update doc

.github/workflows/publish.yml

@@ -1,16 +1,20 @@
+# From https://github.com/zjp-CN/mdbook-template
+# Simple workflow for deploying static content to GitHub Pages
 name: Deploy mdBook
-
 on:
+  # Runs on pushes targeting the default branch
   push:
-    branches: [ "main" ]
+    branches: ["main"]
+  workflow_run:
+    workflows: ["Mdbook build"]
 
 jobs:
   build:
-
     runs-on: ubuntu-latest
-
     steps:
       - uses: actions/checkout@v2
+      - name: cd into doc dir
+        run: cd doc
       - uses: XAMPPRocky/[email protected]
         with:
-          token: ${{ secrets.GITHUB_TOKEN }}
+            token: ${{ secrets.GITHUB_TOKEN }}

README.md

@@ -1,91 +1,14 @@
 ![Rust](https://github.com/AlexandreDubray/schlandals/actions/workflows/rust.yml/badge.svg)
 [![codecov](https://codecov.io/gh/AlexandreDubray/schlandals/branch/main/graph/badge.svg?token=J4J2I9Q9KX)](https://codecov.io/gh/AlexandreDubray/schlandals)
 
-**The code is still in early development and should not be considered stable**
-
-# schlandals
-
-Schlandals is a projected weighted model counter which targets inference in probabilistic models.
-Current known probability queries that can be solved with the solver include
-  - Computing the probability of some target variables (given some evidences) in a Bayesian network
+Schlandals is a state-of-the-art _Projected Weighted Model Counter_ specialized for probabilistic inference over discrete probability distributions.
+Currently, there are known modelization for the following problems
+  - Computing the marginal probabilities of a variable in a Bayesian Network
   - Computing the probability that two nodes are connected in a probabilistic graph
-  - A mapping from [ProbLog](https://github.com/ML-KULeuven/problog) programs to Schlandals
-
-# Problem specification
-
-A model counter is a  program that counts the number of satisfying assignments of a boolean formula F.
-In its projected version, the models are reduced to a subset of the variables (which we call *probabilistic*) and if the problem is weighted, each model has a weight
-and the counter returns the sum of the model's weight.
-In addition to that, we impose two more constraints
-  - All clauses in the formula F are Horn clauses (of the form `I => h` with `I` a conjuction of literals)
-  - The probabilistic variables are partioned into *distributions* such that the weights in each distribution sum up to 1
-
-Schlandals takes as input a file using a modified version of the [DIMACS](https://mccompetition.org/assets/files/2021/competition2021.pdf) format
-```
-c Lines starting with `c` alone are comments.
-c The first line must be the head in the form of "p cnf <number variable> <number clauses>
-p cnf 16 11
-c The distribution are defined by lines starting with "c p distribution"
-c Each value in a distribution is assigned to a variable, starting from 1. Hence, the first N variables
-c are used for the distributions.
-c p distribution 0.2 0.8
-c p distribution 0.3 0.7
-c p distribution 0.4 0.6
-c p distribution 0.1 0.9
-c p distribution 0.5 0.5
-c Finally the clauses are encoded as in DIMACS.
-11 -1 0
-12 -2 0
-13 -11 -3 0
-13 -12 -5 0
-14 -11 -4 0
-14 -12 -6 0
-15 -13 -7 0
-15 -14 -9 0
-16 -13 -8 0
-16 -14 -10 0
--12 0
-```
-
-# Usage
-
-To use the solver you must have the Rust toolchain installed. Once this is done you can clone this repository and build the solver with the following commands
-```
-git clone [email protected]:aia-uclouvain/schlandals.git && cd schlandals && cargo build --release
-```
-Once the command has been built, the binary is located in `<SCHLANDALS_DIR>/target/release/schlandals` (with `<SCHLANDALS_DIR>` the directory in which you cloned the repo).
-If you want to be able to run Schlandals for anywhere, you can add `<SCHLANDALS_DIR>/target/release` to your `$PATH`.
-
-The binary's CLI arguments are organized by commands. Three commands are currently supported: `search` to solve the problem by a DPLL search, `compile` to compile the input into an arithmetic circuit and `read-compiled` to evaluate a previously compiled input.
-These are explained next, after a quick note on the heuristics supported by the solver (for the `search` and `compile` sub-command)
-### Heuristics
-
-Schlandals comes with various heuristic that can be used during the search/compilation.
-The current heuristics are based on the implication graph of the input. In such graph, there is one node per clause and a link between clause C1 (I1 => h1) and C2 (I2 => h2) if the h1 is in I2.
-The available heuristics are
-  - `min-in-degree`: Select a distribution from the clause with the lowest in degree.
-  - `min-out-degree`: Select a distribution from the clause with the lowest out degree.
-  - `max-degree`: Select a distribution from the clause with the highest degree.
-
-### Search
-
-`schlandals search -i <input> -b <branching heuristic> [-s -m <memory limit]` launch the search based solver.
-The `i` is a path to a valid input file, `b` is a valid branching heuristic.
-The optional `s` flag tells if stats must be recorded or not and `m` can be used to provide a memory limit.
-
-### Compilation
-
-`schlandals compile -i <input> -b <branching heuristic> [--fdac <outfile> --dotfile <dotfile>]` can be used to compile the input problem as an arithmetic circuit.
-The circuit can be stored in the file given by the `fdac` argument and a DOT visualization can be saved in the argument provided by the `dotfile` argument.
-
-### Reading a compiled file
-
-A previously compiled file can be read using `schlandals read-compiled -i <fdac file> [--dotfile <dotfile]`.
-
-# Citing
-
-To cite Schlandals in your work, use the following
+  - Computing the probability of [ProbLog](https://github.com/ML-KULeuven/problog) programs
 
+For more information on how to use Schlandals and its mechanics, check [the documentation](https://aia-uclouvain.github.io/schlandals/doc).
+You can cite Schlandals using the following bibtex entry
 ```
 @InProceedings{dubray_et_al:LIPIcs.CP.2023.15,
   author =	{Dubray, Alexandre and Schaus, Pierre and Nijssen, Siegfried},
@@ -105,4 +28,4 @@ To cite Schlandals in your work, use the following
   doi =		{10.4230/LIPIcs.CP.2023.15},
   annote =	{Keywords: Model Counting, Bayesian Networks, Probabilistic Networks}
 }
-```
+```