scholarly journals Multi-modal Multi-agent Path Finding with Optimal Resource Utilization

2020 ◽  
pp. 313-324 ◽  
Author(s):  
Aysu Bogatarkan ◽  
Esra Erdem ◽  
Alexander Kleiner ◽  
Volkan Patoglu
Keyword(s):  
Resource Utilization ◽  
Path Finding ◽  
Optimal Resource ◽  
Multi Agent

scholarly journals Explanation Generation for Multi-Modal Multi-Agent Path Finding with Optimal Resource Utilization using Answer Set Programming

2020 ◽  
Vol 20 (6) ◽  
pp. 974-989
Author(s):  
AYSU BOGATARKAN ◽  
ESRA ERDEM
Keyword(s):  
Answer Set Programming ◽  
Search Problem ◽  
Path Finding ◽  
Combinatorial Search ◽  
Transportation Modes ◽  
Optimal Resource ◽  
Multi Agent ◽  
Nonexistence Of Solutions ◽  
Velocity Constraints ◽  
Answer Set

AbstractThe multi-agent path finding (MAPF) problem is a combinatorial search problem that aims at finding paths for multiple agents (e.g., robots) in an environment (e.g., an autonomous warehouse) such that no two agents collide with each other, and subject to some constraints on the lengths of paths. We consider a general version of MAPF, called mMAPF, that involves multi-modal transportation modes (e.g., due to velocity constraints) and consumption of different types of resources (e.g., batteries). The real-world applications of mMAPF require flexibility (e.g., solving variations of mMAPF) as well as explainability. Our earlier studies on mMAPF have focused on the former challenge of flexibility. In this study, we focus on the latter challenge of explainability, and introduce a method for generating explanations for queries regarding the feasibility and optimality of solutions, the nonexistence of solutions, and the observations about solutions. Our method is based on answer set programming.

scholarly journals Multi-Agent Path Finding with Deadlines

2018 ◽  
Author(s):  
Hang Ma ◽  
Glenn Wagner ◽  
Ariel Felner ◽  
Jiaoyang Li ◽  
T. K. Satish Kumar ◽  
...  
Keyword(s):  
Flow Problem ◽  
Search Algorithms ◽  
The Other ◽  
Path Finding ◽  
Combinatorial Search ◽  
Programming Formulation ◽  
Commodity Flow ◽  
Linear Programming Formulation ◽  
Multi Agent ◽  
Integer Linear Programming Formulation

We formalize Multi-Agent Path Finding with Deadlines (MAPF-DL). The objective is to maximize the number of agents that can reach their given goal vertices from their given start vertices within the deadline, without colliding with each other. We first show that MAPF-DL is NP-hard to solve optimally. We then present two classes of optimal algorithms, one based on a reduction of MAPF-DL to a flow problem and a subsequent compact integer linear programming formulation of the resulting reduced abstracted multi-commodity flow network and the other one based on novel combinatorial search algorithms. Our empirical results demonstrate that these MAPF-DL solvers scale well and each one dominates the other ones in different scenarios.

Adversarial Multi-Agent Path Finding is Intractable

2021 ◽  
Author(s):  
Marika Ivanova ◽  
Pavel Surynek
Keyword(s):  
Path Finding ◽  
Multi Agent

Hierarchical Load Balancing Model by Optimal Resource Utilization

2021 ◽  
pp. 150-164
Author(s):  
Jagdish Chandra Patni
Keyword(s):  
Load Balancing ◽  
Grid Computing ◽  
Resource Utilization ◽  
Resource Availability ◽  
High Performance ◽  
Low Cost ◽  
Original Form ◽  
Computing Paradigm ◽  
Optimal Resource ◽  
Performance Computing

Powerful computational capabilities and resource availability at a low cost is the utmost demand for high performance computing. The resources for computing can viewed as the edges of an interconnected grid. It can attain the capabilities of grid computing by balancing the load at various levels. Since the nature of resources are heterogeneous and distributed geographically, the grid computing paradigm in its original form cannot be used to meet the requirements, so it can use the capabilities of the cloud and other technologies to achieve the goal. Resource heterogeneity makes grid computing more dynamic and challenging. Therefore, in this article the problem of scalability, heterogeneity and adaptability of grid computing is discussed with a perspective of providing high computing, load balancing and availability of resources.

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