no code implementations • 15 May 2023 • Suguman Bansal, Yong Li, Lucas Martinelli Tabajara, Moshe Y. Vardi, Andrew Wells
Our central result is that LTLf model checking of non-terminating transducers is \emph{exponentially harder} than that of terminating transducers.
no code implementations • 6 Jun 2022 • Kishor Jothimurugan, Suguman Bansal, Osbert Bastani, Rajeev Alur
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.
1 code implementation • 20 May 2022 • Suguman Bansal, Lydia Kavraki, Moshe Y. Vardi, Andrew Wells
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.
1 code implementation • NeurIPS 2021 • Kishor Jothimurugan, Suguman Bansal, Osbert Bastani, Rajeev Alur
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.
1 code implementation • 6 Jan 2021 • Suguman Bansal, Krishnendu Chatterjee, Moshe Y. Vardi
Several problems in planning and reactive synthesis can be reduced to the analysis of two-player quantitative graph games.
1 code implementation • 19 Nov 2019 • Suguman Bansal, Yong Li, Lucas M. Tabajara, Moshe Y. Vardi
Our approach utilizes both explicit and symbolic representations of the state-space, and effectively leverages their complementary strengths.