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Incremental LTLf Synthesis
arXiv – CS AI|Giuseppe De Giacomo, Yves Lesp\'erance, Gianmarco Parretti, Fabio Patrizi, Moshe Y. Vardi||5 views
🤖AI Summary
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.
Key Takeaways
- →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.
#ltlf-synthesis#reactive-synthesis#ai-agents#automata#formula-progression#incremental-synthesis#autonomous-systems
Read Original →via arXiv – CS AI
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