Efficient Algorithms And Representations For Chance-constrained Mixed Constraint Programming

Resistance to autonomous systems comes in part from the perceived unreliability of the systems. Concerns can be addressed by guarantees of the probability of success. This is achieved in chanceconstrained constraint programming (CC-CP) by imposing constraints required for success, and providing upper-bounds on the probability of violating constraints. This extended abstract reports on novel uncertainty representations to address problems prevalent in current methods.