  In decentralized stochastic control, standard approaches for sequential
decision-making, e.g. dynamic programming, quickly become intractable due to
the need to maintain a complex information state. Computational challenges are
further compounded if agents do not possess complete model knowledge. In this
paper, we take advantage of the fact that in many problems agents share some
common information, or history, termed partial history sharing. Under this
information structure the policy search space is greatly reduced. We propose a
provably convergent, online tree-search based algorithm that does not require a
closed-form model or explicit communication among agents. Interestingly, our
algorithm can be viewed as a generalization of several existing heuristic
solvers for decentralized partially observable Markov decision processes. To
demonstrate the applicability of the model, we propose a novel collaborative
intrusion response model, where multiple agents (defenders) possessing
asymmetric information aim to collaboratively defend a computer network.
Numerical results demonstrate the performance of our algorithm.
