use techniques in the research areas of interest to Dr. Alex Fukunaga at the University of Tokyo who studies many facets of artificial intelligence, especially those regarding autonomous planning, search, and optimization.Extending prior research on unsupervised activity recognition using topic models, this work proposes a two-step approach consisting of constraint optimization and heuristic search. The first phase uses constraint optimization to align a computer's recognized activity sequence with a given human's annotation of the same sequence. Then the second phase similarly aligns a computer's recognized activity sequence with an annotation of the sequence derived by heuristic search over a human-defined hierarchical task network. To develop these methods, the project will include formalizing the constraint and search problems, coding their formulations, and testing results of the identified mappings between the two definition sets. This test will be performed by combining an unsupervised activity recognition method previously developed by the PI for machine-interpreted actions with a commonly used plan recognition method that uses human-perceived action representations. This NSF EAPSI award is funded in collaboration with the Japan Society for the Promotion of Science.