We propose an architecture that integrates Theory of Mind into a robot’s decision-making to infer a human’s intention and adapt to it. The architecture implements humanrobot collaborative decision-making for a robot incorporating human variability in their emotional and intentional states. This research first implements a mechanism for stochastically estimating a human’s belief over the state of the actions that the human could possibly be executing. Then, we integrate this information into a novel stochastic human-robot shared planner that models the human’s preferred plan. Our contribution lies in the ability of our model to handle the conditions: 1) when the human’s intention is estimated incorrectly and the true intention may be unknown to the robot, and 2) when the human’s intention is estimated correctly but the human doesn’t want the robot’s assistance in the given context. A robot integrating this model into its decision-making process would better understand a human’s need for assistance and therefore adapt to behave less intrusively and more reasonably in assisting its human companion.