Pomdp Solvers
POMDP Solver is a computational method designed to determine optimal policies for decision-making under uncertainty in partially observable environments by solving the underlying partially observable Markov decision process model through exact or approximate inference techniques.
204
Publications
13.8K
Citations
391
Authors
165
Institutions
Publications per year
2017–2026
56
Variants
Partially Observable Markov Decision Process Solvers
204
Monte-Carlo Planning in Large POMDPs
David Silver, Joel Veness · DSpace@MIT (Massachusetts Institute of Technology) · 2010 · 856 citations · Full text
A survey of point-based POMDP solvers
Guy Shani, Joëlle Pineau, Robert Kaplow · Autonomous Agents and Multi-Agent Systems · 2012 · 433 citations
1–5 of 204
391
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165
National University of Singapore
Singapore, Singapore
13 h-index
39 publications
4.3K citations
Carnegie Mellon University
Pittsburgh, United States
11 h-index
25 publications
3.9K citations

University of Massachusetts Amherst
Amherst Center, United States
11 h-index
18 publications
1.7K citations
Ben-Gurion University of the Negev
Beersheba, Israel
10 h-index
20 publications
1K citations
Stanford University
Stanford, United States
9 h-index
17 publications
2.3K citations
Massachusetts Institute of Technology
Cambridge, United States
7 h-index
19 publications
1.2K citations

University of Waterloo
Waterloo, Canada
6 h-index
7 publications
315 citations
Korea Advanced Institute of Science and Technology
Daejeon, South Korea
5 h-index
13 publications
573 citations
1–8 of 165
Conference · 13 publications · 414 citations
12
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Repository · 13 publications · 1K citations
11
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7 publications · 311 citations
7
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IJRR · 5 publications · 509 citations
5
h-index
5 publications · 585 citations
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