An architecture for planning, inverse planning, and inference in planning, using PDDL and Gen.
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/ztangent/Plinf.jl.gitTo 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.
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