在完全可观察的非确定领域模型中识别LTLf/PLTLf目标
Recognizing LTLf/PLTLf Goals in Fully Observable Non-Deterministic Domain Models
摘要：
目标识别的任务是辨别正确的预期目标，代理旨在实现，给定一组可能的目标，一个领域模型，和一系列的观察作为一个样本的计划正在执行的环境。现有的方法假设，可能的目标是正式的连接在确定性设置。在本文中，我们开发了一种新的方法，能够识别时间上扩展的目标，在完全可观察的非确定性（FOND）规划领域模型，专注于目标的线性时序逻辑（LTLf）和（纯）过去线性时序逻辑（PLTLf）表示的有限轨迹。我们经验评估我们的目标识别方法，使用不同的LTLf和PLTLf的目标超过六个常见的FOND规划领域模型，并表明我们的方法是准确的，以识别时间上扩展的目标在几个层次的可观察性。
英文摘要：
Goal Recognition is the task of discerning the correct intended goal that an agent aims to achieve, given a set of possible goals, a domain model, and a sequence of observations as a sample of the plan being executed in the environment. Existing approaches assume that the possible goals are formalized as a conjunction in deterministic settings. In this paper, we develop a novel approach that is capable of recognizing temporally extended goals in Fully Observable Non-Deterministic (FOND) planning domain models, focusing on goals on finite traces expressed in Linear Temporal Logic (LTLf) and (Pure) Past Linear Temporal Logic (PLTLf). We empirically evaluate our goal recognition approach using different LTLf and PLTLf goals over six common FOND planning domain models, and show that our approach is accurate to recognize temporally extended goals at several levels of observability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
