The 1998 AI Planning Systems Competition

By Drew V. McDermott. 2000

434 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

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Citations in the corpus

Learning First-Order Symbolic Representations for Planning from the Structure of the State Space (2019, methodology): “It then produces symbolic PDDL-like planning representations that account for the input graphs in the form of a general ﬁrst-order domain with action schemas and predicate symbols, and instance information describing objects and an initial situation.”
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation (2017, methodology): “For example, McDonald and Pustejovsky (1985) argued that “prose style is a consequence of what decisions are made during the transition from the conceptual representation level to the linguistic level” (p.”
Rearrangement: A Challenge for Embodied AI (2020, background): “…tasks focusing on standardized real-world object sets [13], and competition-style setups, such as RoboCup [43, 67] and the International Planning Competition [53], as well as simulated benchmarks focusing on speciﬁc algorithmic approaches such as reinforcement learning [5, 9, 24, 79, 26].”
PDDL2.1: An Extension to PDDL for Expressing Temporal Planning Domains (2003, methodology): “1 extends pddl in principled ways to achieve the additional expressive power following, as far as possible, McDermott’s maxim “physics, not advice” (McDermott, 2000).”
Bridging the gap between planning and scheduling (2000, methodology): “In the recent planning systems competition held at AIPS-98 [92], all but one of the competing planning systems were based on Graphplan techniques.”
The 3rd International Planning Competition: Results and Analysis (2003, background): “It was organised by Drew McDermott, together with a committee of senior members of the research community (McDermott, 2000).”
Reasoning about Deterministic Actions with Probabilistic Prior and Application to Stochastic Filtering (2010, methodology): “Current approaches to probabilistic ﬁltering with PDDL or situation calculus (e.g., (Reiter 2001; Bacchus, Halpern, and Levesque 1999; Hajishirzi and Amir 2008)) use traditional ﬁltering methods as subroutines for reasoning about sequences of deterministic actions with probabilistic priors.”
Unsupervised Grounding of Plannable First-Order Logic Representation from Images (2019, background): “The purpose of this experiment is to show the feasibility of the first order predicate symbols discovered by FOSAE as the source of a PDDL planning model as well as to show the evidence that the relational model is indeed necessary for modeling a complex environment.”
Concise finite-domain representations for PDDL planning tasks (2009, methodology): “Since its inception in 1998 [38], the Planning Domain Definition Language (PDDL) has become the de-facto standard language for representing classical planning tasks.”
Constraint-Based Attribute and Interval Planning (2003, background): “Until quite recently, researchers studied problems in planning represented in propositional formalisms such as STRIPS (Fikes and Nilsson, 1971) or Planning Domain Definition Language (PDDL) (McDermott, 2000).”