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Ufuk Topcu
Researcher at University of Texas at Austin
Publications - 504
Citations - 11791
Ufuk Topcu is an academic researcher from University of Texas at Austin. The author has contributed to research in topics: Markov decision process & Computer science. The author has an hindex of 44, co-authored 437 publications receiving 9636 citations. Previous affiliations of Ufuk Topcu include Google & University of Illinois at Urbana–Champaign.
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Proceedings ArticleDOI
Human-interpretable diagnostic information for robotic planning systems
TL;DR: An approach for generating structured probabilistic counterexamples is proposed using mixed integer linear programming and the usefulness of this approach is demonstrated via a case study of UAV mission planning demonstrated in the AMASE multi-UAV simulator.
Proceedings ArticleDOI
Salty-A Domain Specific Language for GR(1) Specifications and Designs
TL;DR: Salty is presented, a domain-specific language for Generalized Reactivity(l) or GR(1) specifications that makes such specifications easier to write and debug by supporting features such as richer input and output types, user-defined macros, common specification patterns, and specification optimization and sanity checking.
Posted Content
Correct-by-synthesis reinforcement learning with temporal logic constraints
TL;DR: In this article, the synthesis of reactive controllers that optimize some a priori unknown performance criterion while interacting with an uncontrolled environment such that the system satisfies a given temporal logic specification is decoupled into two subproblems.
Proceedings ArticleDOI
Convex optimal uncertainty quantification: Algorithms and a case study in energy storage placement for power grids
TL;DR: The OUQ problem can be solved using convex optimization when the function under evaluation can be expressed in a polytopic canonical form (PCF) and iterative methods for scaling the convex formulation to larger systems are proposed.
Posted Content
The Partially Observable Games We Play for Cyber Deception.
Mohamadreza Ahmadi,Murat Cubuktepe,Nils Jansen,Sebastian Junges,Joost-Pieter Katoen,Ufuk Topcu +5 more
TL;DR: A two-player partially observable stochastic game (POSG) framework is used, wherein the deceiver has full observability over the states of the POSG, and the infiltrator has partial observability, to find robust strategies for the deceivers using mixed-integer linear programming.