Deterministic planning in the fifth international planning competition: PDDL3 and experimental evaluation of the planners

By Alfonso Emilio Gerevini, Patrik Haslum, Derek Long, Alessandro Saetti, Yannis Dimopoulos. Artificial Intelligence, 2009

369 citations recorded by the source corpus.

The international planning competition (IPC) is an important driver for planning research. The general goals of the IPC include pushing the state of the art in planning technology by posing new scientific challenges, encouraging direct comparison of planning systems and techniques, developing and improving a common planning domain definition language, and designing new planning domains and problems for the research community. This paper focuses on the deterministic part of the fifth international planning competition (IPC5), presenting the language and benchmark domains that we developed for the competition, as well as a detailed experimental evaluation of the deterministic planners that entered IPC5, which helps to understand the state of the art in the field. We present an extension of pddl, called pddl3, allowing the user to express strong and soft constraints about the structure of the desired plans, as well as strong and soft problem goals. We discuss the expressive power of the new language focusing on the restricted version that was used in IPC5, for which we give some basic results about its compilability into pddl2. Moreover, we study the relative performance of the IPC5 planners in terms of solved problems, CPU time, and plan quality; we analyse their behaviour with respect to the winners of the previous competition; and we evaluate them in terms of their capability of dealing with soft goals and constraints, and of finding good quality plans in general. Overall, the results indicate significant progress in the field, but they also reveal that some important issues remain open and require further research, such as dealing with strong constraints and computing high quality plans in metric-time domains and domains involving soft goals or constraints.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/7f25169ee1d085d3193f47ea0722a216df5a6a4d

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Citations in the corpus

The LAMA Planner: Guiding Cost-Based Anytime Planning with Landmarks (2010, methodology): “This domain was previously used at IPC 2006 (Gerevini et al., 2009).”
Learning Probabilistic Hierarchical Task Networks as Probabilistic Context-Free Grammars to Capture User Preferences (2014, background): “Deterministic planning in the .fth international planning competition: PDDL3 and experimental evaluation of the planners.”
"Distance"? Who Cares? Tailoring Merge-and-Shrink Heuristics to Detect Unsolvability (2014, background): “, directed model checking (Edelkamp, Lluch-Lafuente, and Leue 2004) and over-subscription planning (Gerevini et al. 2009)), and (b) the practical importance of detecting dead-ends in solvable problems (e.”
Temporal Planning with Preferences and Time-Dependent Continuous Costs (2012, methodology): “For (hold-during a b F) and (hold-after d F) , we use the timed-initial literal compilation (Gerevini et al. 2009).”
Towards Clause-Learning State Space Search: Learning to Recognize Dead-Ends (2016, background): “(Smith 2004; Gerevini et al. 2009; Domshlak and Mirkis 2015)), in planning with limited resources (e.”
Symbiotic human-robot collaborative assembly (2019, methodology): “The definition of the plan can rely on Planning Domain Definition Language (PDDL) [155,65,128] or on timeline planning approaches [176,66,16].”
Planning as an Iterative Process (2012, background): “While it may be possible to enforce such constraints by using PDDL 3.0 preferences (Gerevini et al. 2009) or cleverly modifying operator descriptions, it is awkward to do so, and it is unclear whether existing planners are able to efficiently cope with such constraints.”
Preferred Explanations: Theory and Generation via Planning (2011, methodology): “…by a desire to generate explanations using state-of-the-art planning technology, we propose a means of compiling our f-PLTL preferences into the syntax of PDDL3, the Planning Domain Description Language 3 that supports the representation of temporally extended preferences (Gerevini et al. 2009).”