STRIPS planner with PDDL-like problem specification in Python

by Gary Doran (gary.doran@case.edu)

PyDDL can be installed using pip:

pip install -e git+https://github.com/garydoranjr/pyddl.git#egg=pyddl

or by running the setup script:

python setup.py install

This is a STRIPS-like planner that I wrote for an AI class assignment. A planning domain and problem is specified in Python, in a manner that is intended to be reminiscent of PDDL. However, only a small subset of PDDL-like constructs are implemented. Planning is performed using A* search, but not too much trouble has been taken to optimize the search efficiency. Therefore, PyDDL is best suited for smaller problems.

A planning domain is specified as a Domain object, and takes a list of Action objects. Unlike PDDL, predicates do not need to be explicitly defined. An Action takes a list of arguments similar to that for PDDL. For example, see the eight-puzzle example script containing the following action:

Action(
    'move-up',
    parameters=(
        ('tile', 't'),
        ('position', 'px'),
        ('position', 'py'),
        ('position', 'by'),
    ),
    preconditions=(
        ('dec', 'by', 'py'),
        ('blank', 'px', 'by'),
        ('at', 't', 'px', 'py'),
    ),
    effects=(
        neg(('blank', 'px', 'by')),
        neg(('at', 't', 'px', 'py')),
        ('blank', 'px', 'py'),
        ('at', 't', 'px', 'by'),
    ),
),

One difference from PDDL here is that parameters are typed using (type, name) tuples rather than specifying types with predicates.

Similarly, a planning problem is specified with a Problem object, which takes a planning domain, a dictionary of typed objects, a list of initial predicates, and a list of goal predicates.

Once a problem has been constructed, it can be passed to the planner function with optional arguments (e.g. a search heuristic) to generate a plan (if one exists). The resulting plan is a sequence of Action objects to execute, or None if no plan exists.

PyDDL supports some basic numeric functions, comparisons, and operations. See the "missionaries and cannibals" problem for example usage.