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

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 leading 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.Comment: Published at AAAI 202

Heuristic search under time and cost bounds

Intelligence is difficult to formally define, but one of its hallmarks is the ability find a solution to a novel problem. Therefore it makes good sense that heuristic search is a foundational topic in artificial intelligence. In this context search refers to the process of finding a solution to the problem by considering a large, possibly infinite, set of potential plans of action. Heuristic refers to a rule of thumb or a guiding, if not always accurate, principle. Heuristic search describes a family of techniques which consider members of the set of potential plans of action in turn, as determined by the heuristic, until a suitable solution to the problem is discovered. This work is concerned primarily with suboptimal heuristic search algorithms. These algorithms are not inherently flawed, but they are suboptimal in the sense that the plans that they return may be more expensive than a least cost, or optimal, plan for the problem. While suboptimal heuristic search algorithms may not return least cost solutions to the problem, they are often far faster than their optimal counterparts, making them more attractive for many applications. The thesis of this dissertation is that the performance of suboptimal search algorithms can be improved by taking advantage of information that, while widely available, has been overlooked. In particular, we will see how estimates of the length of a plan, estimates of plan cost that do not err on the side of caution, and measurements of the accuracy of our estimators can be used to improve the performance of suboptimal heuristic search algorithms

Heuristic search under a deadline

In many heuristic search problems of practical interest, insufficient time is available to find a provably optimal solution. The currently accepted methods of finding a best possible sub-optimal solution within a time deadline are the anytime methods which do not directly consider the time remaining in the search. My thesis is that a deadline-cognizant approach, one which attempts to expend all available search effort towards a single final solution, has the potential for outperforming these methods. To support this thesis I introduce two new deadline-cognizant algorithms: Deadline Aware Search and Deadline Decision Theoretic Search. These approaches use on-line measurements of search behavior to guide the search towards the best possible solution reachable before the deadline. An empirical analysis illustrates that DAS is capable of outperforming the current incumbent methods across a wide variety of domains, the first deadline-cognizant heuristic search algorithm to do so

Graph search methods for non-order-preserving evaluation functions: applications to job sequencing problems

AbstractGraph search with A∗ is frequently faster than tree search. But A∗ graph search operates correctly only when the evaluation function is order-preserving. In the non-orderpreserving case, no paths can be discarded and the entire explicit graph must be stored in memory. Such situations arise in one-machine minimum penalty job sequencing problems when setup times are sequence dependent. GREC, the unlimited memory version of a memory-constrained search algorithm of the authors called MREC, has a clear advantage over A∗in that it is able to find optimal solutions to such problems. At the same time, it is as efficient as A∗ in solving graph search problems with order-preserving evaluation functions. Experimental results indicate that in the non-order-preserving case, GREC is faster than both best-first and depth-first tree search, and can solve problem instances of larger size than best-first tree search

Robot motion planning using real-time heuristic search

Autonomous mobile robots must be able to plan quickly and stay reactive to the world around them. Currently, navigating in the presence of dynamic obstacles is a problem that modern techniques struggle to handle in a real-time manner, even when the environment is known. The solutions range from using: 1) sampling-based algorithms which cut down on the shear size of these state spaces, 2) algorithms which quickly try to plan complete paths to the goal (to avoid local minima) and 3) using real-time search techniques designed for static worlds. Each of these methods have fundamental flaws that prevent it from being used in practice. In this thesis I offer three proposed techniques to help improve planning among dynamic obstacles. First, I present a new partitioned learning technique for splitting the costs estimates used by heuristic search techniques into those caused by the static environment and those caused by the dynamic obstacles in the world. This allows for much more accurate learning. Second, I introduce a novel decaying heuristic technique for generalizing cost-to-go over states of the same pose (x. y.theta.v) in the world. Third, I show a garbage collection mechanism for removing useless states from our search to cut down on the overall memory usage. Finally, I present a new algorithm called Partitioned Learning Real-time A*. PLRTA* uses all three of these new enhancements to navigate through worlds with dynamic obstacles in a real-time manner while handling the complex situations in which other algorithms fail. I empirically compare our algorithm to other competing algorithms in a number of random instances as well as hand crafted scenarios designed to highlight desirable behavior in specific situations. I show that PLRTA* outperforms the current state-of-the-art algorithms in terms of minimizing cost over a large number of robot motion planning problems, even when planning in fairly confined environments with up to ten dynamic obstacles

Metareasoning for Heuristic Search Using Uncertainty

Heuristic search methods are widely used in many real-world autonomous systems. Yet, people always want to solve search problems that are larger than time allows. To address these challenging problems, even suboptimally, a planning agent should be smart enough to intelligently allocate its computational resources, to think carefully about where in the state space it should spend time searching. For finding optimal solutions, we must examine every node that is not provably too expensive. In contrast, to find suboptimal solutions when under time pressure, we need to be very selective about which nodes to examine. In this dissertation, we will demonstrate that estimates of uncertainty, represented as belief distributions, can be used to drive search effectively. This type of algorithmic approach is known as metareasoning, which refers to reasoning about which reasoning to do. We will provide examples of improved algorithms for real-time search, bounded-cost search, and situated planning

Metareasoning for Heuristic Search Using Uncertainty

Heuristic search methods are widely used in many real-world autonomous systems. Yet, people always want to solve search problems that are larger than time allows. To address these challenging problems, even suboptimally, a planning agent should be smart enough to intelligently allocate its computational resources, to think carefully about where in the state space it should spend time searching. For finding optimal solutions, we must examine every node that is not provably too expensive. In contrast, to find suboptimal solutions when under time pressure, we need to be very selective about which nodes to examine. In this dissertation, we will demonstrate that estimates of uncertainty, represented as belief distributions, can be used to drive search effectively. This type of algorithmic approach is known as metareasoning, which refers to reasoning about which reasoning to do. We will provide examples of improved algorithms for real-time search, bounded-cost search, and situated planning