Concise finite-domain representations for PDDL planning tasks

By Malte Helmert. Artificial Intelligence, 2009

367 citations recorded by the source corpus.

We introduce an efficient method for translating planning tasks specified in the standard PDDL formalism into a concise grounded representation that uses finite-domain state variables instead of the straight-forward propositional encoding. Translation is performed in four stages. Firstly, we transform the input task into an equivalent normal form expressed in a restricted fragment of PDDL. Secondly, we synthesize invariants of the planning task that identify groups of mutually exclusive propositions which can be represented by a single finite-domain variable. Thirdly, we perform an efficient relaxed reachability analysis using logic programming techniques to obtain a grounded representation of the input. Finally, we combine the results of the third and fourth stage to generate the final grounded finite-domain representation. The presented approach has originally been implemented as part of the Fast Downward planning system for the 4th International Planning Competition (IPC4). Since then, it has been used in a number of other contexts with considerable success, and the use of concise finite-domain representations has become a common feature of state-of-the-art planners.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/d7d0a9262197321c94e4a2448b4844d7345c1ef7

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

1. Paolo Traverso
2. Malik Ghallab
3. Dana S. Nau
4. Silvia Richter
5. F. Ingrand
6. Matthias Westphal
7. Jussi Rintanen

Citations in the corpus

• Automated Planning and Acting (2016, background): “19 The literature contains several examples of cases in which the problem representation and the computation of heuristic functions can be done more easily with state variables than with propositions [268, 510].”
• The LAMA Planner: Guiding Cost-Based Anytime Planning with Landmarks (2010, methodology): “We use a planning formalism with state variables of finite (rather than binary) range, similar to the one employed by Helmert (2009).”
• Learning Generalized Reactive Policies using Deep Neural Networks (2017, neutral): “We assume the reader is familiar with the formalization of deterministic, fully observable planning domains and planning problems in languages such as PDDL (Fox and Long 2003; Helmert 2009) and present the most relevant concepts here.”
• Deliberation for autonomous robots: A survey (2017, background): “Several authors have synthesized from the specification of planning domain state-reachability conditions to speedup planning [185, 102, 21].”