Action Schema Learning
Action Schema Learning is a computational framework within the domain of cognitive robotics and artificial intelligence that enables an agent to acquire, refine, and generalize hierarchical action representations by inferring structured rules—comprising preconditions, effects, and procedural constraints—from sequences of observed or experienced interactions with an environment.
30
Publications
1.6K
Citations
72
Authors
41
Institutions
Publications per year
2017–2026
8
Parents
30
Picture memory: How the action schema affects retention
Gail S. Goodman · Cognitive Psychology · 1980 · 130 citations
1–5 of 30
72
3
h-index
3
h-index
3
h-index
3
h-index
2
h-index
1–5 of 72
41

Australian National University
Canberra, Australia
3 h-index
11 publications
326 citations
University of California, Berkeley
Berkeley, United States
2 h-index
2 publications
30 citations
University of Southern California
Los Angeles, United States
2 h-index
5 publications
129 citations

University of Huddersfield
Huddersfield, United Kingdom
2 h-index
5 publications
327 citations
Rutgers, The State University of New Jersey
New Brunswick, United States
2 h-index
3 publications
136 citations

University of Massachusetts Amherst
Amherst Center, United States
2 h-index
3 publications
58 citations

Mercer University
Macon, United States
1 h-index
1 publications
15 citations
University of Oklahoma
Norman, United States
1 h-index
1 publications
18 citations
1–8 of 41
Repository · 2 publications · 41 citations
2
h-index
Repository · 1 publications · 20 citations
1
h-index
1 publications · 47 citations
1
h-index
1 publications · 42 citations
1
h-index
1 publications · 12 citations
1
h-index
1–5