论文标题
对不完美域模型的目标识别
Goal Recognition over Imperfect Domain Models
论文作者
论文摘要
目标识别是通过在环境中观察其行为来认识到自主代理或人类的预期目标的问题。在过去的几年中，大多数现有的目标和计划识别方法一直在忽略有关对自主代理行为的环境正式环境的域名模型的缺陷的必要性。在本论文中，我们介绍了对不完善域模型的目标识别问题，并开发了解决方案方法，这些方法明确处理了两种不同类型的不完美域模型：（1）不可能的，而不是已知的，而不是行动描述中可能出现的，而不是已知的，而不是已知的前提条件和效果； （2）近似连续域模型，其中过渡函数是从过去的观测值中近似而定义明确的。我们通过利用和调整文献中现有的识别方法来开发对不完美领域模型的新颖目标识别方法。对这两种类型的不完善域模型的实验和评估表明，与文献中的基线方法相比，我们的新型目标识别方法是准确的，在几个级别的可观察性和不完美之处。
Goal recognition is the problem of recognizing the intended goal of autonomous agents or humans by observing their behavior in an environment. Over the past years, most existing approaches to goal and plan recognition have been ignoring the need to deal with imperfections regarding the domain model that formalizes the environment where autonomous agents behave. In this thesis, we introduce the problem of goal recognition over imperfect domain models, and develop solution approaches that explicitly deal with two distinct types of imperfect domains models: (1) incomplete discrete domain models that have possible, rather than known, preconditions and effects in action descriptions; and (2) approximate continuous domain models, where the transition function is approximated from past observations and not well-defined. We develop novel goal recognition approaches over imperfect domains models by leveraging and adapting existing recognition approaches from the literature. Experiments and evaluation over these two types of imperfect domains models show that our novel goal recognition approaches are accurate in comparison to baseline approaches from the literature, at several levels of observability and imperfections.
