Search Results for author:
Found 21 papers, 6 papers with code
Enhancing Lifelong Multi-Agent Path Finding with Cache Mechanism
In this paper, we introduce a novel mechanism called Lifelong MAPF with Cache Mechanism (L-MAPF-CM), which integrates high-level cache storage with low-level path planning.
Caching-Augmented Lifelong Multi-Agent Path Finding
In this paper, we present a novel mechanism named Caching-Augmented Lifelong MAPF (CAL-MAPF), designed to improve the performance of Lifelong MAPF.
FastMapSVM: Classifying Complex Objects Using the FastMap Algorithm and Support-Vector Machines
In this paper, we advance FastMapSVM -- an interpretable Machine Learning framework for classifying complex objects -- as an advantageous alternative to Neural Networks for general classification tasks.
Embedding Directed Graphs in Potential Fields Using FastMap-D
FastMap is an efficient embedding algorithm that facilitates a geometric interpretation of problems posed on undirected graphs.
Lifelong Multi-Agent Path Finding in Large-Scale Warehouses
Multi-Agent Path Finding (MAPF) is the problem of moving a team of agents to their goal locations without collisions.
Idle Time Optimization for Target Assignment and Path Finding in Sortation Centers
In this paper, we study the one-shot and lifelong versions of the Target Assignment and Path Finding problem in automated sortation centers, where each agent needs to constantly assign itself a sorting station, move to its assigned station without colliding with obstacles or other agents, wait in the queue of that station to obtain a parcel for delivery, and then deliver the parcel to a sorting bin.
Multi-Agent Path Finding with Capacity Constraints
Agents can move to neighbor vertices across edges.
Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks
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.
Lifelong Path Planning with Kinematic Constraints for Multi-Agent Pickup and Delivery
For example, we demonstrate that it can compute paths for hundreds of agents and thousands of tasks in seconds and is more efficient and effective than existing MAPD algorithms that use a post-processing step to adapt their paths to continuous agent movements with given velocities.
Multi-Agent Path Finding with Deadlines
We formalize Multi-Agent Path Finding with Deadlines (MAPF-DL).
Multi-Agent Path Finding with Deadlines: Preliminary Results
We formalize the problem of multi-agent path finding with deadlines (MAPF-DL).
Overview: A Hierarchical Framework for Plan Generation and Execution in Multi-Robot Systems
In the plan-generation phase, the framework provides a computationally scalable method for generating plans that achieve high-level tasks for groups of robots and take some of their kinematic constraints into account.
Measuring Territorial Control in Civil Wars Using Hidden Markov Models: A Data Informatics-Based Approach
Territorial control is a key aspect shaping the dynamics of civil war.
Feasibility Study: Moving Non-Homogeneous Teams in Congested Video Game Environments
Multi-agent path finding (MAPF) is a well-studied problem in artificial intelligence, where one needs to find collision-free paths for agents with given start and goal locations.
Rapid Randomized Restarts for Multi-Agent Path Finding Solvers
Multi-Agent Path Finding (MAPF) is an NP-hard problem well studied in artificial intelligence and robotics.
The FastMap Algorithm for Shortest Path Computations
We present a new preprocessing algorithm for embedding the nodes of a given edge-weighted undirected graph into a Euclidean space.
Lifelong Multi-Agent Path Finding for Online Pickup and Delivery Tasks
In the MAPD problem, agents have to attend to a stream of delivery tasks in an online setting.
Path Planning with Kinematic Constraints for Robot Groups
Path planning for multiple robots is well studied in the AI and robotics communities.
Overview: Generalizations of Multi-Agent Path Finding to Real-World Scenarios
Multi-agent path finding (MAPF) is well-studied in artificial intelligence, robotics, theoretical computer science and operations research.
Multi-Agent Path Finding with Delay Probabilities
Several recently developed Multi-Agent Path Finding (MAPF) solvers scale to large MAPF instances by searching for MAPF plans on 2 levels: The high-level search resolves collisions between agents, and the low-level search plans paths for single agents under the constraints imposed by the high-level search.
Compliant Conditions for Polynomial Time Approximation of Operator Counts
In this paper, we develop a computationally simpler version of the operator count heuristic for a particular class of domains.
