论文标题
通过基于模型和无模型的技术识别目标
Goal recognition via model-based and model-free techniques
论文作者
论文摘要
目标识别旨在从一系列观察结果中预测人类意图。这种能力使人们或组织可以预测未来的行动，并以积极的（协作）或负面（对抗性）方式进行干预。目标识别已成功地用于许多领域，但很少被金融机构使用。我们声称，这些技术在与金融相关的任务中的广泛使用已经成熟。执行目标识别的主要两种方法是基于模型（基于计划）和无模型（基于学习）。在本文中，我们将最新的学习技术调整为目标识别，并比较不同领域中基于模型和模型的方法。我们分析了实验数据，以了解使用两种方法的权衡。实验表明，基于计划的方法已准备好用于某些目标识别融资任务。
Goal recognition aims at predicting human intentions from a trace of observations. This ability allows people or organizations to anticipate future actions and intervene in a positive (collaborative) or negative (adversarial) way. Goal recognition has been successfully used in many domains, but it has been seldom been used by financial institutions. We claim the techniques are ripe for its wide use in finance-related tasks. The main two approaches to perform goal recognition are model-based (planning-based) and model-free (learning-based). In this paper, we adapt state-of-the-art learning techniques to goal recognition, and compare model-based and model-free approaches in different domains. We analyze the experimental data to understand the trade-offs of using both types of methods. The experiments show that planning-based approaches are ready for some goal-recognition finance tasks.
