Model Recognition as Planning
By Diego Aineto, Sergio Jiménez, Eva Onaindia, Miquel Ramírez. 2019
20 citations recorded by the source corpus.
Given a partially observed plan execution, and a set of possible planning models (models that share the same state variables but different action schemata), model recognition is the task of identifying the model that explains the observation. The paper formalizes this task and introduces a novel method that estimates the probability of a STRIPS model to produce an observation of a plan execution. This method builds on top of off-the-shelf classical planning algorithms and it is robust to missing actions and intermediate states in the observation. The effectiveness of the method is tested in three experiments, each encoding a set of different STRIPS models and all using empty-action observations: (1) a classical string classification task; (2) identification of the model that encodes a failure present in an observation; and (3) recognition of a robot navigation policy.
Subfields: Artificial Intelligence
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In Artificial Intelligence
- Blai Bonet
- Hector Geffner
- Malte Helmert
- Jussi Rintanen
- Carmel Domshlak
- Leslie Pack Kaelbling
- Tomas Lozano-Perez
- Malik Ghallab
Citations in the corpus
- Learning First-Order Symbolic Representations for Planning from the Structure of the State Space (2019, background): “Object-oriented MDPs [13] and similar work in classical planning [37, 2, 1], build first-order model representations but starting with a first-order symbolic language, or with information about the actions and their arguments [11].”