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1. EECBS: A Bounded-Suboptimal Search for Multi-Agent Path Finding

   Li, Jiaoyang; Ruml, Wheeler Ruml; Koenig, Sven (January 2021, Proceedings of the AAAI Conference on Artificial Intelligence)

   null (Ed.)

   Multi-Agent Path Finding (MAPF), i.e., finding collision-free paths for multiple robots, is important for many applications where small runtimes are necessary, including the kind of automated warehouses operated by Amazon. CBS is a lead- ing two-level search algorithm for solving MAPF optimally. ECBS is a bounded-suboptimal variant of CBS that uses focal search to speed up CBS by sacrificing optimality and instead guaranteeing that the costs of its solutions are within a given factor of optimal. In this paper, we study how to decrease its runtime even further using inadmissible heuristics. Motivated by Explicit Estimation Search (EES), we propose Explicit Estimation CBS (EECBS), a new bounded-suboptimal variant of CBS, that uses online learning to obtain inadmissible estimates of the cost of the solution of each high-level node and uses EES to choose which high-level node to expand next. We also investigate recent improvements of CBS and adapt them to EECBS. We find that EECBS with the improvements runs significantly faster than the state-of-the-art bounded-suboptimal MAPF algorithms ECBS, BCP-7, and eMDD-SAT on a variety of MAPF instances. We hope that the scalability of EECBS enables additional applications for bounded-suboptimal MAPF algorithms. 

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2. Multi-Agent Path Finding with Mutex Propagation

   Zhang, H.; Li, J.; Surynek, P; Koenig, S.; Kumar, S. (June 2020, Proceedings of the International Conference on Automated Planning and Scheduling)

   Mutex propagation is a form of efficient constraint propagation popularly used in AI planning to tightly approximate the reachable states from a given state. We utilize this idea in the context of Multi-Agent Path Finding (MAPF). When adapted to MAPF, mutex propagation provides stronger constraints for conflict resolution in Conflict-Based Search (CBS), a popular optimal MAPF algorithm, and provides it with the ability to identify and reason with symmetries in MAPF. While existing work identifies a limited form of symmetries using rectangle reasoning and requires the manual design of symmetry-breaking constraints, mutex propagation is more general and allows for the automated design of symmetry-breaking constraints. Our experimental results show that CBS with mutex propagation is capable of outperforming CBSH with rectangle reasoning, a state-of-the-art variant of CBS, with respect to runtime and success rate. 

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3. Conflict-Based Search with Optimal Task Assignment

   Hoenig, W.; Kiesel, S.; Tinka, A.; Durham, J.; Ayanian, N. (January 2018, Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems)

   We consider a variant of the Multi-Agent Path-Finding problem that seeks both task assignments and collision-free paths for a set of agents navigating on a graph, while minimizing the sum of costs of all agents. Our approach extends Conflict-Based Search (CBS), a framework that has been previously used to find collision-free paths for a given fixed task assignment. Our approach is based on two key ideas: (i) we operate on a search forest rather than a search tree; and (ii) we create the forest on demand, avoiding a factorial explosion of all possible task assignments. We show that our new algorithm, CBS-TA, is complete and optimal. The CBS framework allows us to extend our method to ECBS-TA, a bounded suboptimal version. We provide extensive empirical results comparing CBS-TA to task assignment followed by CBS, Conflict-Based Min-Cost-Flow (CBM), and an integer linear program (ILP) solution, demonstrating the advantages of our algorithm. Our results highlight a significant advantage in jointly optimizing the task assignment and path planning for very dense cases compared to the traditional method of solving those two problems independently. For large environments with many robots we show that the traditional approach is reasonable, but that we can achieve similar results with the same runtime but stronger suboptimality guarantees. 

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4. A New Constraint Satisfaction Perspective on Multi-Agent Path Finding: Preliminary Results

   Wang, J.; Li, J.; Ma, H.; Koenig, S.; Kumar, S. (January 2019, 18th International Conference on Autonomous Agents and Multi-Agent Systems)

   In this paper, we adopt a new perspective on the Multi-Agent Path Finding (MAPF) problem and view it as a Constraint Satisfaction Problem (CSP). A variable corresponds to an agent, its domain is the set of all paths from the start vertex to the goal vertex of the agent, and the constraints allow only conflict-free paths for each pair of agents. Although the domains and constraints are only implicitly defined, this new CSP perspective allows us to use successful techniques from CSP search. With the concomitant idea of using matrix computations for calculating the size of the reduced domain of an uninstantiated variable, we apply Dynamic Variable Ordering and Rapid Random Restarts to the MAPF problem. In our experiments, the resulting simple polynomial-time MAPF solver, called Matrix MAPF solver, either outperforms or matches the performance of many state-of-the-art solvers for the MAPF problem and its variants. 

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5. PRUC: P-regions with user-defined constraint

   https://doi.org/10.14778/3494124.3494133  

   Liu, Yongyi; Mahmood, Ahmed R.; Magdy, Amr; Rey, Sergio (November 2021, Proceedings of the VLDB Endowment)

   This paper introduces a generalized spatial regionalization problem, namely, PRUC ( P -Regions with User-defined Constraint) that partitions spatial areas into homogeneous regions. PRUC accounts for user-defined constraints imposed over aggregate region properties. We show that PRUC is an NP-Hard problem. To solve PRUC, we introduce GSLO (Global Search with Local Optimization), a parallel stochastic regionalization algorithm. GSLO is composed of two phases: (1) Global Search that initially partitions areas into regions that satisfy a user-defined constraint, and (2) Local Optimization that further improves the quality of the partitioning with respect to intra-region similarity. We conduct an extensive experimental study using real datasets to evaluate the performance of GSLO. Experimental results show that GSLO is up to 100× faster than the state-of-the-art algorithms. GSLO provides partitioning that is up to 6× better with respect to intra-region similarity. Furthermore, GSLO is able to handle 4× larger datasets than the state-of-the-art algorithms. 

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