Incremental LTLf Synthesis

Researchers present a new approach to incremental LTLf synthesis, where AI agents must adapt their strategies in real-time when receiving new goals during execution. The study proposes efficient techniques using auxiliary data structures and formula progression, though naive implementation of progression-based methods proves computationally uncompetitive.

• →Incremental LTLf synthesis allows AI agents to adapt strategies dynamically when new goals arrive during execution.
• →The proposed solution uses auxiliary data structures from automata-based synthesis to efficiently handle multiple LTLf goals.
• →Formula progression generates exponentially larger formulas, but their minimal automata remain bounded in size.
• →Naive implementation of progression-based solutions is computationally inefficient compared to the proposed approach.
• →This work advances reactive synthesis capabilities for autonomous systems that must handle evolving objectives.