Markov Decision Processes are a powerful framework for planning under uncertainty, but current algorithms have difficulties scaling to large problems. We present a novel probabil...
Iterative solvers such as the Jacobi and Gauss-Seidel relaxation methods are important, but time-consuming building blocks of many scientific and engineering applications. The per...
We present a scheduler with a web interface for generating fair game schedules of a tournament. The tournament can be either single or double round-robin or something in between. T...
We provide a reformulation of the constraint hierarchies (CHs) framework based on the notion of error indicators. Adapting the generalized view of local consistency in semiring-ba...
Stefano Bistarelli, Philippe Codognet, Kin Chuen H...
Optimized solvers for the Boolean Satisfiability (SAT) problem have many applications in areas such as hardware and software verification, FPGA routing, planning, etc. Further use...
Fadi A. Aloul, Arathi Ramani, Igor L. Markov, Kare...