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STRIPS Action Discovery

Author
Suarez, A.; Segovia , J.; Torras, C.; Alenyà, G.
Type of activity
Report
Date
2020-01-30
URL
https://arxiv.org/abs/2001.11457 Open in new window
Abstract
The problem of specifying high-level knowledge bases for planning becomes a hard task in realistic environments. This knowledge is usually handcrafted and is hard to keep updated, even for system experts. Recent approaches have shown the success of classical planning at synthesizing action models even when all intermediate states are missing. These approaches can synthesize action schemas in Planning Domain Definition Language (PDDL) from a set of execution traces each consisting, at least, of a...
Keywords
Planning, SAT, STRIPS learning
Group of research
ROBiri - IRI Robotics Group

Participants