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Found 12 papers, 3 papers with code
Uncertainty Estimation based on Geometric Separation
In machine learning, accurately predicting the probability that a specific input is correct is crucial for risk management.
A Geometric Method for Improved Uncertainty Estimation in Real-time
Predicting the probability of a specific input to be correct is called uncertainty (or confidence) estimation and is crucial for risk management.
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.
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.
Position Paper: From Multi-Agent Pathfinding to Pipe Routing
The 2D Multi-Agent Path Finding (MAPF) problem aims at finding collision-free paths for a number of agents, from a set of start locations to a set of goal positions in a known 2D environment.
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.
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.
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.
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.
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.
Learning and Optimization with Submodular Functions
Set functions, and specifically submodular set functions, characterize a wide variety of naturally occurring optimization problems, and the property of submodularity of set functions has deep theoretical consequences with wide ranging applications.
