PDDL-the planning domain definition language

By Drew V. McDermott, Malik Ghallab, A. Howe, Craig A. Knoblock, Ashwin Ram, Manuela M. Veloso, Daniel S. Weld, D. Wilkins. 1998

Subfields: Knowledge Graphs & Representation

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Knowledge Graphs & Representation

Citations in the corpus

ALFWorld: Aligning Text and Embodied Environments for Interactive Learning (2020, methodology): “ALFWorld uses PDDL - Planning Domain Deﬁnition Language (McDermott et al., 1998) to describe each scene from ALFRED and to construct an equivalent text game using the TextWorld engine.”
ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks (2019, methodology): “Thus, to generate expert demonstrations we encode the agent and object states, as well as high-level environment dynamics, into Planning Domain Definition Language (PDDL) rules [18].”
Handling Model Uncertainty and Multiplicity in Explanations via Model Reconciliation (2018, background): “This forms the classical definition of a planning problem (Russell and Norvig 2003) whose models are represented in the syntax of PDDL (McDermott et al. 1998).”
Symbols as a Lingua Franca for Bridging Human-AI Chasm for Explainable and Advisable AI Systems (2021, unknown): “These models should again be constructed using the elements from the shared symbolic vocabulary (see Section 4.1) and may need to incorporate additional representational considerations like allowing for causal relations (such as structural causal models a la Pearl 2009) and being compatible with fol”
HTN planning for Web Service composition using SHOP2 (2004, background): “The expressivity of SHOP2 preconditions and effects are similar to those found in Planning Domain Definition Language (PDDL) [7].”
Deterministic planning in the fifth international planning competition: PDDL3 and experimental evaluation of the planners (2009, background): “The general goals of the IPC include pushing the state of the art in planning technology by posing new scientific challenges, encouraging and conducting direct comparison of planning systems and techniques, developing and improving a common planning domain definition language, pddl [29,36,41], and d”
PDDLStream: Integrating Symbolic Planners and Blackbox Samplers via Optimistic Adaptive Planning (2018, methodology): “pling procedures in Planning Domain Definition Language (PDDL) (McDermott et al. 1998).”
Inferring Team Task Plans from Human Meetings: A Generative Modeling Approach with Logic-Based Prior (2015, methodology): “The Planning Domain Description Language (PDDL) (McDermott et al., 1998) is a standard planning language, inspired by the Stanford Research Institute Problem Solver (STRIPS) (Fikes & Nilsson, 1972) and Action Description Language (ADL) (Pednault, 1987), and is now utilized in the International…”
DAML-S: Semantic Markup for Web Services (2001, methodology): “In developing the ontology, we drew from a variety of sources, including work in AI on standardizations of planning languages [9], work in programming languages and distributed systems [16, 15], emerging standards in process modeling and workflow technology such as the NIST’s Process Specification L”
A review of generalized planning (2019, background): “The Planning Domain Definition Language (PDDL) [59] is the input language for the International Planning Competition (IPC) [95] and the de facto”
Regression Planning Networks (2019, background): “In traditional AI literature, symbolic planners have shown remarkable capability in solving high-level reasoning problems by planning in human-interpretable symbolic spaces [1, 2].”
IkeaBot: An autonomous multi-robot coordinated furniture assembly system (2013, background): “ABPL aims to overcome the necessary complexity of expressing object-oriented relations within first-order logical systems such as PDDL, the Planning Domain Definition Language [9].”
Web services composition: A decade's overview (2014, background): “In OWLS-Xplan, an artificial intelligence planner called Xplan is applied to generate the services composition plan, which takes as input the PDDL descriptions of OWL-S services and a planning query, and returns a plan sequence of component services that satisfies the query goal.”
A Survey of the Seventh International Planning Competition (2012, background): “The major important contribution of the first competition was to establish a common standard language for defining planning problems — the planning domain definition language (PDDL) (McDermott 1998) — which has been developed and extended throughout the competition series.”
Learning action models from plan examples using weighted MAX-SAT (2007, background): “Keywords: Learning action models; Automated planning; Statistical relational learning”