The Increasing Cost Tree Search (ICTS) algorithm is used to produce optimal solutions to the multi-agent path finding problem (MAPF). In this problem, multiple agents are trying to reach their goals without conflicting with each other, while minimizing the total cost of the paths. ICTS has been shown to be very effective in finding optimal solutions. In this paper we consider the problem of finding solutions with bounded suboptimality by changing the order in which ICTS searches its increasing cost tree. With a variety of strategies, we are unable to consistently and significantly reduce the cost of ICTS. Further experimentation suggests why significantly more work is needed to modify ICTS to find suboptimal solutions.

We address the problem of optimal path finding for multiple agents where agents must not collide and their total travel cost should be minimized. Previous work used traditional single-agent search variants of the A* algorithm. We present a novel formalization for this problem which includes a search tree called the increasing cost tree (ICT) and a corresponding search algorithm that finds optimal solutions. We analyze this new formalization and compare it to the previous state-of-the-art A*-based approach. Experimental results on various domains show the benefits and drawbacks of this approach. A speedup of up to 3 orders of magnitude was obtained in a number of cases.