PDDL2.1: An Extension to PDDL for Expressing Temporal Planning Domains

By Maria Fox, Derek Long. 2003

2,320 citations recorded by the source corpus.

In recent years research in the planning community has moved increasingly towards application of planners to realistic problems involving both time and many types of resources. For example, interest in planning demonstrated by the space research community has inspired work in observation scheduling, planetary rover exploration and spacecraft control domains. Other temporal and resource-intensive domains including logistics planning, plant control and manufacturing have also helped to focus the community on the modelling and reasoning issues that must be confronted to make planning technology meet the challenges of application. The International Planning Competitions have acted as an important motivating force behind the progress that has been made in planning since 1998. The third competition (held in 2002) set the planning community the challenge of handling time and numeric resources. This necessitated the development of a modelling language capable of expressing temporal and numeric properties of planning domains. In this paper we describe the language, PDDL2.1, that was used in the competition. We describe the syntax of the language, its formal semantics and the validation of concurrent plans. We observe that PDDL2.1 has considerable modelling power -- exceeding the capabilities of current planning technology -- and presents a number of important challenges to the research community.

Subfields: Artificial Intelligence, Computer Networks and Communications

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Citations in the corpus

Plan explicability and predictability for robot task planning (2015, methodology): “In this paper, we assume that this model is based on PDDL [8], a general planning domain definition language.”
ROSPlan: Planning in the Robot Operating System (2015, methodology): “The ROSPLAN framework is intended to run with any PDDL 2.1 (Fox and Long 2003) domain and planner, and to automate the planning process in ROS, coordinating the activities of lower level controllers.”
Robotic roommates making pancakes (2011, background): “The paper is concluded with a discussion of limitations and open research issues.”
PDDLStream: Integrating Symbolic Planners and Blackbox Samplers via Optimistic Adaptive Planning (2018, methodology): “To compactly model the domain in section 4, we make use of derived predicates (axioms) (Fox and Long 2003; Thiébaux, Hoffmann, and Nebel 2005), which are defined by a logical formula on a state.”
Deterministic planning in the fifth international planning competition: PDDL3 and experimental evaluation of the planners (2009, methodology): “Note that the conditional effects updating the states of the automata also make each action mutually exclusive with every other action (according to PDDL2 definition of mutex actions [29]), and hence force the plan to be sequential.”
Learning Generalized Reactive Policies using Deep Neural Networks (2017, positive): “guided search algorithms. Formal Framework 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. A planning problem dom”
Learning High-Level Planning from Text (2012, methodology): “Defining the Domain In order to execute a traditional planner on the Minecraft domain, we define the domain using the Planning Domain Definition Language (PDDL) (Fox and Long, 2003).”
Constraint-Based Attribute and Interval Planning (2003, background): “1 [6], a version of PDDL which includes facilities for representing temporal planning domains.”
The Fast Downward Planning System (2006, background): “2 (Fox & Long, 2003; Edelkamp & Hoffmann, 2004), i. e., in addition to STRIPS planning, it supports arbitrary formulae in operator preconditions and goal conditions, and it can deal with conditional and universally quantified effects and derived predicates (axioms).”
A Survey of the Seventh International Planning Competition (2012, background): “His research interests are planning with soft goals and planning and execution for robotics and real-time environments.”
Online Replanning in Belief Space for Partially Observable Task and Motion Problems (2019, background): “Effect formulas may set a fluent fact to be true, set a fluent fact to be false (not), or increase the plan cost (incr) [29].”
Concurrent Plan Recognition and Execution for Human-Robot Teams (2014, background): “For our purposes, these actions are grounded PDDL operators (Fox and Long 2003).”
The 3rd International Planning Competition: Results and Analysis (2003, background): “1 language (Fox & Long, 2003).”
The LAMA Planner: Guiding Cost-Based Anytime Planning with Landmarks (2010, background): “2 Level 1 format (Fox & Long, 2003; Edelkamp & Hoffmann, 2004), including ADL conditions and effects and derived predicates (axioms).”