State Abstraction
State Abstraction is a methodological construct in artificial intelligence that selectively retains relevant features of a state space while discarding irrelevant details to improve computational efficiency and generalization in decision-making processes.
158
Publications
8.1K
Citations
410
Authors
138
Institutions
Publications per year
2017–2026
49
Parents
158
Liren Chen, Katia Sycara · 1998 · 458 citations
State abstraction for programmable reinforcement learning agents
David André, Stuart Russell · 2002 · 214 citations
Discovering Hierarchy in Reinforcement Learning with HEXQ
Bernhard Hengst · 2002 · 184 citations
Computing, Mathematics, Algorithms +16
1–5 of 158
410
Rutgers, The State University of New Jersey, United States
7 publications · 532 citations
7
h-index
7
h-index
5
h-index
5
h-index
3
h-index
1–5 of 410
138
Carnegie Mellon University
Pittsburgh, United States
12 h-index
28 publications
2.8K citations

University of Alberta
Edmonton, Canada
11 h-index
29 publications
1.4K citations
University of California, Berkeley
Berkeley, United States
8 h-index
19 publications
860 citations
Brown University
Providence, United States
5 h-index
11 publications
743 citations

University of Massachusetts Amherst
Amherst Center, United States
5 h-index
8 publications
577 citations
Oregon State University
Corvallis, United States
5 h-index
7 publications
1.6K citations
The University of Texas at Austin
Austin, United States
5 h-index
13 publications
488 citations
Stanford University
Stanford, United States
5 h-index
7 publications
130 citations
1–8 of 138
Repository · 8 publications · 243 citations
8
h-index
Conference · 7 publications · 231 citations
7
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JAIR · 6 publications · 1.7K citations
6
h-index
5 publications · 241 citations
5
h-index
4 publications · 131 citations
4
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1–5