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arXiv · 2410.08599

Synthesis from LTL with Reward Optimization in Sampled Oblivious Environments

Jean-François Raskin·Yun Chen Tsai
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Abstract

This paper addresses the synthesis of reactive systems that enforce hard constraints while optimizing for quality-based soft constraints. We build on recent advancements in combining reactive synthesis with example-based guidance to handle both types of constraints in stochastic, oblivious environments accessible only through sampling. Our approach constructs examples that satisfy LTL-based hard constraints while maximizing expected rewards-representing the soft constraints-on samples drawn from the environment. We formally define this synthesis problem, prove it to be NP-complete, and propose an SMT-based solution, demonstrating its effectiveness with a case study.

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Jean-François Raskin, Yun Chen Tsai. 2024-10-11. Synthesis from LTL with Reward Optimization in Sampled Oblivious Environments. https://arxiv.org/abs/2410.08599

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