  Social intelligence manifests the capability, often referred to as the Theory
of Mind (ToM), to discern others' behavioral intentions, beliefs, and other
mental states. ToM is especially important in multi-agent and human-machine
interaction environments because each agent needs to understand the mental
states of other agents in order to better respond, interact, and collaborate.
Recent research indicates that the ToM model possesses the capability to infer
beliefs, intentions, and anticipate future observations and actions;
nonetheless, its deployment in tackling intricate tasks remains notably
limited. The challenges arise when the number of agents increases, the
environment becomes more complex, and interacting with the environment and
predicting the mental state of each other becomes difficult and time consuming.
To overcome such limits, we take inspiration from the Theory of Collective Mind
(ToCM) mechanism, predicting observations of all other agents into a unified
but plural representation and discerning how our own actions affect this mental
state representation. Based on this foundation, we construct an imaginative
space to simulate the multi-agent interaction process, thus improving the
efficiency of cooperation among multiple agents in complex decision-making
environments. In various cooperative tasks with different numbers of agents,
the experimental results highlight the superior cooperative efficiency and
performance of our approach compared to the Multi-Agent Reinforcement Learning
(MARL) baselines. We achieve consistent boost on SNN- and DNN-based decision
networks, and demonstrate that ToCM's inferences about others' mental states
can be transferred to new tasks for quickly and flexible adaptation.
