Articles | Sciaria - Science Aria

Heuristic search solvers like RTDP-Bel and LAO* have proven effective for computing optimal and bounded sub-optimal solutions for Partially Observable...

Review:

This paper addresses a critical practical limitation in solving Partially Observable Markov Decision Processes (POMDPs) using heuristic search methods...

View Full Research

Keisuke Okumura, Hiroki Nagai

Lightweight and Effective Preference Construction in PIBT for Large-Scale Multi-Agent Pathfinding

Robotics

PIBT is a computationally lightweight algorithm that can be applied to a variety of multi-agent pathfinding (MAPF) problems, generating the next colli...

Review:

This paper presents a valuable contribution to the field of multi-agent pathfinding (MAPF), specifically focusing on improving the performance of PIBT...

View Full Research

Ankur Nath, Alan Kuhnle

Hierarchical DeepPruner: A Novel Framework for Search Space Reduction

Informatics

Combinatorial optimization (CO) problems on graphs arise in various applications across diverse domains. Many of these problems are NP-hard, and heuri...

Review:

This paper introduces Hierarchical DeepPruner, a novel framework designed to tackle the significant computational challenges posed by NP-hard Combinat...

View Full Research

Edward Lam, Peter J. Stuckey

Low-Level Search on Time Intervals in Branch-and-Cut-and-Price for Multi-Agent Path Finding

Informatics

Multi-agent path finding is the problem of navigating a set of agents from their starting locations to their target locations while avoiding collision...

Review:

This paper addresses the challenging problem of Multi-Agent Path Finding (MAPF), a crucial area in robotics and AI where multiple agents must navigate...

View Full Research

Daniel Koyfman, Dor Atzmon, Shahaf Shperberg, Ariel Felner

Minimizing Fuel in Multi-Agent Pathfinding

Informatics

The multi-agent pathfinding problem (MAPF) of finding conflict-free paths for multiple agents has attracted a large number of researchers in the past....

Review:

The paper "Minimizing Fuel in Multi-Agent Pathfinding" presents a compelling and timely contribution to the Multi-Agent Pathfinding (MAPF) domain by s...

View Full Research

Weimin Huang, Natalie M. Isenberg, Ján Drgoňa, Draguna L Vrabie, Bistra Dilkina

Efficient Primal Heuristics for Mixed Binary Quadratic Programs Using Suboptimal Rounding Guidance

Informatics

Mixed Binary Quadratic Programs (MBQPs) are a class of NP-hard problems that arise in a wide range of applications, including finance, machine learnin...

Review:

This paper addresses the challenging problem of solving Mixed Binary Quadratic Programs (MBQPs), which are known to be NP-hard and arise in diverse hi...

View Full Research

Yaron Halle, Ariel Felner, Sven Koenig, Oren Salzman

A Preprocessing Framework for Efficient Approximate Bi-Objective Shortest-Path Computation in the Presence of Correlated Objectives

Informatics

The bi-objective shortest-path (BOSP) problem seeks to find paths between start and target vertices of a graph while optimizing two conflicting object...

Review:

The paper presents a valuable contribution to the field of multi-objective optimization by addressing the bi-objective shortest-path (BOSP) problem, s...

View Full Research

Garrett M. Fereday, Eric A. Hansen

A Bucket-Based Priority Queue for Bounded-Suboptimal and Anytime A* Search

Informatics

We introduce a priority queue data structure, called a bucket heap, which generalizes the bucket queue commonly used to accelerate A* search for short...

Review:

The paper introduces a novel data structure, the "bucket heap," designed to significantly enhance the performance of A* search algorithms. Positioned...

View Full Research

Shao-Hung Chan, Thomy Phan, Jiaoyang Li, Sven Koenig

New Mechanisms in Flex Distribution for Bounded Suboptimal Multi-Agent Path Finding

Informatics

Multi-Agent Path Finding (MAPF) is the problem of finding a set of collision-free paths, one for each agent in a shared environment. Its objective is...

Review:

This paper presents a significant advancement in the efficiency of Explicit Estimation Conflict-Based Search (EECBS), a leading algorithm for bounded-...

Review:

This paper presents a significant advancement in grid path planning, directly addressing the common trade-off between search speed and solution qualit...