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The Multi-Agent Path Finding (MAPF) is a problem of finding a plan for agents to reach their desired locations without colliding. Distributed Multi-Agent Path Finder (DMAPF) solves the MAPF problem by decomposing a given MAPF problem instance into smaller subproblems and solve them in parallel. DMAPF works in rounds. Between two consecutive rounds, agents may migrate between two adjacent subproblems following their abstract plans, which are pre-computed, until all of them reach the areas that contain their desired locations. Previous works on DMAPF compute an abstract plan for each agent without the knowledge of other agents’ abstract plans, resulting in high congestion in some areas, especially those that act as corridors. The congestion negatively impacts the runtime of DMAPF and prevents it from being able to solve dense MAPF problems. In this paper, we (i) investigate the use of Uniform-Cost Search to mitigate the congestion. Additionally, we explore the use of several other techniques including (ii) using timeout estimation to preemptively stop solving and relax a subproblem when it is likely to get stuck; (iii) allowing a solving process to manage multiple subproblems – aimed to increase concurrency; and (iv) integrating with MAPF solvers from the Conflict-Based Search family. Experimental results show that our new system is several times faster than the previous ones; can solve larger and denser problems that were unsolvable before; and has better runtime than PBS and EECBS, which are state-of-the-art centralized suboptimal MAPF solvers, in problems with a large number of agents. 

Inspired by the recent problems in supply chains, we propose an approach to declarative modeling of contracts between agents that will eventually support reasoning about resilience of and about ways to improve supply chains. Specifically, we present a high-level language for specifying and reasoning about contracts over action domains of agents. We assume that the behavior of the agents can be formally expressed through action theories and view a contract as a collection of constraints. Each constraint specifies the responsibility of an agent to achieve a certain result by a deadline. Each agent also has a mapping between constraints and the agent’s concerns, i.e. issues that the agent is concerned about, which are modeled in accordance with the CPS Framework proposed by the National Institute of Standards and Technology. We discuss how common questions related to the fulfillment of a contract or the concerns of the agents can be answered and computed via Answer Set Programming.