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Found 6 papers, 4 papers with code
Model Checking Strategies from Synthesis Over Finite Traces
Our central result is that LTLf model checking of non-terminating transducers is \emph{exponentially harder} than that of terminating transducers.
Specification-Guided Learning of Nash Equilibria with High Social Welfare
Our empirical evaluation demonstrates that our algorithm computes equilibrium policies with high social welfare, whereas state-of-the-art baselines either fail to compute Nash equilibria or compute ones with comparatively lower social welfare.
Synthesis from Satisficing and Temporal Goals
An alternative approach combining LTL synthesis with satisficing DS rewards (rewards that achieve a threshold) is sound and complete for integer discount factors, but, in practice, a fractional discount factor is desired.
Compositional Reinforcement Learning from Logical Specifications
Our approach then incorporates reinforcement learning to learn neural network policies for each edge (sub-task) within a Dijkstra-style planning algorithm to compute a high-level plan in the graph.
On Satisficing in Quantitative Games
Several problems in planning and reactive synthesis can be reduced to the analysis of two-player quantitative graph games.
Hybrid Compositional Reasoning for Reactive Synthesis from Finite-Horizon Specifications
Our approach utilizes both explicit and symbolic representations of the state-space, and effectively leverages their complementary strengths.
