Influence-Optimistic Local Values for Multiagent Planning

Over the last decade, methods for multiagent planning under uncertainty have increased in scalability. However, many methods assume value factorization or are not able to provide quality guarantees. We propose a novel family of influence-optimistic upper bounds on the optimal value for problems with 100s of agents that do not exhibit value factorization.