  When agents swarm to execute a mission, some of them frequently exhibit
sudden failure, as observed from the command base. It is generally difficult to
determine whether a failure is caused by actuators (hypothesis, $h_a$) or
sensors (hypothesis, $h_s$) by solely relying on the communication between the
command base and concerning agent. However, by instigating collusion between
the agents, the cause of failure can be identified; in other words, we expect
to detect corresponding displacements for $h_a$ but not for $h_s$. In this
study, we considered the question as to whether artificial intelligence can
autonomously generate an action plan $\boldsymbol{g}$ to pinpoint the cause as
aforedescribed. Because the expected response to $\boldsymbol{g}$ generally
depends upon the adopted hypothesis [let the difference be denoted by
$D(\boldsymbol{g})$], a formulation that uses $D\left(\boldsymbol{g}\right)$ to
pinpoint the cause can be made. Although a $\boldsymbol{g}^*$ that maximizes
$D(\boldsymbol{g})$ would be a suitable action plan for this task, such an
optimization is difficult to achieve using the conventional gradient method, as
$D(\boldsymbol{g})$ becomes nonzero in rare events such as collisions with
other agents, and most swarm actions $\boldsymbol{g}$ give
$D(\boldsymbol{g})=0$. In other words, throughout almost the entire space of
$\boldsymbol{g}$, $D(\boldsymbol{g})$ has zero gradient, and the gradient
method is not applicable. To overcome this problem, we formulated an action
plan using Q-table reinforcement learning. Surprisingly, the optimal action
plan generated via reinforcement learning presented a human-like solution to
pinpoint the problem by colliding other agents with the failed agent. Using
this simple prototype, we demonstrated the potential of applying Q-table
reinforcement learning methods to plan autonomous actions to pinpoint the
causes of failure.
