The talk will have three parts in which I will discuss the results achieved with my colleagues about nondeterministic, deterministic, and semi-deterministic omega-automata. For nondeterministic automata, I will show which aspects of property automata can influence the performance of explicit model checking and what improvements can be made in LTL-to-automata traslation if we have some knowledge about the verified system. Then I will present our efficient translation of a fragment of LTL to deterministic automata. Finally, I will explore the jungle of Buchi automata classes that lie between deterministic and nondeterministic automata. I will present how to efficiently complement semi-deterministic automata and how to obtain them from nondeterministic generalized Buchi automata.
Languages K and L are separable by language S if K is included in S and L is disjoint from S.
I will present recent results on decidability of the problem whether two languages of one counter automata are separable
by some regular language.
Probabilistic programming is en vogue. It is used to describe
complex Bayesian networks, quantum programs, security protocols and
biological systems. Programming languages like C, C#, Java, Prolog,
Scala, etc. all have their probabilistic version. Key features are
random sampling and means to adjust distributions based on obtained
information from measurements and system observations. We show some
semantic intricacies, argue that termination is more involved than the
halting problem, and discuss recursion as well as run-time analysis.
Constrained counting and sampling are two fundamental problems in Computer Science with numerous applications, including network reliability, privacy, probabilistic reasoning, and constrained-random verification. In constrained counting, the task is to compute the total weight, subject to a given weighting function, of the set of solutions of the given constraints . In constrained sampling, the task is to sample randomly, subject to a given weighting function, from the set of solutions to a set of given constraints.
In this talk, I will introduce a novel algorithmic framework for constrained sampling and counting that combines the classical algorithmic technique of universal hashing with the dramatic progress made in Boolean reasoning over the past two decades. This has allowed us to obtain breakthrough results in constrained sampling and counting, providing a new algorithmic toolbox in machine learning, probabilistic reasoning, privacy, and design verification . I will demonstrate the utility of the above techniques on various real applications including probabilistic inference, design verification and our ongoing collaboration in estimating the reliability of critical infrastructure networks during natural disasters.
Kuldeep Meel is a final year PhD candidate in Rice University working with Prof. Moshe Vardi and Prof. Supratik Chakraborty. His research broadly lies at the intersection of artificial intelligence and formal methods. He is the recipient of a 2016-17 IBM PhD Fellowship, the 2016-17 Lodieska Stockbridge Vaughn Fellowship and the 2013-14 Andrew Ladd Fellowship. His research won the best student paper award at the International Conference on Constraint Programming 2015. He obtained a B.Tech. from IIT Bombay and an M.S. from Rice in 2012 and 2014 respectively. He co-won the 2014 Vienna Center of Logic and Algorithms International Outstanding Masters thesis award.