While the study of unit-cost Multi-Agent Pathfinding (MAPF) problems has been popular, many real-world problems require continuous time and costs. In this context, this paper studies symmetry-breaking enhancements for Continuous-Time Conflict-Based Search (CCBS), a solver for continuous-time MAPF. Resolving conflict symmetries in MAPF can require an exponential amount of work. We adapt known symmetry-breaking enhancements from unit-cost domains for CCBS: bypassing and biclique constraints. We then improve upon these to produce a new state-of-the-art algorithm: CCBS with disjoint k-partite cliques (CCBS+DK). Finally, we show empirically that CCBS+DK solves for up to 20% more agents in the same amount of time when compared to previous state-of-the-art.

It is important for autonomous multi-agent teams to coordinate actions so that collaborative goals can be achieved efficiently without conflicts. Practical issues in multi-agent, real-time systems are limited sensing and communication capabilities. A significant number of multi-agent algorithms rely on accurate state information for all agents in order to effectively coordinate. In this paper, we propose an approach called Reinforcement Learning with Epistemic Priors (MARL-EP). MARL-EP incorporates a shared mental model between agents by leveraging epistemic estimation to infer portions of the obser-vation space which are unobservable. We show that MARL-EP allows a very high level of coordination to be achieved with severely impaired sensing and zero communication between agents.

The main idea of conflict-based search (CBS), a popular, state-of-the-art algorithm for multi-agent pathfinding is to resolve conflicts between agents by systematically adding constraints to agents. Recently, CBS has been adapted for new domains and variants, including non-unit costs and continuous time settings. These adaptations require new types of constraints. This paper introduces a new automatic constraint generation technique called bipartite reduction (BR). BR converts the constraint generation step of CBS to a surrogate bipartite graph problem. The properties of BR guarantee completeness and optimality for CBS. Also, BR's properties may be relaxed to obtain suboptimal solutions. Empirical results show that BR yields significant speedups in 2k connected grids over the previous state-of-the-art for both optimal and suboptimal search.

The multi-agent pathfinding problem (MAPF) is the fundamental problem of planning paths for multiple agents, where the key constraint is that the agents will be able to follow these paths concurrently without colliding with each other. Applications of MAPF include automated warehouses, autonomous vehicles, and robotics. Research on MAPF has been flourishing in the past couple of years. Different MAPF research papers assume different sets of assumptions, e.g., whether agents can traverse the same road at the same time, and have different objective functions, e.g., minimize makespan or sum of agents' actions costs. These assumptions and objectives are sometimes implicitly assumed or described informally. This makes it difficult for establishing appropriate baselines for comparison in research papers, as well as making it difficult for practitioners to find the papers relevant to their concrete application. This paper aims to fill this gap and facilitate future research and practitioners by providing a unifying terminology for describing the common MAPF assumptions and objectives. In addition, we also provide pointers to two MAPF benchmarks. In particular, we introduce a new grid-based benchmark for MAPF, and demonstrate experimentally that it poses a challenge to contemporary MAPF algorithms.

Conflict-Based Search (CBS) is a state of the art algorithm for multi-agent pathfinding (MAPF). CBS has been studied in many domains, however, most research has focused on classic domains with point agents that move with unit time steps and unit costs. In this work, we are interested in MAPF solutions for classic domains and complex domains, that is, domains which include shaped agents, actions with non-unit costs, non-uniform action durations and/or non-holonomic or kinodynamic movement constraints. Prior work on sub-optimal formulations of CBS has focused on heuristics. Instead, our work introduces new types of constraints. We show that certain constraint formulations have properties that can cause CBS to run orders of magnitude faster, but may cause the algorithm to be incomplete and yield sub-optimal results. We introduce new conditional constraints which allow CBS to exploit constraint properties which cause it to run faster and still retain algorithmic completeness. We additionally formulate a new constraint accumulation technique called constraint overloading which utilizes conditional constraints in order to achieve further performance gains.

Multi-agent pathfinding (MAPF) has applications in navigation, robotics, games and planning. Most work on search-based optimal algorithms for MAPF has focused on simple domains with unit cost actions and unit time steps. Although these constraints keep many aspects of the algorithms simple, they also severely limit the domains that can be used. In this paper we introduce a new definition of the MAPF problem for non-unit cost and non-unit time step domains along with new multiagent state successor generation schemes for these domains. Finally, we define an extended version of the increasing cost tree search algorithm (ICTS) for non-unit costs, with two new sub-optimal variants of ICTS: epsilon-ICTS and w-ICTS. Our experiments show that higher quality sub-optimal solutions are achievable in domains with finely discretized movement models in no more time than lower-quality, optimal solutions in domains with coarsely discretized movement models.