Mimicking Behaviors in Separated Domains
This work addresses a foundational problem in AI for formalizing and automating behavior mimicry across domains, but it appears incremental as it builds on existing LTLf frameworks without introducing a new paradigm.
The paper tackles the problem of synthesizing strategies for intelligent agents to mimic behaviors between two separated dynamic domains using LTLf specifications, and it studies synthesis algorithms and computational properties for various forms of mapping specifications.
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.