Acquiring planning domain models using LOCM

By S. Cresswell, T. McCluskey, M. West. 2013

143 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Learning First-Order Symbolic Representations for Planning from the Structure of the State Space (2019, background): “2 Related Research 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].”
Model Recognition as Planning (2019, result): “While most approaches can learn from empty or partially speciﬁed models (Yang, Wu, and Jiang 2007; Amir and Chang 2008; Cresswell, McCluskey, and West 2013), model recognition requires the ability of editing a possibly inconsistent model M so that the resulting model M (cid:48) is consistent with a…”
Robust planning with incomplete domain models (2017, background): “Similarly, although there exist efforts (Amir and Chang, 2008; Yang et al., 2007; Zhuo and Yang, 2014; Cresswell et al., 2013) that attempt to either learn models from scratch or revise existing ones, their operation is contingent on the availability of successful plan traces or access to execution ”
Unsupervised Grounding of Plannable First-Order Logic Representation from Images (2019, methodology): “Extending or leveraging the existing Action Model Acquisition methods (e.g., AMA 1 , AMA 2 , Action Learner (Amado et al. 2018), (Yang, Wu, and Jiang 2007; Mour˜ao et al. 2012; Cresswell, McCluskey, and West 2013)) is an important avenue for future work.”
Maintaining Evolving Domain Models (2016, background): “Knowledge engineering for planning (Cresswell et al., 2013, Vaquero et al., 2013b, Yang et al., 2007) can be greatly enhanced with systems like Marshal.”
Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning (2023, unknown): “In classical planning, the community has explored numerous learning-based methods [59, 61, 9, 25, 4] and interactive editor-based methods [49] for acquiring symbolic domain models.”
A review of learning planning action models (2018, methodology): “LOCM2 (Cresswell & Gregory (2011)) is generalized and heuristic in nature to allow multiple state machines to represent a single object.”