Search Results for author: Liron Cohen

Found 12 papers, 3 papers with code

Uncertainty Estimation based on Geometric Separation

no code implementations11 Jan 2023 Gabriella Chouraqui, Liron Cohen, Gil Einziger, Liel Leman

In machine learning, accurately predicting the probability that a specific input is correct is crucial for risk management.

Autonomous Driving Management

A Geometric Method for Improved Uncertainty Estimation in Real-time

1 code implementation23 Jun 2022 Gabriella Chouraqui, Liron Cohen, Gil Einziger, Liel Leman

Predicting the probability of a specific input to be correct is called uncertainty (or confidence) estimation and is crucial for risk management.

Management

Embedding Directed Graphs in Potential Fields Using FastMap-D

1 code implementation4 Jun 2020 Sriram Gopalakrishnan, Liron Cohen, Sven Koenig, T. K. Satish Kumar

FastMap is an efficient embedding algorithm that facilitates a geometric interpretation of problems posed on undirected graphs.

Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks

1 code implementation19 Jun 2019 Roni Stern, Nathan Sturtevant, Ariel Felner, Sven Koenig, Hang Ma, Thayne Walker, Jiaoyang Li, Dor Atzmon, Liron Cohen, T. K. Satish Kumar, Eli Boyarski, Roman Bartak

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.

Autonomous Vehicles

Position Paper: From Multi-Agent Pathfinding to Pipe Routing

no code implementations21 May 2019 Gleb Belov, Liron Cohen, Maria Garcia de la Banda, Daniel Harabor, Sven Koenig, Xinrui Wei

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.

Multi-Agent Path Finding Robot Navigation

Overview: A Hierarchical Framework for Plan Generation and Execution in Multi-Robot Systems

no code implementations30 Mar 2018 Hang Ma, Wolfgang Hönig, Liron Cohen, Tansel Uras, Hong Xu, T. K. Satish Kumar, Nora Ayanian, Sven Koenig

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

no code implementations4 Oct 2017 Hang Ma, Jingxing Yang, Liron Cohen, T. K. Satish Kumar, Sven Koenig

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.

Multi-Agent Path Finding

Rapid Randomized Restarts for Multi-Agent Path Finding Solvers

no code implementations8 Jun 2017 Liron Cohen, Glenn Wagner, T. K. Satish Kumar, Howie Choset, Sven Koenig

Multi-Agent Path Finding (MAPF) is an NP-hard problem well studied in artificial intelligence and robotics.

Multi-Agent Path Finding

The FastMap Algorithm for Shortest Path Computations

no code implementations8 Jun 2017 Liron Cohen, Tansel Uras, Shiva Jahangiri, Aliyah Arunasalam, Sven Koenig, T. K. Satish Kumar

We present a new preprocessing algorithm for embedding the nodes of a given edge-weighted undirected graph into a Euclidean space.

Path Planning with Kinematic Constraints for Robot Groups

no code implementations25 Apr 2017 Wolfgang Hönig, T. K. Satish Kumar, Liron Cohen, Hang Ma, Sven Koenig, Nora Ayanian

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

no code implementations17 Feb 2017 Hang Ma, Sven Koenig, Nora Ayanian, Liron Cohen, Wolfgang Hoenig, T. K. Satish Kumar, Tansel Uras, Hong Xu, Craig Tovey, Guni Sharon

Multi-agent path finding (MAPF) is well-studied in artificial intelligence, robotics, theoretical computer science and operations research.

Multi-Agent Path Finding

Learning and Optimization with Submodular Functions

no code implementations7 May 2015 Bharath Sankaran, Marjan Ghazvininejad, Xinran He, David Kale, Liron Cohen

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.

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