  The ultimate navigation efficiency of mobile robots in human environments
will depend on how we will appraise them: merely as impersonal machines or as
human-like agents. In the latter case, an agent may take advantage of the
cooperative collision avoidance, given that it possesses recursive cognition,
i.e.,the agent's decisions depend on the decisions made by humans that in turn
depend on the agent's decisions. To deal with this high-level cognitive skill,
we propose a neural network architecture implementing Prediction-for-CompAction
paradigm. The network predicts possible human-agent collisions and compacts the
time dimension by projecting a given dynamic situation into a static map.
Thereby emerging compact cognitive map can be readily used as a "dynamic GPS"
for planning actions or mental evaluation of the convenience of cooperation in
a given context. We provide numerical evidence that cooperation yields
additional room for more efficient navigation in cluttered pedestrian flows,
and the agent can choose path to the target significantly shorter than a robot
treated by humans as a functional machine. Moreover, the navigation safety,
i.e., the chances to avoid accidental collisions, increases under cooperation.
Remarkably, these benefits yield no additional load to the mean society effort.
Thus, the proposed strategy is socially compliant, and the humanoid agent can
behave as "one of us".
