JudgeD is a proof-of-concept implementation of a probabilistic variant of Datalog. JudgeD is available under the MIT license.

JudgeD requires Python 3.4 or newer.

1. Find the release you are interested in
2. pip install <release tar.gz> to install (if you also have old versions of Python installed on your system, you may need to explictly use pip3 instead)
3. Have a look at the examples in examples/ in this repository, or play with the interactive interpreter: judged

1. git clone this repository
2. Set up a virtualenv with python3.4+
3. Get to work on the source, using ./judged.py (or python -m judged) as entry point
4. Run tests with python -m tests
5. Package source release with python setup.py sdist

The JudgeD solver currently has three variants: deterministic, exact and montecarlo.

The deterministic variant is the deterministic basis of JudgeD. It is an SLDNF based implementation of Datalog with negation in Python.

The exact and montecarlo variants are two proof-of-concept implementations of probabilistic datalog. The exact version determines the exact sentence describing the validity of the answers, it does not calculate probabilities, nor does it handle negation. The montecarlo version calculate answer probabilities through Monte Carlo simulation, it approximates the probabilities but does not provide an exact sentence.

The syntax of JudgeD program closely resembles traditional datalog, with the addition of the descriptive sentences. Additionally, the probabilities attached to the labels are included in the syntax. An example of a simple coin-flip would be:

heads(c1) [x=1].
tails(c1) [x=2].

@P(x=1) = 0.5.
@P(x=2) = 0.5.

The first two lines establish simple facts and attach sentences to make them mutually exclusive. The third line contain annotations that attach probabilities to the labels to allow the calculation of answer probabilities. When presented with the query heads(C)? the answer heads(c1) has a probability of 0.5.

The descriptive sentences are propositional logic expressions that use labels of the form partition=value as atoms. It is allowed to use non-numeric values, i.e., labels like x=heads or choice=opens_door_1 are valid. Complex dependencies between clauses can be given through the combination of these labels with the and, or and not operations leading to sentences like (x=1 and y=2) or z=1.

For the Monte Carlo simulation, the probabilities for each label have to be defined. This can be done manually per label:

@P(x=1) = 0.333.
@P(x=2) = 0.333.
@P(x=3) = 0.333.

Or, if a uniform distribution is desired, with:

@uniform x.

Note that the @uniform annotation should be placed after all values for the given partition have been defined.

Furthermore, it is possible to describe a set of actions that is to be performed multiple times, based on the answer of a query (this construct is called the generator syntax):

coin(c1).
coin(c2).

{
    result(C, heads) :- coin(C) [c(C)=heads].
    result(C, tails) :- coin(C) [c(C)=tails].
    @uniform c(C).
| coin(C) }

This declares the result and attached sentence once for each answer to coin(C)?. The generator syntax effectively reads as "perform these actions, given the answer to this query". The variables that are bound in the query are used to substitute variables in the body of the generator. In the example, C will be replaced with c1 and c2. To see the generator syntax in action inspect examples/coins.dl.

Interpreter parameter documentation can be produced by invoking judged with the --help flag. The subcommands each have their own help documentation. For ease of use, some useful combinations are given here.

• judged exact --help: Gets the full list of options for the exact variant of JudgeD.
• judged deterministic -V: runs an interactive deterministic datalog prompt in verbose mode, showing each statement is it is processed.
• judged deterministic -f color -v -i examples/power.dl: -f color Explicitly declares colored output formatting, -v runs in verbose mode to show each statement as it is processed, and -i switches to interactive mode after the given files are processed. Use -f plain to switch the colored output off, which is especially useful for use in a Windows console which does not support used ANSI standard with proper drivers.
• judged deterministic -d examples/ancestor.dl: Runs the ancestor.dl example file with a debugging trace of the query answering process.

Furthermore, in interactive mode the interpreter offers several introspective commands. More information on these can be obtained through type .help in the interpreter.

It is possible to write extensions to JudgeD in python. This is demonstrated in examples/exthello.dl and examples/exthello.py. To run the example, use judged exact -e examples.exthello examples/exthello.dl.