论文标题
行为痕迹的独立生成和分类
Domain-independent generation and classification of behavior traces
论文作者
论文摘要
金融机构主要与人打交道。因此，表征各种人类行为可以极大地帮助机构改善其与客户的关系以及与监管办公室的关系。在许多这样的互动中，人类都有一些内部目标，并在金融体系内执行一些行动，使他们实现目标。在本文中，我们将这些任务作为行为跟踪分类任务解决。观察者试图通过在给定环境中采取行动时观察其行为来学习其他代理人的表征。其他代理可以是几种类型的，观察者的目标是确定给定观测值痕迹的其他代理的类型。我们提出了cabbot，这是一种学习技术，允许代理商对观察行为的计划代理的类型进行在线分类。在这项工作中，观察者具有对环境的部分和嘈杂的可观察性（其他代理的状态和行动）。为了评估学习技术的性能，我们生成了一个独立于域的基于目标的代理模拟器。我们在几个（财务和非财务）领域中介绍了实验，并有令人鼓舞的结果。
Financial institutions mostly deal with people. Therefore, characterizing different kinds of human behavior can greatly help institutions for improving their relation with customers and with regulatory offices. In many of such interactions, humans have some internal goals, and execute some actions within the financial system that lead them to achieve their goals. In this paper, we tackle these tasks as a behavior-traces classification task. An observer agent tries to learn characterizing other agents by observing their behavior when taking actions in a given environment. The other agents can be of several types and the goal of the observer is to identify the type of the other agent given a trace of observations. We present CABBOT, a learning technique that allows the agent to perform on-line classification of the type of planning agent whose behavior is observing. In this work, the observer agent has partial and noisy observability of the environment (state and actions of the other agents). In order to evaluate the performance of the learning technique, we have generated a domain-independent goal-based simulator of agents. We present experiments in several (both financial and non-financial) domains with promising results.
