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

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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

Code for one-stage adaptive set-based HOI detector AS-Net.

Hand tracking demo for DIY Smart Glasses with a remote computer doing the work

Interpretable-contrastive-word-mover-s-embedding

Implementation of fast algorithms for Maximum Spanning Tree (MST) parsing that includes fast ArcMax+Reweighting+Tarjan algorithm for single-root dependency parsing.

(ImageNet pretrained models) The official pytorch implemention of the TPAMI paper "Res2Net: A New Multi-scale Backbone Architecture"

Implementation of "Deep Implicit Templates for 3D Shape Representation"

Sound Source Localization for AI Grand Challenge 2021

Source code for our paper "Do Not Trust Prediction Scores for Membership Inference Attacks"

Using image super resolution models with vapoursynth and speeding them up with TensorRT

这是一个deeplabv3-plus-pytorch的源码，可以用于训练自己的模型。

Chainer Implementation of Semantic Segmentation using Adversarial Networks

An investigation project for SISR.

Codecov coverage standard for Python

THIS IS THE **OLD** PYMC PROJECT. PLEASE USE PYMC3 INSTEAD:

This is the official implementation for "Do Transformers Really Perform Bad for Graph Representation?".

Off-policy continuous control in PyTorch, with RDPG, RTD3 & RSAC

Code to run experiments in SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression.

Customizable RecSys Simulator for OpenAI Gym

A deep learning network built with TensorFlow and Keras to classify gender and estimate age.

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THIS IS THE OLD PYMC PROJECT. PLEASE USE PYMC3 INSTEAD: