Iterative Greedy Combinatorial Auction for Self Interested Multi-Agent Path nding

Abstract

This paper proposes an iterative greedy combinatorial auction (CA) mechanism for multi-agent pathfinding (MAPF). MAPF algorithms assign agents non-conflicting paths that minimize a global cost (e.g. the sum of travel costs). CA mechanisms allocate agents items that maximize social welfare. Recent works proposed two mechanisms that coordinate self-interested agents in MAPF: one is called iterative taxation framework (ITF), which is similar to the simultaneous ascending auction, and the other is an iterative CA mechanism, which guarantees to minimize the global cost. However, both methods have drawbacks: the former has no approximation guarantee, and the latter suffers from the fact that winner determination in the CA is computationally infeasible (NP-hard). In this paper, the author propose two iterative greedy CA (IGCA) mechanisms for MAPF that are computationally efficient. The first mechanism provides square of the number of agents approximation. Second mechanism was shown, by numerical simulations, to give a lower cost than ITF.