  Currently, in the study of multiagent systems, the intentions of agents are
usually ignored. Nonetheless, as pointed out by Theory of Mind (ToM), people
regularly reason about other's mental states, including beliefs, goals, and
intentions, to obtain performance advantage in competition, cooperation or
coalition. However, due to its intrinsic recursion and intractable modeling of
distribution over belief, integrating ToM in multiagent planning and decision
making is still a challenge. In this paper, we incorporate ToM in multiagent
partially observable Markov decision process (POMDP) and propose an adaptive
training algorithm to develop effective collaboration between agents with ToM.
We evaluate our algorithms with two games, where our algorithm surpasses all
previous decentralized execution algorithms without modeling ToM.
