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Dettaglio pubblicazione

2023, THE JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH, Pages 1087-1112 (volume: 77)

Mimicking Behaviors in Separated Domains (01a Articolo in rivista)

De Giacomo G., Fried D., Patrizi F., Zhu S.

Devising a strategy to make a system mimic 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, DA and DB , and an LTLf specification that formalizes the notion of mimicking by mapping properties on behaviors (traces) of DA into properties on behaviors of DB . The goal is to synthesize a strategy that step-by-step maps every behavior of DA into a behavior of DB 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.
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