PDDL+ Planning with Hybrid Automata: Foundations of Translating Must Behavior
By Sergiy Bogomolov, Daniele Magazzeni, Stefano Minopoli, Martin Wehrle. 2015
42 citations recorded by the source corpus.
Planning in hybrid domains poses a special challenge due to the involved mixed discrete-continuous dynamics. A recent solving approach for such domains is based on applying model checking techniques on a translation of PDDL+ planning problems to hybrid automata. However, the proposed translation is limited because must behavior is only over-approximated, and hence, processes and events are not reflected exactly. In this paper, we present the theoretical foundation of an exact PDDL+ translation. We propose a schema to convert a hybrid automaton with must transitions into an equivalent hybrid automaton featuring only may transitions.
Subfields: Artificial Intelligence, Computational Theory and Mathematics, Software
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Derek Long
- Maria Fox
- Daniele Magazzeni
- Paolo Traverso
- Malik Ghallab
- Dana S. Nau
- Subbarao Kambhampati
- Been Kim
In Computational Theory and Mathematics
- Derek Long
- Maria Fox
- Paolo Traverso
- Dana S. Nau
- Daniele Magazzeni
- Malik Ghallab
- Giulia Vilone
- Longo Luca
In Software
Citations in the corpus
- Explainable Planning (2017, methodology): “Recently, this paradigm has been applied to complex planning problems with temporal and numeric features [Bogomolov et al., 2014; Bogomolov et al., 2015].”
- Automated Planning and Acting (2016, background): “[78, 77] exploit the close relationship of the PDDL+ semantics with hybrid automata.”