# Ariel Felner's research while affiliated with Ben-Gurion University of the Negev and other places

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## Publications (222)

This paper solves the Watchman Route Problem (WRP) on a general discrete graph with Heuristic Search. Given a graph, a line-of-sight (LOS) function, and a start vertex, the task is to (offline) find a (shortest) path through the graph such that all vertices in the graph will be visually seen by at least one vertex on the path. WRP is reminiscent bu...

There are many settings that extend the basic shortest path search problem. In Bounded-Cost Search, we are given a constant bound and the task is to find a solution within the bound. In Bi-Objective Search, each edge is associated with two costs (objectives) and the task is to minimize both objectives. In this paper, we combine both these settings...

In the Watchman Route Problem (WRP), the task is to find a path for a watchman agent such that all locations in the given map will be visually seen by the watchman at least once during the path traversal. Recently, the problem has been optimally solved on a grid map using heuristic search. In this paper, we extend this work to the case of multiple...

Online MAPF extends the classical Multi-Agent Path Finding problem (MAPF) by considering a more realistic problem in which new agents may appear over time. As online solvers are not aware of which agents will join in the future, the notion of snapshot-optimal was defined, where only current knowledge is considered. In this paper, we perform an exte...

Work in machine learning has grown tremendously in the past years, but has had little to no impact on optimal search approaches. This paper looks at challenges in using deep learning as a part of optimal search, including what is feasible using current public frameworks, and what barriers exist for further adoption. The primary contribution of the...

The longest simple path and snake-in-a-box are combinatorial search problems of considerable research interest. We create a common framework of longest constrained path in a graph that contains these two problems, as well as other interesting maximum path problems, as special cases. We analyze properties of this general framework, produce bounds on...

To transmit information or transfer an object, two agents may need to reach the same location and meet. Often, such two agents operate in two separate environments and they can only meet at border locations. For example, a ship, sailing in the sea, needs to meet a truck traveling on land. These two agents are able to meet only at the shoreline. We...

In Multi-Agent Pathfinding (MAPF), the task is to find non-colliding paths for a set of agents. This paper focuses on search-based MAPF algorithms from the Conflict-Based Framework, which is introduced here. A common technique in such algorithms is to merge a group of dependent agents into a meta-agent and plan non-colliding paths for the meta-agen...

The Pareto-optimal frontier for a bi-objective search problem instance consists of all solutions that are not worse than any other solution in both objectives. The size of the Pareto-optimal frontier can be exponential in the size of the input graph, and hence finding it can be hard. Some existing works leverage a user-specified approximation facto...

In multi-objective search, edges are annotated with cost vectors consisting of multiple cost components. A path dominates another path with the same start and goal vertices iff the component-wise sum of the cost vectors of the edges of the former path is ``less than'' the component-wise sum of the cost vectors of the edges of the latter path. The P...

In the multi-agent path finding problem (MAPF) we are given a set of agents each with respective start and goal positions. The task is to find paths for all agents while avoiding collisions, aiming to minimize a given objective function. Many MAPF solvers were introduced in the past decade for optimizing two specific objective functions: sum-of-cos...

In this paper we study the One-to-Many Shortest Path Problem (OMSPP), which is the problem of solving k shortest path problems that share the same start node. This problem has been studied in the context of routing in road networks. While past work on routing relied on pre-processing the network, which is assumed to be provided explicitly. We explo...

In the multi agent path finding problem (MAPF) paths should be found for several agents, each with a different start and goal position such that agents do not collide. Previous optimal solvers applied global A*-based searches. We present a new search algorithm called Conflict Based Search (CBS). CBS is a two-level algorithm. At the high level, a se...

A* is optimally effective with regard to node expansions among unidirectional admissible algorithms—those that only assume that the heuristic used is admissible. Among bidirectional algorithms the Fractional MM algorithm is optimally effective (given the correct parameters) among admissible algorithms.This paper generalizes the bidirectional result...

Multi-Agent Path Finding (MAPF) is the problem of finding non-colliding paths for multiple agents. The classical problem assumes that all agents are homogeneous, with a fixed size and behavior. However, in reality agents are heterogeneous, with different sizes and behaviors. In this paper, we generalize MAPF to G-MAPF for the case of heterogeneous...

In this paper, we formalize and study the Moving Agents in Formation (MAiF) problem, that combines the tasks of finding short collision-free paths for multiple agents and keeping them in close adherence to a desired formation. Previous work includes controller-based algorithms, swarm-based algorithms, and potential-field-based algorithms. They usua...

Snake in the Box (SIB) is the problem of finding the longest simple path along the edges of an n-dimensional cube, subject to certain constraints. SIB has important applications in coding theory and communications. State of the art algorithms for solving SIB apply uninformed search with symmetry breaking techniques. We formalize this problem as a s...

Most work in heuristic search focused on path finding problems in which the cost of a path in the state space is the sum of its edges' weights. This paper addresses a different class of path finding problems in which the cost of a path is the product of its weights. We present reductions from different classes of multiplicative path finding problem...

Dynamic Potential Search (DPS) is a recently introduced search algorithm that returns a bounded-suboptimal cost solution. DPS orders nodes in the open-list based on their potential which is a combination of both the g- and h-values of a node. In this paper we study the behavior of DPS on weighted graphs. In particular, we develop a new variant of D...

Conflict-Based Search (CBS) is a leading algorithm for optimal Multi-Agent Path Finding (MAPF) which features strong performance. In CBS, one conflict in a high-level node is resolved to generate two child nodes, until a node with no conflicts is found. Choosing the right conflict to resolve can greatly speed up the search. It is currently recommen...

The task in the multi-agent path finding problem (MAPF) is to find paths for multiple agents, each with a different start and goal position, such that agents do not collide. A successful optimal MAPF solver is the conflict-based search (CBS) algorithm. CBS is a two level algorithm where special conditions ensure it returns the optimal solution. Sol...

Meta-reasoning can improve numerous search algorithms, but necessitates collection of statistics to be used as probability distributions, and involves restrictive meta-reasoning assumptions. The recently suggested scheme of type systems in search algorithms is used in this paper for collecting these statistics. The statistics are then used to bette...

In the multi-agent path finding (MAPF) the task is to find non-conflicting paths for multiple agents. Recently, existing makespan optimal SAT-based solvers for MAPF have been modified for the sum-of-costs objective. In this paper, we empirically compare the hardness of solving MAPF with SAT-based and search-based solvers under the makespan and the...

In multi-agent path finding (MAPF) the task is to find nonconflicting paths for multiple agents. In this paper we focus on finding suboptimal solutions for MAPF for the sum-of-costs variant. Recently, a SAT-based approached was developed to solve this problem and proved beneficial in many cases when compared to other search-based solvers. In this p...

Bidirectional search algorithms interleave a search forward from the start state (start ) and a search backward (i.e. using reverse operators) from the goal state (goal). We say that the two searches “meet in the middle” if neither search expands a node whose g-value (in the given direction) exceeds C*/2 , where C* is the cost of an optimal solutio...

Potential Search (PS) is an algorithm that is designed to solve bounded cost search problems. In this paper, we modify PS to work within the framework of bounded suboptimal search and introduce Dynamic Potential Search (DPS). DPS uses the idea of PS but modifies the bound to be the product of the minimal f-value in OPEN and the required suboptimal...

This paper introduces Exponential Deepening A* (EDA*), an Iterative Deepening (ID) algorithm where the threshold between successive Depth-First calls is increased exponentially. EDA* can be viewed as a Real-Time Agent-Centered (RTACS) algorithm. Unlike most existing RTACS algorithms, EDA* is proven to hold a worst case bound that is linear in the s...

A* is optimal among admissible unidirectional algorithms when searching with a consistent heuristic. Recently, similar optimality bounds have been established for bidirectional search, but no practical algorithm is guaranteed to always achieve this bound. In this paper we study the nature of the number of nodes that must be expanded in any front-to...

Multi-agent pathfinding (MAPF) is an area of expanding research interest. At the core of this research area, numerous diverse search-based techniques were developed in the past 6 years for optimally solving MAPF under the sum-of-costs objective function. In this paper we survey these techniques, while placing them into the wider context of the MAPF...

NBS is a non-parametric bidirectional search algorithm, proved to expand at most twice the number of node expansions required to verify the optimality of a solution. We introduce new variants of NBS that are aimed at finding all optimal solutions. We then introduce an algorithmic framework that includes NBS as a special case. Finally, we introduce...

Pattern databases are among the strongest known heuristics for many classical search benchmarks such as sliding-tile puzzles, the 4-peg Towers of Hanoi puzzles, Rubik's Cube, and TopSpin. Min-compression is a generally applicable technique for augmenting pattern database heuristics that has led to marked experimental improvements in some settings,...

Recent work in bidirectional heuristic search characterize pairs of nodes from which at least one node must be expanded in order to ensure optimality of solutions. We use these findings to propose a method for improving existing heuristics by propagating lower bounds between the forward and backward frontiers. We then define a number of desirable p...

Most work in heuristic search considers problems where a low cost solution is preferred (MIN problems). In this paper, we investigate the complementary setting where a solution of high reward is preferred (MAX problems). Example MAX problems include finding the longest simple path in a graph, maximal coverage, and various constraint optimization pr...

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 i...

In the multi-agent path-finding (MAPF) problem, the task is to find a plan for moving a set of agents from their initial locations to their goals without collisions. Following this plan, however, may not be possible due to unexpected events that delay some of the agents. We explore the notion of k-robust MAPF, where the task is to find a plan that...

Pattern Databases (PDBs) are a common form of abstraction-based heuristic whichare often compressed so that a large PDB can fit inmemory. Partial Pattern Databases (PPDBs) achieve this by storing only layersof the PDB which are close to the goal. This paper studies the problem of howto best compress and use the 457 GB 12-edge Rubik's cube PDB, sugg...

In the multi-agent path-finding (MAPF) problem a plan is needed to move a set of agents from their initial location to their goals without collisions. In this paper we introduce and study the k-robust MAPF problem, where we seek a plan that is robust to k unexpected delays per agent.

In this paper we study the k goal search problem (kGS), which is the problem of solving k shortest path problems that share the same start state. Two fundamental heuristic search approaches are analyzed: searching for the k goals one at a time, or searching for all k goals together in a single pass. Key theoretical properties are established and a...

Conflict-Based Search (CBS) and its generalization, Meta-Agent CBS are amongst the strongest newly introduced algorithms for Multi-Agent Path Finding. This paper introduces ICBS, an improved version of CBS. ICBS incorporates three orthogonal improvements to CBS which are systematically described and studied. Experimental results show that each of t...

The Target Oriented Network Intelligence Collection (TONIC) problem is the problem of finding profiles in a social network that contain publicly available information about a given target profile via automated crawling. Such profiles are called leads. Leads can be found by crawling the network using the profiles' friend lists (immediate neighborhoo...

In a multi-agent path-finding (MAPF) problem, the task is to find a plan for moving a set of agents from their initial locations to their goals without collisions. Following this plan, however, may not be possible due to unexpected events that delay some of the agents. Guaranteeing that collisions will never occur may be impossible. An important ta...

A* with lookahead (AL*) is a variant of A* that performs a cost-bounded DFS lookahead from a node when it is generated. We show that the original version of AL* (AL*0) can, in some circumstances, fail to return an optimal solution because of the move pruning it does. We present two new versions, AL*1 and ELH, that we prove to always be correct and...

fMM and GBFSH are two prominent bidirectional heuristic search algorithms. Over the past few years, there has been a great deal of theoretical and empirical work on both of these algorithms. As part of the research conducted on these algorithms, some interesting theoretical properties were proven for fMM and not for GBFSH and vice versa. In additio...

We examine two policies for reopening of nodes: never reopen (NR) and always reopen (AR). While there are circumstances where each policy isbeneficial, we observed empirically that NR is usually faster. However, NR may fail to return a solution of the desired quality in two scenarios:(1) in a bounded suboptimal search when inconsistent heuristics a...

In the Multi-Agent Meeting (MAM) problem, the task is to find a meeting location for multiple agents, as well as a path for each agent to that location. In this paper, we introduce MM*, a Multi-Directional Search algorithm that finds the optimal meeting location under different cost functions. MM* generalizes the Meet in the Middle (MM) bidirection...

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 fl...

Monte-Carlo Tree Search (MCTS) algorithms estimate the value of MDP states based on rewards received by performing multiple random simulations. MCTS algorithms can use different strategies to aggregate these rewards and provide an estimation for the states’ values. The most common aggregation method is to store the mean reward of all simulations. A...

This paper addresses the Target-Value Search (TVS) problem, which is the problem of finding a path between two nodes in a graph whose cost is as close as possible to a given target value, T. This problem has been previously addressed: first, for directed acyclic graphs; second, for general graphs under the assumption that nodes can be revisited giv...

Conflict-Based Search (CBS) and its enhancements are among the strongest algorithms for Multi-Agent Pathfinding. Recent work introduced an admissible heuristic to guide the high-level search of CBS. In this work, we prove the limitation of this heuristic, as it is based on cardinal conflicts only. We then introduce two new admissible heuristics by...

Multi-Agent Path Finding (MAPF) has been widely studied in the AI community. For example, Conflict-Based Search (CBS) is a state-of-the-art MAPF algorithm based on a two-level tree-search. However, previous MAPF algorithms assume that an agent occupies only a single location at any given time, e.g., a single cell in a grid. This limits their applic...

Optimal planning and heuristic search systems solve state-space searchproblems by finding a least-cost path from start to goal. As a byproduct of having an optimal path they also determine the optimal solution cost. In this paper we focus on the problem of determining the optimal solution cost for a state-space search problem directly, i.e., withou...

Multi-agent pathfinding (MAPF) deals with planning paths for individual agents such that a global cost function (e.g., the sum of costs) is minimized while avoiding collisions between agents. Previous work proposed centralized or fully cooperative decentralized algorithms assuming that agents will follow paths assigned to them. When agents are {\em...

This paper addresses the Target-Value Search (TVS) problem, which is the problem of finding a path between two nodes in a graph whose cost is as close as possible to a given target value T. This problem has been previously addressed only for directed acyclic graphs. In this work we develop the theory required to solve this problem optimally for any...

The task in the multi-agent path finding problem (MAPF) isto find paths for multiple agents, each with a different startand goal position, such that agents do not collide. It is possibleto solve this problem optimally with algorithms that arebased on the A* algorithm. Recently, we proposed an alternativealgorithm called Conflict-Based Search (CBS)...

A* is often described as being 'optimal,' in that it expands the minimum number of unique nodes. But, A* may generate many extra nodes which are never expanded. This is a performance loss, especially when the branching factor is large. Partial Expansion A* (PEA*) addresses this problem when expanding a node, n, by generating all the children of n b...

We present a new two-level search algorithm for optimal multi-agent path finding called Conflict Based Search (CBS). At the high level, a search is performed on a tree based on conflicts between agents. At the low level, a search is performed only for a single agent at a time. Experimental results on various problems shows a speedup of up to a full...

The differential heuristic (DH) is an effective memory-based heuristic for explicit state spaces. In this paper, we aim to improve its performance and memory usage. We introduce a compression method for DHs which stores only a portion of the original uncompressed DH, while preserving enough information to enable efficient search. Compressed DHs (CD...

In the Canadian Traveler Problem (CTP) a traveling agent is given a weighted graph, where some of the edges may be blocked, with a known probability. The agent needs to travel to a given goal. A solution for CTP is a policy, that has the smallest expected traversal cost. CTP is intractable. Previous work has focused on the case of a single agent. W...

A* is a best-first search algorithm that returns an optimal solution. w-admissible algorithms guarantee that the returned solution is no larger than w times the optimal solution. In this paper we introduce a generalization of the w-admissibility concept that we call PAC search, which is inspired by the PAC learning framework in Machine Learning. Th...

The baseline approach for optimal path finding in 4-connected grids is A* with Manhattan Distance. Nevertheless, a large number of enhancements were suggested over the years, usually requiring a preprocessing phase and/or additional memory to store smart lookup tables. In this paper we introduce an enhancement to A* (called BOXA*) on grids which do...

This extended abstract presents a bidirectional heuristic search algorithm called IDBiHS that operates under restricted memory. Several variants of this algorithm are introduced for different types of memory restrictions, and are compared against existing algorithms with similar restrictions.

Multi-Agent Meeting (MAM) is the problem of finding a meeting location for multiple agents and paths to that location. Recently, a Multi-Directional Heuristic Search algorithm, called MM*, was introduced. MM* is a state-of-the-art MAM optimal solver that searches from multiple directions (one for each agent) and is guided by a heuristic function. P...

In the Watchman Route Problem (WRP) we are given a grid map with obstacles and the task is to (offline) find a (shortest) path through the grid such that all cells in the map can be visually seen by at least one cell on the path. WRP was recently formalized and optimally solved with heuristic search. In this paper we show how the previous optimal m...

Multi-agent path finding (MAPF) is the problem of planning a set of non-conflicting plans on a graph, for a set of agents. Online MAPF extends MAPF by considering a more realistic problem in which new agents may appear over time. While planning, an online solver does not know whether and which agents will join in the future. Therefore, in online pr...

Conflict-Based Search (CBS) is a leading two-level algorithm for optimal Multi-Agent Path Finding (MAPF). At its high level, CBS expands nodes by resolving conflicts. In recent years, admissible heuristics were added to the high level of CBS. We enhance all known heuristic functions for CBS by using information about the cost of resolving certain c...

It is well known that many graph problems, like the Traveling Salesman Problem, are easier to solve in a Euclidean space. This motivates the idea of quickly preprocessing a given graph by embedding it in a Euclidean space to solve graph problems efficiently. In this paper, we study a nearlinear time algorithm, called FastMap, that embeds a given no...

Multi-Agent Path Finding (MAPF) is the planning problem of finding collision-free paths for a team of agents. We focus on Conflict-Based Search (CBS), a two-level tree-search state-of-the-art MAPF algorithm. The standard splitting strategy used by CBS is not disjoint, i.e., when it splits a problem into two subproblems, some solutions are shared by...

Conflict-Based Search (CBS) is a leading two-level algorithm for optimal Multi-Agent Path Finding (MAPF). The main step of CBS is to expand nodes by resolving conflicts (where two agents collide). Choosing the ‘right’ conflict to resolve can greatly speed up the search. CBS first resolves conflicts where the costs (g-values) of the resulting child...

The field of bidirectional heuristic search has recently seen great advances. However, the subject of memory-restricted bidirectional search has not received recent attention. In this paper we introduce a general iterative deepening bidirectional heuristic search algorithm (IDBiHS) that searches simultaneously in both directions while controlling t...

Multi-Agent Meeting (MAM) is the problem of finding a meeting location for multiple agents and paths to that location. Practically, a solution to MAM may contain conflicting paths. A related problem that plans conflict-free paths to a given set of goal locations is the Multi-Agent Path Finding problem (MAPF). In this paper, we solve the Conflict-Fr...

Two popular optimal search-based solvers for the multi-agent pathfinding (MAPF) problem, Conflict-Based Search (CBS) and Increasing Cost Tree Search (ICTS), have been extended separately for continuous time domains and symmetry breaking. However, an approach to symmetry breaking in continuous time domains remained elusive. In this work, we introduc...

Multi-Agent Meeting (MAM) is the problem of finding a meeting location for multiple agents and paths to that location. Practically, a solution to MAM may contain conflicting paths. A related problem that plans conflict-free paths to a given set of goal locations is the Multi-Agent Path Finding problem (MAPF). In this paper, we solve the Conflict-Fr...

Conflict-Based Search (CBS) is a leading algorithm for optimal Multi-Agent Path Finding (MAPF). CBS variants typically compute MAPF solutions using some form of A* search. However, they often do so under strict time limits so as to avoid exhausting the available memory. In this paper, we present IDCBS, an iterative-deepening variant of CBS which ca...

Recent work on bidirectional search defined a lower bound on costs of paths between pairs of nodes, and introduced a new algorithm, NBS, which is based on this bound. Building on these results, we introduce DVCBS, a new algorithm that aims to to further reduce the number of expansions. Generalizing beyond specific algorithms, we then propose a meth...

In a multi-agent path finding (MAPF) problem, the task is to move a set of agents to their goal locations without conflicts. In the real world, unexpected events may delay some of the agents. In this paper, we therefore study the problem of finding a p-robust solution to a given MAPF problem, which is a solution that succeeds with probability at le...

The question of when bidirectional heuristic search outperforms unidirectional heuristic search has been revisited numerous times in the field of Artificial Intelligence. This paper re-addresses the question of when bidirectional search outperforms unidirectional search using an updated theoretical understanding of the problem. We show that a core...

Prior approaches for finding the longest simple path (LSP) in a graph used constraints solvers and genetic algorithms. In this work, we solve the LSP problem with heuristic search. We first introduce several methods for pruning dominated path prefixes. Then, we propose several admissible heuristic functions for this problem. Experimental results de...

Multi-Agent Path Finding (MAPF) is the problem of finding non-colliding paths for multiple agents. The classical problem assumes that all agents are homogeneous, with a fixed size and behavior. However, in reality agents are heterogeneous, with different sizes and behaviors. In this paper, we generalize MAPF to G-MAPF for the case of heterogeneous...

In the Multi-Agent Meeting problem (MAM), the task is to find a meeting location for multiple agents, as well as a path for each agent to that location. In this paper, we introduce MM*, a Multi-Directional Heuristic Search algorithm that finds the optimal meeting location under different cost functions. MM* generalizes the Meet in the Middle (MM) b...

In a multi-agent path finding (MAPF) problem, the task is to move a set of agents to their goal locations without conflicts. In the real world, unexpected events may delay some of the agents. In this paper, we therefore study the problem of finding a p-robust solution to a given MAPF problem, which is a solution that succeeds with probability at le...

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...

Multi-agent path-finding (MAPF) is the problem of finding a plan for moving a set of agents from their initial locations to their goals without collisions. Following this plan, however, may not be possible due to unexpected events that delay some of the agents. In this work, we propose a holistic solution for MAPF that is robust to such unexpected...

A* is optimally efficient with regard to node expansions among unidirectional admissible algorithms — those that only assume that the heuristic used is admissible. This paper studies algorithms that are optimally efficient for bidirectional search algorithms. We present the Fractional MM algorithm and its sibling, the MT algorithm, which is simpler...

Conflict-Based Search (CBS) and its enhancements are among the strongest algorithms for Multi-Agent Path Finding. Recent work introduced an admissible heuristic to guide the high-level search of CBS. In this work, we prove the limitation of this heuristic, as it is based on cardinal conflicts only. We then introduce two new admissible heuristics by...

Multi-Agent Path Finding (MAPF) has been widely studied in the AI community. For example, Conflict-Based Search (CBS) is a state-of-the-art MAPF algorithm based on a twolevel tree-search. However, previous MAPF algorithms assume that an agent occupies only a single location at any given time, e.g., a single cell in a grid. This limits their applica...

NBS is a non-parametric bidirectional search algorithm proven to expand at most twice the number of node expansions required to verify the optimality of a solution. We introduce new variants of NBS that are aimed at finding all optimal solutions. We then introduce an algorithmic framework that includes NBS as a special case. Finally, we introduce D...

Target Oriented Network Intelligence Collection (TONIC) is a crawling process whose goal is to find social network profiles that contain information about a given target. Such profiles are called leads and the TONIC problem is how to minimize crawling costs incurred while finding them. We model this problem as a search problem in an unknown graph a...

The MAPF problem 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 and autonomous vehicles. Research on MAPF has been flourishing in the past couple of years...

Goal recognition is the task of inferring the goal of an actor given its observed actions. Attack graphs are a common representation of assets, vulnerabilities, and exploits used for analysis of potential intrusions in computer networks. This paper introduces new goal recognition algorithms on attack graphs. The main challenges involving goal recog...

In the multi-agent path-finding (MAPF) problem, the task is to find a plan for moving a set of agents from their initial locations to their goals without collisions. Following this plan, however, may not be possible due to unexpected events that delay some of the agents. We explore the notion of k-robust MAPF, where the task is to find a plan that...

We formalize Multi-Agent Path Finding with Deadlines (MAPF-DL). The objective is to maximize the number of agents that can reach their given goal vertices from their given start vertices within the deadline, without colliding with each other. We first show that MAPF-DL is NP-hard to solve optimally. We then present two classes of optimal algorithms...

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...