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

Manifold Alignment for Semantically Aligned Style Transfer

tinykernel - A minimal Python kernel so you can run Python in your Python

Script utilizando OpenCV e modelo Machine Learning para detectar o uso de máscaras.

Moiré Attack (MA): A New Potential Risk of Screen Photos [NeurIPS 2021]

A full-fledged version of Pix2Seq

Implementation of ETSformer, state of the art time-series Transformer, in Pytorch

Impelmentation for paper Feature Generation and Hypothesis Verification for Reliable Face Anti-Spoofing

Code for "Long-tailed Distribution Adaptation"

A configurable, tunable, and reproducible library for CTR prediction

[ICCV2021] Official Pytorch implementation for SDGZSL (Semantics Disentangling for Generalized Zero-Shot Learning)

AI创造营：Metaverse启动机之重构现世，结合PaddlePaddle 和 Wechaty 创造自己的聊天机器人

Kaggle DSTL Satellite Imagery Feature Detection

Welcome to The Eigensolver Quantum School, a quantum computing crash course designed by students for students.

EMNLP 2021 Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collections

Generating images from caption and vice versa via CLIP-Guided Generative Latent Space Search

Official Implementation of "Designing an Encoder for StyleGAN Image Manipulation"

Region-aware Contrastive Learning for Semantic Segmentation, ICCV 2021

Graph Neural Networks with Keras and Tensorflow 2.

Pytorch Implementation of Zero-Shot Image-to-Text Generation for Visual-Semantic Arithmetic

the code for our CVPR 2021 paper Bilateral Grid Learning for Stereo Matching Network [BGNet]

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

Manifold Alignment for Semantically Aligned Style Transfer

tinykernel - A minimal Python kernel so you can run Python in your Python

Script utilizando OpenCV e modelo Machine Learning para detectar o uso de máscaras.

Moiré Attack (MA): A New Potential Risk of Screen Photos [NeurIPS 2021]

A full-fledged version of Pix2Seq

Implementation of ETSformer, state of the art time-series Transformer, in Pytorch

Impelmentation for paper Feature Generation and Hypothesis Verification for Reliable Face Anti-Spoofing

Code for "Long-tailed Distribution Adaptation"

A configurable, tunable, and reproducible library for CTR prediction

[ICCV2021] Official Pytorch implementation for SDGZSL (Semantics Disentangling for Generalized Zero-Shot Learning)

AI创造营 ：Metaverse启动机之重构现世，结合PaddlePaddle 和 Wechaty 创造自己的聊天机器人

Kaggle DSTL Satellite Imagery Feature Detection

Welcome to The Eigensolver Quantum School, a quantum computing crash course designed by students for students.

EMNLP 2021 Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collections

Generating images from caption and vice versa via CLIP-Guided Generative Latent Space Search

Official Implementation of "Designing an Encoder for StyleGAN Image Manipulation"

Region-aware Contrastive Learning for Semantic Segmentation, ICCV 2021

Graph Neural Networks with Keras and Tensorflow 2.

Pytorch Implementation of Zero-Shot Image-to-Text Generation for Visual-Semantic Arithmetic

the code for our CVPR 2021 paper Bilateral Grid Learning for Stereo Matching Network [BGNet]

AI创造营：Metaverse启动机之重构现世，结合PaddlePaddle 和 Wechaty 创造自己的聊天机器人