Exploit ILP to learn symmetry breaking constraints of ASP programs.

Last update: Apr 13, 2022

Overview

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

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}
}

Exploit ILP to learn symmetry breaking constraints of ASP programs.

Related tags

Overview

ILP Symmetry Breaking

Overview

Project Structure

Prerequisites

Usage

1) Create default positive examples

2) Create positive and negative examples

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

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

3) Run ILASP to extend the active background knowledge

Citations

Owner

Research Group Production Systems

PyTorch implementation for STIN

The object detection pipeline is based on Ultralytics YOLOv5

The dataset and source code for our paper: "Did You Ask a Good Question? A Cross-Domain Question IntentionClassification Benchmark for Text-to-SQL"

Joint Gaussian Graphical Model Estimation: A Survey

Learning Logic Rules for Document-Level Relation Extraction

A Python library for differentiable optimal control on accelerators.

Code for “ACE-HGNN: Adaptive Curvature ExplorationHyperbolic Graph Neural Network”

Fuzzy Overclustering (FOC)

Get started learning C# with C# notebooks powered by .NET Interactive and VS Code.

Here is the implementation of our paper S2VC: A Framework for Any-to-Any Voice Conversion with Self-Supervised Pretrained Representations.

Human Pose estimation with TensorFlow framework

A really easy-to-use and powerful sudoku solver.

Official code of "Mitigating the Mutual Error Amplification for Semi-Supervised Object Detection"

Code and datasets for TPAMI 2021

Keras implementation of AdaBound

Learning Features with Parameter-Free Layers (ICLR 2022)

Optimal Camera Position for a Practical Application of Gaze Estimation on Edge Devices,

🕹️ Official Implementation of Conditional Motion In-betweening (CMIB) 🏃

A very lightweight monitoring system for Raspberry Pi clusters running Kubernetes.

The official repository for Deep Image Matting with Flexible Guidance Input