Devising a strategy to make a system mimicking behaviors from another system is a problem that naturally arises in many areas of Computer Science. In this work, we interpret this problem in the context of intelligent agents, from the perspective of LTLf, a formalism commonly used in AI for expressing finite-trace properties. Our model consists of two separated dynamic domains, D_A and D_B, and an LTLf specification that formalizes the notion of mimicking by mapping properties on behaviors (traces) of D_A into properties on behaviors of D_B. The goal is to synthesize a strategy that step-by-step maps every behavior of D_A into a behavior of D_B so that the specification is met. We consider several forms of mapping specifications, ranging from simple ones to full LTLf, and for each we study synthesis algorithms and computational properties.
翻译:设计一个让系统模仿来自另一个系统的行为的战略是计算机科学许多领域自然产生的一个问题。 在这项工作中,我们从智能剂的角度从LTLf的角度来解释这一问题,这是AI通常用来表达有限跟踪属性的一种形式主义。我们的模型包括两个分开的动态域,D_A和D_B,以及一个LTLf规格,它将模拟概念正式化,将D_A的行为属性(跟踪)映射成D_B的行为属性。目标是将一个逐步绘制D_A每项行为的策略综合到D_B的行为,以便达到规格。我们考虑了从简单的域到完整的LTLf,以及我们每次研究合成算法和计算属性的几种绘图规格形式。