We consider the problem of finding a single-path intra-domain routing for time-varying traffic. We characterize the traffic variations by a finite set of traffic profiles with given non-zero fractions of occurrence. Our goal is to optimize the average performance over all of these traffic profiles. We solve the optimal multi-path version of this problem using linear programming and develop heur...

Planning collision-free paths for multiple robots is important real-world multi-robot systems and has been studied as an optimization problem on graphs, called multi-agent path finding (MAPF). This review surveys different categories of classic state-of-the-art MAPF algorithms research attempts to tackle the challenges generalizing techniques scenarios. Solving problems optimally computationall...

We describe a new sound and complete method forcompiling contingent planning problems with sensingactions into classical planning. Our encodesconditional plans within linear, classicalplan. This allows our planner, MPSR, to reasonabout multiple future outcomes of sensing actions,and makes it less susceptible dead-ends. MPRS,however, generates very large planningproblems. To overcome this, we us...

Learning communication via deep reinforcement learning (RL) or imitation (IL) has recently been shown to be an effective way solve Multi-Agent Path Finding (MAPF). However, existing based MAPF solvers focus on broadcast communication, where agent broadcasts its message all other predefined agents. It is not only impractical but also leads redundant information that could even impair the multi-a...