rusty-razor

Rusty Razor is a tool for constructing finite models for first-order theories. The model-finding algorithm is inspired by the chase for database systems. Given a first-order theory, Razor constructs a set of homomorphically minimal models for the theory.

Check out Razor's documentation for more information about the project and introductory examples.

Build

rusty-razor is written in Rust, so you will need Rust 1.48.0 or newer to compile it. To build rusty-razor:

git clone https://github.com/salmans/rusty-razor.git
cd rusty-razor
cargo build --release

This puts rusty-razor's executable in /target/release.

Run

solve

Use the solve command to find models for a theory written in an <input> file:

razor solve -i <input>

The --count parameter limits the number of models to construct:

razor solve -i <input> --count <number>

Bounded Model-Finding

Unlike conventional model-finders such as Alloy, Razor doesn't require the user to provide a bound on the size of the models that it constructs. However, Razor may never terminate when running on theories with non-finite models -- it can be shown that a run of Razor on an unsatisfiable theory (i.e., a theory with no models) is guaranteed to terminate (although it might take a very very long time).This is a direct consequence of semi-decidability of first-order logic.

To guarantee termination, limit the size of the resulting models by the number of their elements using the --bound option with a value for the domain parameter:

razor solve -i <input> --bound domain=<number>

Model-Finding Scheduler

Use the --scheduler option to choose how Razor processes search branches. The fifo scheduler (the default scheduler) schedules new branches last and is a more suitable option for processing theories with few small satisfying models. The lifo scheduler schedules new branches first, and is more suitable for processing theories with many large models.

razor solve -i <input> --scheduler <fifo/lifo>

Toy Example

Consider the following example:

// All cats love all toys:
forall c, t . (Cat(c) and Toy(t) implies Loves(c, t));

// All squishies are toys:
forall s . (Squishy(s) implies Toy(s));

Cat('duchess);   // Duchess is a cat
Squishy('ducky); // Ducky is a squishy

You can download the example file here and run the razor command-line tool on it:

razor solve -i theories/examples/toy.raz

Razor returns only one model with e#0 and e#1 as elements that denote 'duchess and 'ducky respectively. The facts Cat(e#0), Squishy(e#1), and Toy(e#1) in the model are directly forced by the last two formula in the input theory. The fact Loves(e#0, e#1) is deduced by Razor:

Domain: e#0, e#1

Elements: 'duchess -> e#0, 'ducky -> e#1

Facts: Cat(e#0), Loves(e#0, e#1), Squishy(e#1), Toy(e#1)

Razor's documentation describes the syntax of Razor's input and contains more examples.