ILP Symmetry Breaking

Overview

This project aims to exploit inductive logic programming to lift symmetry breaking constraints of ASP programs.

Given an ASP file, we use the system SBASS (symmetry-breaking answer set solving) to infer its graph representation and then detect the symmetries as a graph automorphism problem (performed by the system SAUCY). SBASS returns a set of (irredundant) graph symmetry generators, which are used in our framework to compute the positive and negative examples for the ILP system ILASP.

Note: the files of Active Background Knowledge (active_BK/active_BK_sat) contain the constraints learned for the experiments. To test the framework, remove the constraints and follow the files' instructions to obtain the same result.

Project Structure

.
├── \Experiments              # Directory with experiments results 
│   ├── experiments.csv         # CSV file with results
│   └── experiments             # Script to compare the running-time performance     
│
├── \Instances              # Directory with problem instances
│   ├── \House_Configuration     # House-Configuration Problem     
│   ├── \Pigeon_Owner            # Pigeon-Hole Problem with colors and owners extension   
│   ├── \Pigeon_Color            # Pigeon-Hole Problem with colors extension
│   └── \Pigeon_Hole             # Pigeon-Hole Problem  
│
├── \src                    # Sources  
│   ├── \ILASP4                  # ILASP4 
│   ├── \SBASS                   # SBASS 
│   ├── file_names.py            # Python module with file names
│   ├── parser.py                # Main python file: create the positive and negative examples from SBASS output
│   ├── remove.py                # Auxiliary python file to remove duplicate in smodels file
│   └── permutations.lp          # ASP file which computes the (partial) non symmetric 
│                                  permutations of atoms
│
├── .gitignore 
├── .gitattributes
├── ILP_SBC                 # Script that runs SBASS and lift the SBC found using ILASP
└── README.md

Prerequisites

• Python3
• Clingo
• ILASP4 dependencies

Usage

1) Create default positive examples

Create the default positive examples for Pigeon_Hole problem: each instance in the directory Gen generate a positive example.

$ .\ILP_SBC -g .\Instances\Pigeon_Hole

2) Create positive and negative examples

Default mode: each non-symmetric answer set defines a positive example

 $ .\ILP_SBC -d .\Instances\Pigeon_Hole

Satisfiable mode: define a single positive example with empty inclusions and exclusions

 $ .\ILP_SBC -s .\Instances\Pigeon_Hole

3) Run ILASP to extend the active background knowledge

 $ .\ILP_SBC -i .\Instances\Pigeon_Hole

Citations

C. Drescher, O. Tifrea, and T. Walsh, “Symmetry-breaking answer set solving” (SBASS)

@article{drescherSymmetrybreakingAnswerSet2011,
	title = {Symmetry-breaking answer set solving},
	volume = {24},
	doi = {10.3233/AIC-2011-0495},
	number = {2},
	journal = {AI Commun.},
	author = {Drescher, Christian and Tifrea, Oana and Walsh, Toby},
	year = {2011},
	pages = {177--194}
}

M. Law, A. Russo, and K. Broda, “The {ILASP} System for Inductive Learning of Answer Set Programs” (ILASP)

@article{larubr20b,
     title = {The {ILASP} System for Inductive Learning of Answer Set Programs},
     author = {M. Law and A. Russo  and K. Broda},
     journal = {The Association for Logic Programming Newsletter},
     year = {2020}
}
@misc{ilasp,
     author = {M. Law and A. Russo  and K. Broda},
     title = {Ilasp Releases},
     howpublished = {\url{www.ilasp.com}},
     note = {Accessed: 2020-10-01},
     year={2020}
}