Optimal LTLf Synthesis
Strategy synthesis typically follows an all-or-nothing paradigm, returning unrealisable whenever a specification cannot be guaranteed in an uncertain environment. In this paper, we introduce optimal LTLf synthesis, where the goal is to realise as many objectives as possible from a given specification consisting of multiple objectives, especially for the case that they are not all jointly realisable. We first consider max-guarantee synthesis, which commits to a maximal set of objectives that we can a priori guarantee to realise. We then introduce max-observation synthesis, which maximises a posteriori realised objectives that may be incomparable on different executions. Finally, we present...
| Authors | Yujian Cao, Sven Schewe, Qiyi Tang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 12 May 2026 |
| Last updated at source | 27 May 2026 |
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