  A game theoretic distributed decision making approach is presented for the
problem of control effort allocation in a robotic team based on a novel variant
of fictitious play. The proposed learning process allows the robots to
accomplish their objectives by coordinating their actions in order to
efficiently complete their tasks. In particular, each robot of the team
predicts the other robots' planned actions while making decisions to maximise
their own expected reward that depends on the reward for joint successful
completion of the task. Action selection is interpreted as an $n$-player
cooperative game. The approach presented can be seen as part of the
\emph{Belief Desire Intention} (BDI) framework, also can address the problem of
cooperative, legal, safe, considerate and emphatic decisions by robots if their
individual and group rewards are suitably defined. After theoretical analysis
the performance of the proposed algorithm is tested on four simulation
scenarios. The first one is a coordination game between two material handling
robots, the second one is a warehouse patrolling task by a team of robots, the
third one presents a coordination mechanism between two robots that carry a
heavy object on a corridor and the fourth one is an example of coordination on
a sensors network.
