Abstract The Decentralized Partially Observable Markov Decision Process (Dec-POMDP) is a powerful model for multiagent planning under uncertainty, but its applicability i hindered by its high complexity – solving Dec-POMDPs optimally is NEXP-hard. Recently, Kumar et al. introduced the Value Factorization (VF) framework, which exploits decomposable value functions that can be factored into subfunctions. This framework has been shown to be a generalization of several models that leverage sparse agent interactions such as TI-Dec-MDPs, NDPOMDPs and TD-POMDPs. Existing algorithms for these models assume that the interaction graph of the problem is given. In this paper, we introduce three algorithms to automatically generate interaction graphs for models within the VF framework and establish lower and upper bounds on the expected reward of an optimal joint policy. We illustrate experimentally the benefits of thes techniques for sensor placement in a decentralized tracking application.