PDDL.jl: An Extensible Interpreter and Compiler Interface for Fast and Flexible AI Planning
Name
Tan-xuan-SM-EECS-2022-thesis.pdf
Description
Thesis PDF
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2.15 MB
Format
Adobe PDF
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Author(s)
Zhi-Xuan, Tan
Advisor(s)
Mansinghka, Vikash K.
Tenenbaum, Joshua B.
Date Issued
February 2022
Publisher
Massachusetts Institute of Technology
Abstract
The Planning Domain Definition Language (PDDL) is a formal specification language for symbolic planning problems and domains that is widely used by the AI planning community. However, most implementations of PDDL are closely tied to particular planning systems and algorithms, and are not designed for interoperability or modular use within larger AI systems. This limitation also makes it difficult to support extensions to PDDL without implementing a dedicated planner for that extension, inhibiting the generality and reach of automated planning.
To address these limitations, we present PDDL.jl, an extensible interpreter and compiler interface for fast and flexible AI planning. PDDL.jl exposes the semantics of planning domains through a common interface for executing actions, querying state variables, and other basic operations used within AI planning applications. PDDL.jl also supports the extension of PDDL semantics (e.g. to stochastic and continuous domains), domain abstraction for generalized heuristic search (via abstract interpretation), and domain compilation for efficient planning, enabling speed and flexibility for PDDL and its many descendants. Collectively, these features allow PDDL.jl to serve as a general high-performance platform for AI applications and research programs that leverage the integration of symbolic planning with other AI technologies, such as neuro-symbolic reinforcement learning, probabilistic programming, and Bayesian inverse planning for value learning and goal inference.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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