pracmln comes with an easy-to-use API, which lets you use the learning and reasoning methods provided by pracmln convently in your own applications.


An MLN is represented by an instance of the class pracmln.MLN. An existing MLN can be loaded using its static load method:

mln = MLN.load(files='path/to/file')

Alternatively, the constructor can be used to load the MLN::

mln = MLN(mlnfile='path/to/mlnfile', grammar='PRACGrammar', logic='FirstOrderLogic')

If no mlnfile is specified, the constructor creates an empty MLN object. Using the << operator, one can feed content into the MLN:

>>> from pracmln import MLN
>>> mln = MLN()
>>> mln << 'foo(x)' # predicate declaration
>>> mln << 'bar(y)' # another pred declaration
>>> mln << 'bar(?x) => bar(?y).' # hard logical constraint
>>> mln << 'logx(.75)/log(.25) foo(?x)' # weighted formula

We can dump the MLN into the regular MLN file format by using the write method:

>>> mln.write()

// predicate declarations

// formulas
bar(?x) => bar(?y).
logx(.75)/log(.25)  foo(?x)

If your terminal supports ASCII escape sequences, the syntax of the result will be highlighted. By specifying a stream, one can dump the MLN into a file.

We can access the predicates of the MLN using the predicates attribute:

>>> for pred in mln.predicates:
...     print repr(pred)
<Predicate: bar(y)>
<Predicate: foo(x)>

Formulas are instances of pracmln.logic.Formula and stored in the formulas attribute:

>>> for f in mln.formulas:
...     print f
...     f.print_structure()
bar(?x) => bar(?y)
<Implication: bar(?x) => bar(?y)>: [idx=0, weight=inf] bar(?x) => bar(?y) = ?
    <Lit: bar(?x)>: [idx=?, weight=?] bar(?x) = ?
    <Lit: bar(?y)>: [idx=?, weight=?] bar(?y) = ?
<Lit: foo(?x)>: [idx=1, weight=logx(.75)/log(.25)] foo(?x) = ?

The method print_structure prints the logical structure of a formula as well as a couple of properties, such as its index in the MLN and its weight. As you can see, hard formula constraints are internally represented by assiging them a weight of float('inf'). In presence of evidence, print_structure also prints the truth value of each constituent of a formula, which is a float value in {0,1} for formulas with FOL semantics, and in [0,1] in case of fuzzy logics semantics. If the truth value cannot be determined (as in our example here for we don’t have evidence yet), this is indicated by a question mark = ?.


The central datastructures for representing relational data is the pracmln.Database class. It stores atomic facts about the relational domain of discourse by maintaining a mapping of ground atoms to their respective truth value.

A serialized .db database file can be loaded using the Database.load method:

>>> from pracmln import Database
>>> dbs = Database.load(mln, 'path/to/dbfile')

Since there may be multiple independent databases stored in a single .db file (separated by ---) load always returns a list of pracmln.Database objects.

The mln parameter of the load method must point to an instantated pracmln.MLN object containing all the predicate declarations of atoms occuring in the database. The default constructor creates an empty database:

>>> db = Database(mln)

Loading a database from a static serialized file is fine, but if you consider integrating SRL techniques seamlessly in your application, you rather want to create databases representing the evidences for your reasoning tasks at runtime. pracmln has been designed to support convenient dynamic generation of relational data. Once a database has been loaded or created, new facts can be added using the << operator

>>> db << 'foo(X)'

or by directly setting the truth value of an atom:

>>> db['bar(Y)'] = .0

Similarly to the pracmln.MLN, databases have a write method that prints them to the console:

>>> db.write()
[                              ]   0.000 %  bar(Y)
[■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■] 100.000 %  foo(X)

The bars in front of the atom names indicate the truth values/probabilities of the respective atom. When writing them to a file, they can be switched off.

Truth values of atoms that have been asserted once can be retracted from a database using the del operator:

>>> del db['bar(Y)']
>>> db.write()
[■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■] 100.000 %  foo(X)

A convenient method is Database.query, which lets you make a PROLOG-like query to the database for an arbitrary formula in FOL.


Once an MLN and a database have been loaded or created, we can perform inference using the pracmln.MLNQuery class, which wraps around all inference algorithms provided by pracmln. It takes the MLN object subject to reasoning as a parameter as well as any of the parameters for the inference algorithms described in Inference Methods.

>>> from pracmln import MLNQuery
>>> result = MLNQuery(mln=mln, db=db).run()
>>> result.write()
[■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■] 100.000 % bar(X)
[■■■■■■■■■■■■■■■■■■■■■■■       ]  75.660 % foo(K)

API Reference


In this section, we will introduce the basic interface for using the reasoning methods for MLNs provided by pracmln.

The easiest way

ROS Service interface


This is a tutorial for the ROS service that can be used to query an MLN. We will describe the implemantation of the server together with the ROS messages that are associated with this project. We will also present an example client program that can be used as a template.

Server methods


Handles the query from the client. It expects req to have two fields, req.query and an optional req.config. req.query should be of type MLNQuery while req.config should be of type MLNConfig.

It returns a list of AtomProbPair objects. Each element of the list is an atom with it’s corresponding degree of truth.


Keeps an infinite loop while waiting for clients to ask for the service.


Storage is a singleton class that keeps track of an MLNInfer object together with the settings for the inference proceedure.

Example client

scripts.mln_mln.mln_interface_client(query, config=None)
This is an example of the client quering the service. The important thing to note is that you have the option to set the configuration parameters only once and use the the same settings in further calls.



This ROS message contains the following fields:

queries - This message is an ecoding of the queries that will
sent to the service.

This ROS message contains the follwing fields:

results - This is what is returned by the service. results
is a list of AtomProbPair objects. Each atom is associated with a probability value.

This is a message that is used to initialize the configuration parameters for quering the service. You have an option to pass this argument only once and reuse the same configurations over and over. It contains the following fields:

mlnFiles - a *.mln file that describes the MLN

db - the evidence database

method - the inference method to be used

engine - the inference engine to be used

output_filename - the name of the output filename

saveResults - this field should be set to true if you wish to save the results

logic - specifies the logic to be used for inference

grammar - specifies the grammar to be used


This message is a pair of an Atom and a Probabality. It contains the following fields:

atom - string describing the atom

prob - a probability value for the atom’s degree of truth



This is the main service. It contains two fields:

MLNQuery - This is the query string

MLNConfig - This specifies which engine, inference method
etc is going to be used for inference. This should be set at least once.