SHOP2: An HTN Planning System

By T. Au, O. Ilghami, U. Kuter, J. William Murdock, Dana S. Nau, D. Wu, F. Yaman. Journal of Artificial Intelligence Research, 2003

1,204 citations recorded by the source corpus.

The SHOP2 planning system received one of the awards for distinguished performance in the 2002 International Planning Competition. This paper describes the features of SHOP2 which enabled it to excel in the competition, especially those aspects of SHOP2 that deal with temporal and metric planning domains.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/6d21ebdd3339ff8617e04d498eca7084a3edfc70

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Citations in the corpus

Hierarchical task and motion planning in the now (2011, background): “[13] provides a hierarchical combined task and motion planner based on hierarchical transition networks (HTNs) [14] and applies it to a manipulation-planning problem.”
Learning action models from plan examples using weighted MAX-SAT (2007, methodology): “These schemata are also known as methods [32,33].”
Learning Probabilistic Hierarchical Task Networks as Probabilistic Context-Free Grammars to Capture User Preferences (2014, methodology): “This comes at a price: HTNs, even just SHOP [Nau et al. 2003], are normally richer languages for representing constraints over plans.”
Autonomously constructing hierarchical task networks for planning and human-robot collaboration (2016, background): “This highly effective approach consists of identifying abstractions present within collections of primitive skills or subtasks to reduce planning difficulty [15].”
Bringing Semantics to Web Services: The OWL-S Approach (2004, background): “, [23] [24]), which is a state-of-the-art HTN”
Bringing Semantics to Web Services with OWL-S (2007, methodology): “In a similar spirit, other researchers [39, 80] have used the paradigm of Hierarchical Task Network (HTN) planning [58] to perform automated Web Service composition.”
Reasoning with Language Model is Planning with World Model (2023, background): “Classical planning methods have been widely adopted in robots and embodied environments [9, 42, 8, 61, 26].”
Broadly-Exploring, Local-Policy Trees for Long-Horizon Task Planning (2020, methodology): “These methods then plan over them, for example, via trees or optimization-based methods [11, 12, 13, 14, 15, 16, 17].”
Automated Planning and Acting (2016, background): “On one hand, constraining the search in this manner can convey a substantial efficiency advantage [445].”
ML-Plan for Unlimited-Length Machine Learning Pipelines (2018, background): “Standard hierarchical planners such as SHOP2 (Au et al., 2011) lack some fundamental requirements of AutoML, e.”