Pavel Surynek

Czech Technical University in Prague | ČVUT · Faculty of Information Technology (FIT)

An optimization variant of a problem of path planning for multiple robots is addressed in this work. The task is to find spatial-temporal path for each robot of a group of robots such that each robot can reach its destination by navigating through these paths. In the optimization variant of the problem, there is an additional requirement that the m...

Dynamic Constraint Satisfaction Problems play a very important role in modeling and solving real-life problems where the set of constraints is changing. The paper addresses a problem of maintaining arc consistency after removing a constraint from the constraint model. A new dynamic arc consis- tency algorithm is proposed that improves the practical...

This paper addresses a problem of path planning for multiple robots. An abstraction where the environment for robots is modeled as an undirected graph with robots placed in its vertices is used (this abstraction is also known as the problem of pebble motion on graphs). A class of the problem with bi-connected graph and at least two unoccupied verti...

We are dealing with solving difficult SAT instances in this pa per. We propose a method for preprocessing SAT instances (CNF formulas) by using consistency techniques known from constraint programming methodology and by using our own consistency technique based on clique decomposition of a graph representing conflicts in the i nput formula. The cli...

We address a variant of multi-agent path finding in continuous environment (CE-MAPF), where agents move along sets of smooth curves. Collisions between agents are resolved via avoidance in the space domain. A new Continuous Environment Conflict-Based Search (CE-CBS) algorithm is proposed in this work. CE-CBS combines conflict-based search (CBS) for...

We address rule-based algorithms for multi-agent path finding (MAPF). MAPF is a task of finding non-conflicting paths connecting agents' initial and goal positions in a shared environment specified via an undirected graph. Rule-based algorithms use a fixed set of predefined primitive operations to move agents to their goal positions in a complete m...

We focus on rule-based algorithms for multi-agent path finding (MAPF) in this paper. MAPF is a task of finding non-conflicting paths connecting agents’ specified initial and goal positions in a shared environment specified via an undirected graph. Rule-based algorithms use a fixed set of predefined primitives to move agents to their goal positions...

We study the planning-acting loop for the multi-agent path finding problem (MAPF). MAPF is a problem of navigating agents from their start positions to specified individual goal positions so that agents do not collide with each other. We focus on executing MAPF plans with a group of Crazyflies, small indoor quadcopters, for which we developed a pla...

Counterexample guided abstraction refinement (CEGAR) represents a powerful symbolic technique for various tasks such as model checking and reachability analysis. Recently, CEGAR combined with Boolean satisfiability (SAT) has been applied for multi-agent path finding (MAPF), a problem where the task is to navigate agents from their start positions t...

We address the problem of variable and truth-value choice in modern search-based Boolean satisfiability (SAT) solvers depending on the problem domain. The SAT problem is the task to determine truth-value assignment for variables of a given Boolean formula under which the formula evaluates to true. The SAT problem is often used as a canonical repres...

Multi-agent path finding (MAPF) is a task of finding non-conflicting paths connecting agents' specified initial and goal positions in a shared environment. We focus on compilation-based solvers in which the MAPF problem is expressed in a different well established formalism such as mixed-integer linear programming (MILP), Boolean satisfiability (SA...

Multi-agent pathfinding (MAPF) represents a core problem in robotics. In its abstract form, the task is to navigate agents in an undirected graph to individual goal vertices so that conflicts between agents do not occur. Many algorithms for finding feasible or optimal solutions have been devised. We focus on the execution of MAPF solutions with a s...

We address the problem of evacuation from the perspective of agent-based modeling (ABM) in this paper. The evacuation problem is modeled as a navigation of multiple agents that spatially interact with each other in a known environment. The environment is divided into a danger and a safe zone while the task of agents is to move from the danger zone...

We address the problem of evacuation from the heuristic search perspective combined with agent-based modeling (ABM). The evacuation problem is modeled as a navigation of multiple agents in a known environment. The environment is divided into a danger and a safe zone while the task of agents is to move from the danger zone to the safe zone in a coll...

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 CBS, a popular optimal...

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...

Multi-Agent Pathfinding (MAPF) is the problem of finding paths for multiple agents such that each agent reaches its goal and the agents do not collide. In recent years, variants of MAPF have risen in a wide range of real-world applications such as warehouse management and autonomous vehicles. Optimizing common MAPF objectives, such as minimizing su...

In multi-agent path finding (MAPF), the task is to find non-conflicting paths for multiple agents from their initial positions to given individual goal positions. MAPF represents a classical artificial intelligence problem often addressed by heuristic-search. An important alternative to search-based techniques is compilation of MAPF to a different...

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...

Solving cooperative path finding (CPF) by translating it to propositional satisfiability represents a viable option in highly constrained situations. The task in CPF is to relocate agents from their initial positions to given goals in a collision free manner. In this paper, we propose a reduced time expansion that is focused on makespan sub-optimal...

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...

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...

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...

The task in multi-agent path finding (MAPF) is to find non-conflicting paths connecting agents' start and goal positions. The MAPF problem is often compiled to Boolean satisfiability (SAT) and solved by existing SAT solvers. Contemporary compilation approaches of MAPF to SAT regard the SAT solver as an external tool whose task is to return an assig...

We address the design of heuristics for near-optimal solving of the (N2–1)-puzzle using the A* search algorithm in this paper. The A* search algorithm explores configurations of the puzzle in the order determined by a heuristic that tries to estimate the minimum number of moves needed to reach the goal from the given configuration. To guarantee fin...

We present ESO-MAPF, a research and educational platform for experimenting with multi-agent path finding (MAPF). ESO-MAPF focuses on demonstrating the planning-acting chain in the MAPF domain. MAPF is the task of finding collision free paths for agents from their starting positions to given individual goals. The standard MAPF uses the abstraction w...

We introduce multi-goal multi agent path finding (MG-MAPF) which generalizes the standard discrete multi-agent path finding (MAPF) problem. While the task in MAPF is to navigate agents in an undirected graph from their starting vertices to one individual goal vertex per agent, MG-MAPF assigns each agent multiple goal vertices and the task is to vis...

Multi-agent path finding (MAPF) attracts considerable attention in artificial intelligence community as well as in robotics, and other fields such as warehouse logistics. The task in the standard MAPF is to find paths through which agents can navigate from their starting positions to specified individual goal positions. The combination of two addit...

We address multi-agent path finding (MAPF) with continuous movements and geometric agents, i.e. agents of various geometric shapes moving smoothly between predefined positions. We analyze a new solving approach based on satisfiability modulo theories (SMT) that is designed to obtain optimal solutions with respect to common cumulative objectives. Th...

Path planning for multiple robots (MRPP) represents a task of finding non-colliding paths for robots through which they can navigate from their initial positions to specified goal positions. The problem is usually modeled using undirected graphs where robots move between vertices across edges. Contemporary optimal solving algorithms include dedicat...

Multi-agent path finding (MAPF) is the problem of planning a set of non-colliding paths for a set of agents so that each agent reaches its individual goal location following its path. A mutex from classical planning is a constraint forbidding a pair of facts to be both true or a pair of actions to be executed simultaneously. In the context of MAPF,...

We introduce multi-goal multi agent path finding (MAPF$^{MG}$) which generalizes the standard discrete multi-agent path finding (MAPF) problem. While the task in MAPF is to navigate agents in an undirected graph from their starting vertices to one individual goal vertex per agent, MAPF$^{MG}$ assigns each agent multiple goal vertices and the task i...

Multi-agent path finding with continuous movements and time (denoted MAPFR) is addressed. The task is to navigate agents that move smoothly between predefined positions to their individual goals so that they do not collide. Recently a novel solving approach for obtaining makespan optimal solutions called SMT-CBSR based on satisfiability modulo theo...

Multi-agent path finding with continuous movements and time (denoted MAPF) is addressed. The task is to navigate agents that move smoothly between predefined positions to their individual goals so that they do not collide. Recently a novel solving approach for obtaining makespan optimal solutions called SMT-CBS based on satisfiability modulo theori...

The At-Most-One (AMO) constraint is a special case of cardinality constraint that requires at most one variable from a set of Boolean variables to be set to TRUE. AMO is important for modeling problems as Boolean satisfiability (SAT) from domains where decision variables represent spatial or temporal placements of some objects that cannot share the...

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 (...

Multi-agent path finding in continuous space and time with geometric agents MAPF$^\mathcal{R}$ is addressed in this paper. The task is to navigate agents that move smoothly between predefined positions to their individual goals so that they do not collide. We introduce a novel solving approach for obtaining makespan optimal solutions called SMT-CBS...

We address engineering of smart behavior of agents in evacuation problems from the perspective of cooperative path finding (CPF) in this paper. We introduce an abstract version of evacuation problems we call multi-agent evacuation (MAE) that consists of an undirected graph representing the map of the environment and a set of agents moving in this g...

This paper gives an overview of conflict reasoning in generalizations of multi-agent path finding (MAPF). MAPF and derived variants assume items placed in vertices of an undirected graph with at most one item per vertex. Items can be relocated across edges while various constraints depending on the concrete type of relocation problem must be satisf...

In multi-agent path finding (MAPF) the task is to navigate agents from their starting positions to given individual goals. The problem takes place in an undirected graph whose vertices represent positions and edges define the topology. Agents can move to neighbor vertices across edges. In the standard MAPF, space occupation by agents is modeled by...

We unify search-based and compilation-based approaches to multi-agent path finding (MAPF) through satisfiability modulo theories (SMT). The task in MAPF is to navigate agents in an undirected graph to given goal vertices so that they do not collide. We rephrase Conflict-Based Search (CBS), one of the state-of-the-art algorithms for optimal MAPF sol...

In multi-agent path finding (MAPF) the task is to navigate agents from their starting positions to given individual goals. The problem takes place in an undirected graph whose vertices represent positions and edges define the topology. Agents can move to neighbor vertices across edges. In the standard MAPF, space occupation by agents is modeled 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...

We discuss milestones on the tour towards DPLL(MAPF), a multi-agent path finding (MAPF) solver fully integrated with the Davis-Putnam-Logemann-Loveland (DPLL) propositional satisfiability testing algorithm through satisfiability modulo theories (SMT). The task in MAPF is to navigate agents in an undirected graph in a non-colliding way so that each...

This paper addresses a variant of multi-agent path finding (MAPF) in continuous space and time. We present a new solving approach based on satisfiability modulo theories (SMT) to obtain makespan optimal solutions. The standard MAPF is a task of navigating agents in an undirected graph from given starting vertices to given goal vertices so that agen...

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 an objective function. Two such common objective functions is the sum-of-costs and the makespan. Many optimal solvers were introduced in the...

We address item relocation problems in graphs in this paper. We assume items placed in vertices of an undirected graph with at most one item per vertex. Items can be moved across edges while various constraints depending on the type of relocation problem must be satisfied. We introduce a general problem formulation that encompasses known types of i...

We present and evaluate diBOX, an algorithm for multi-agent path finding on strongly biconnected directed graphs. diBOX runs in polynomial time, computes suboptimal solutions and is complete for instances on strongly biconnected digraphs with at least two unoccupied positions. A detailed empirical analysis shows a good scalability for diBOX.

We study practical approaches to solving the token swapping (TSWAP) problem optimally in this short paper. In TSWAP, we are given an undirected graph with colored vertices. A colored token is placed in each vertex. A pair of tokens can be swapped between adjacent vertices. The goal is to perform a sequence of swaps so that token and vertex colors a...

In multi-agent path finding (MAPF) on graphs, the task is to find paths for distinguishable agents so that each agent reaches its unique goal vertex from the given start while collisions between agents are forbidden. A cumulative objective function is often minimized in MAPF. The main contribution of this paper consists in integrating independence...

Much of the literature on suboptimal, polynomial-time algorithms for multi-agent path finding focuses on undirected graphs, where motion is permitted in both directions along a graph edge. Despite this, traveling on directed graphs is relevant in navigation domains, such as path finding in games, and asymmetric communication networks. We consider m...

This paper deals with solving cooperative path finding (CPF) problems in a makespan-optimal way. A feasible solution to the CPF problem lies in the moving of mobile agents where each agent has unique initial and goal positions. The abstraction adopted in CPF assumes that agents are discrete units that move over an undirected graph by traversing its...

The multi-agent pathfinding (MAPF) problem has attracted considerable attention because of its relation to practical applications. In this paper, we present a constraint-based declarative model for MAPF, together with its implementation in Picat, a logic-based programming language. We show experimentally that our Picat-based implementation is highl...

The problem of mesh matching is addressed in this work. For a given n-sided planar region bounded by one loop of n polylines we are selecting optimal quadrilateral mesh from existing catalogue of meshes. The formulation of matching between planar shape and quadrilateral mesh from the catalogue is based on the problem of finding longest common subse...

We address a problem of area protection in graph-based scenarios with multiple mobile agents where connectivity is maintained among agents to ensure they can communicate. The problem consists of two adversarial teams of agents that move in an undirected graph shared by both teams. Agents are placed in vertices of the graph; at most one agent can oc...

We address a problem of area protection in graph-based scenarios with multiple agents. The problem consists of two adversarial teams of agents that move in an undirected graph shared by both teams. Agents are placed in vertices of the graph; at most one agent can occupy a vertex; and they can move into adjacent vertices in a conflict free way. Team...

In multi-agent path finding (MAPF) the task is to find non-conflicting 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...

In multi-agent path finding (MAPF) the task is to find non-conflicting 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...

The problem of makespan optimal solving of cooperative path finding (CPF) is addressed in this paper. The task in CPF is to relocate a group of agents in a non-colliding way so that each agent eventually reaches its goal location from the given initial location. The abstraction adopted in this work assumes that agents are discrete items moving in a...

The problem of solving $(n^2-1)$-puzzle and cooperative path-finding (CPF) sub-optimally by rule based algorithms is addressed in this manuscript. The task in the puzzle is to rearrange $n^2-1$ pebbles on the square grid of the size of n x n using one vacant position to a desired goal configuration. An improvement to the existent polynomial-time al...

The present paper deals with the problem of solving the (\(n^2 - 1\))-puzzle and cooperative path-finding (CPF) problems sub-optimally by rule-based algorithms. To solve the puzzle, we need to rearrange \(n^2 - 1\) pebbles in the \(n \times n\)-sized square grid using one vacant position to achieve the goal configuration. An improvement to the exis...

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...

This paper focuses on finding optimal solutions to the multi-agent path finding (MAPF) problem over undirected graphs where the task is to find non-colliding paths for multiple agents, each with a different start and goal position. An encoding of MAPF to Boolean satisfiability (SAT) is already known to the makespan optimal variant of the problem. I...

Solving cooperative path finding (CPF) by translating it to propositional satisfiability represents a viable option in highly constrained situations. The task in CPF is to relocate agents from their initial positions to given goals in a collision free manner. In this paper, we propose a reduced time expansion that is focused on makespan sub-optimal...

A novel eager encoding of the ALLDIFFERENT constraint over bit-vectors is presented in this short paper. It is based on 1-to-1 mapping of the input bit-vectors to a linearly ordered set of auxiliary bit-vectors. Experiments with four SAT solvers showed that the new encoding could be solved order of magnitudes faster than the standard encoding in a...

Much of the literature on multi-agent path finding focuses on undirected graphs, where motion is permitted in both directions along a graph edge. Despite this, travelling on directed graphs is relevant in navigation domains, such as pathfinding in games, and asymmetric communication networks. We consider multi-agent path finding on strongly biconne...

Much of the literature on multi-agent path finding focuses on undirected graphs, where motion is permitted in both directions along a graph edge. Despite this, travelling on directed graphs is relevant in navigation domains, such as pathfinding in games, and asymmetric communication networks. We consider multi-agent path finding on strongly biconne...

A parallel version of the problem of cooperative path-finding (pCPF) is introduced in this paper. The task in CPF is to determine a spatio-temporal plan for each member of a group of agents. Each agent is given its initial location in the environment and its task is to reach the given goal location. Agents must avoid obstacles and must not collide...

This paper addresses makespan optimal solving of cooperative path-finding problem (CPF) by translating it to propositional satisfiability (SAT). A novel very simple SAT encoding of CPF is proposed and compared with existing elaborate encodings. The conducted experimental evaluation shown that the simple design of the encoding allows solving it fast...

This paper addresses makespan optimal solving of cooperative pathfinding problem (CPF) by translating it to propositional satisfiability (SAT). The task in CPF is to relocate a set of agents to given goal locations so that they do not collide with each other. Recent findings indicate that a simple direct encoding outperforms the more elaborate enco...