00mjk / plinf.jl

Planning, inverse planning, and inference in planning, using PDDL and Gen.

Plinf.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 https://github.com/JuliaPlanners/PDDL.jl.git
add https://github.com/ztangent/Plinf.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 activate . and instantiate to install all necessary dependencies.

Examples

Plinf.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