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Parosh Aziz Abdulla

Researcher at Uppsala University

Publications -  291
Citations -  8196

Parosh Aziz Abdulla is an academic researcher from Uppsala University. The author has contributed to research in topics: Model checking & Decidability. The author has an hindex of 45, co-authored 281 publications receiving 7722 citations. Previous affiliations of Parosh Aziz Abdulla include University of Edinburgh & Information Technology University.

Papers
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Book ChapterDOI

Stochastic games with lossy channels

TL;DR: Turn-based stochastic games on infinite graphs induced by game probabilistic lossy channel systems (GPLCS) are decidable, which generalizes the decidability result for PLCS-induced Markov decision processes in [10].
Proceedings ArticleDOI

General decidability theorems for infinite-state systems

TL;DR: This paper presents decidability results for a class of systems, which consist of a finite control part operating on an infinite data domain, and shows that the following properties are decidable for well-structured systems: reachability; eventuality; and simulation.
Journal ArticleDOI

Verifying programs with unreliable channels

TL;DR: The verification of a particular class of infinite-state systems, namely, systems consisting of finite-state processes that communicate via unbounded lossy FIFO channels, is considered and it is shown that several interesting verification problems are decidable by giving algorithms for verifying.
Journal ArticleDOI

Algorithmic Analysis of Programs with Well Quasi-ordered Domains

TL;DR: This paper presents decidability results for a class of systems which consist of a finite control part operating on an infinite data domain, and shows that the following properties are decidable for well-structured systems.
Book ChapterDOI

Symbolic Reachability Analysis Based on SAT-Solvers

TL;DR: This paper shows how to adapt standard algorithms for symbolic reachability analysis to work with SAT-solvers and shows that even with relatively simple techniques it is possible to verify systems that are known to be hard for BDD-based model checkers.