pyddl

KO | EN

gitlite — search

#javascript #python #hacktoberfest #react #ai #android #machine-learning #linux #nodejs #windows #kubernetes #security #mcp #open-source

(★ 48)

STRIPS planner with PDDL-like problem specification in Python

pyddl Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

PyDDL
=====
_STRIPS planner with PDDL-like problem specification in Python_

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

Installation
------------
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

About
-----
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.

Usage
-----
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.

pyddl

pyddl Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

pyddl

pyddl Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README