2021 IEEE Intl Conf on Parallel &Amp; Distributed Processing With Applications, Big Data &Amp; Cloud Computing, Sustainable Com 2021
DOI: 10.1109/ispa-bdcloud-socialcom-sustaincom52081.2021.00102
|View full text |Cite
|
Sign up to set email alerts
|

Accelerating Message Passing Operation of GDL-Based Constraint Optimization Algorithms Using Multiprocessing

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2023
2023
2023
2023

Publication Types

Select...
1

Relationship

0
1

Authors

Journals

citations
Cited by 1 publication
(1 citation statement)
references
References 13 publications
0
1
0
Order By: Relevance
“…Since its incep�on in 2019, the Cogni�ve Agents and Interac�on Lab (CAIL) at the University of Dhaka has become an ac�ve contributor to the field of ar�ficial intelligence (AI). With a star�ng focus on Distributed Constraint Op�miza�on Problems [1] [2] [3], CAIL has grown to be recognized for its role in advancing distributed problem-solving for mul�-agent systems [4]. As the interests of our researchers have evolved, our team later started exploring other compelling areas, including Mul�-Agent Path Finding [5], mul�-modal recommender systems [6] [7], causal inference [8], deep reinforcement learning [9] [10], Graph Neural Networks [11], and others [12] [13].…”
Section: Introductionmentioning
confidence: 99%
“…Since its incep�on in 2019, the Cogni�ve Agents and Interac�on Lab (CAIL) at the University of Dhaka has become an ac�ve contributor to the field of ar�ficial intelligence (AI). With a star�ng focus on Distributed Constraint Op�miza�on Problems [1] [2] [3], CAIL has grown to be recognized for its role in advancing distributed problem-solving for mul�-agent systems [4]. As the interests of our researchers have evolved, our team later started exploring other compelling areas, including Mul�-Agent Path Finding [5], mul�-modal recommender systems [6] [7], causal inference [8], deep reinforcement learning [9] [10], Graph Neural Networks [11], and others [12] [13].…”
Section: Introductionmentioning
confidence: 99%