Mimicking Behaviors in Separated Domains

TL;DR

This paper presents a formal framework for synthesizing strategies that enable one system to mimic another's behaviors within separated domains, using LTLf specifications to formalize and verify the mimicking process.

Contribution

It introduces a novel approach to behavior mimicking in separated domains using LTLf, along with synthesis algorithms and analysis of their computational properties.

Findings

Developed synthesis algorithms for various LTLf-based mimicking specifications

Analyzed the computational complexity of the proposed synthesis methods

Provided formal guarantees for behavior mapping correctness

Abstract

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.