InversePlanning.jl

An architecture for planning, inverse planning, and inference in planning, using PDDL and Gen.

Setup

To use this library in your own projects, press ] at the Julia REPL to enter the package manager, then run:

add PDDL SymbolicPlanners
add Gen GenParticleFilters
add PDDLViz GLMakie
add https://github.com/probcomp/InversePlanning.jl.git

To explore the examples provided in this repository, clone this repository, press ] at the Julia REPL to enter the package manager, then run the following commands:

activate examples
dev ./
instantiate

This will activate the examples directory as the project environment, set up your cloned copy of InversePlanning.jl as a dependency, and install any remaining dependencies.

Examples

InversePlanning.jl can be used to model agents that perform model-based heuristic search to achieve their goals. Below, we visualize a sampled trace for a replanning agent that interleaves resource-bounded plan search with plan execution:

We can then perform goal inference for these agents:

Notice that the correct goal is eventually inferred, despite backtracking by the agent. This is because we model the agent as boundedly rational: it does not always produce optimal plans. Indeed, this modeling assumption also allows us to infer goals from failed plans:

Because we use the Planning Domain Definition Language (PDDL) as our underlying state representation, our architecture supports a large range of domains, including the classic Blocks World:

For more details about the modeling and inference architecture, consult our paper:

T. Zhi-Xuan, J. L. Mann, T. Silver, J. B. Tenenbaum, and V. K. Mansinghka, “Online Bayesian Goal Inference for Boundedly-Rational Planning Agents,” arXiv:2006.07532 [cs], Jun. 2020.

Full example code for several domains can be found here: Gridworld; Doors, Keys & Gems; Block Words