Probabilistic Inference Techniques for Scalable Multiagent Decision Making
Decentralized POMDPs provide an expressive framework for multiagent sequential decision making. However, the complexity of these models---NEXP-Complete even for two agents---has limited their scalability. We present a promising new class of approximation algorithms by developing novel connections be...
Saved in:
Main Authors: | Akshat KUMAR, ZILBERSTEIN, Shlomo, TOUSSAINT, Marc |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2015
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/3076 https://ink.library.smu.edu.sg/context/sis_research/article/4076/viewcontent/10944_Article_Text_20419_1_10_20180216.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
Similar Items
-
Scalable Multiagent Planning using Probabilistic Inference
by: KUMAR, Akshat, et al.
Published: (2011) -
Lagrangian Relaxation Techniques for Scalable Spatial Conservation Planning
by: KUMAR, Akshat, et al.
Published: (2012) -
Collective multiagent sequential decision making under uncertainty
by: NGUYEN, Duc Thien, et al.
Published: (2017) -
Multiagent decision making for maritime traffic management
by: SINGH, Arambam James, et al.
Published: (2019) -
Multiagent decision making for maritime traffic management
by: ARAMBAM JAMES SINGH,, et al.
Published: (2019)