4 research outputs found
Hybrid Compositional Reasoning for Reactive Synthesis from Finite-Horizon Specifications
LTLf synthesis is the automated construction of a reactive system from a
high-level description, expressed in LTLf, of its finite-horizon behavior. So
far, the conversion of LTLf formulas to deterministic finite-state automata
(DFAs) has been identified as the primary bottleneck to the scalabity of
synthesis. Recent investigations have also shown that the size of the DFA state
space plays a critical role in synthesis as well.
Therefore, effective resolution of the bottleneck for synthesis requires the
conversion to be time and memory performant, and prevent state-space explosion.
Current conversion approaches, however, which are based either on
explicit-state representation or symbolic-state representation, fail to address
these necessities adequately at scale: Explicit-state approaches generate
minimal DFA but are slow due to expensive DFA minimization. Symbolic-state
representations can be succinct, but due to the lack of DFA minimization they
generate such large state spaces that even their symbolic representations
cannot compensate for the blow-up.
This work proposes a hybrid representation approach for the conversion. Our
approach utilizes both explicit and symbolic representations of the
state-space, and effectively leverages their complementary strengths. In doing
so, we offer an LTLf to DFA conversion technique that addresses all three
necessities, hence resolving the bottleneck. A comprehensive empirical
evaluation on conversion and synthesis benchmarks supports the merits of our
hybrid approach.Comment: Accepted by AAAI 2020. Tool Lisa for (a). LTLf to DFA conversion, and
(b). LTLf synthesis can be found here: https://github.com/vardigroup/lis
LTLf best-effort synthesis in nondeterministic planning domains
We study best-effort strategies (aka plans) in fully observable nondeterministic domains (FOND) for goals expressed in Linear Temporal Logic on Finite Traces (LTLf). The notion of best-effort strategy has been introduced to also deal with the scenario when no agent strategy exists that fulfills the goal against every possible nondeterministic environment reaction. Such strategies fulfill the goal if possible, and do their best to do so otherwise. We present a game-theoretic technique for synthesizing best-effort strategies that exploit the specificity of nondeterministic planning domains. We formally show its correctness and demonstrate its effectiveness experimentally, exhibiting a much greater scalability with respect to a direct best-effort synthesis approach based on re-expressing the planning domain as generic environment specifications