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README.md

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-# PASP - Probabilistic Answer Set Programming
+# dPASP: A Flexible Framework For Neuro-Probabilistic Answer Set Programming
 
 [![Tests](https://github.com/kamel-usp/dpasp/actions/workflows/tests.yml/badge.svg)](https://github.com/kamel-usp/dpasp/actions/workflows/tests.yml)
 [![Docs](https://github.com/kamel-usp/dpasp/actions/workflows/docs.yml/badge.svg)](https://github.com/kamel-usp/dpasp/actions/workflows/docs.yml)
-[![](https://img.shields.io/badge/docs-master-blue.svg)](https://renatogeh.github.io/pasp)
+[![](https://img.shields.io/badge/docs-master-blue.svg)](https://kamel-usp.github.io/dpasp)
 [![GitHub](https://img.shields.io/github/license/kamel-usp/dpasp?color=blue&label=License)](https://github.com/kamel-usp/dpasp/blob/master/LICENSE)
 
-Please see [Learning dPASP Through Examples](http://kamel.ime.usp.br/pages/learn_dpasp) for more information.
+The dPASP framework presents a powerful high-level language for describing
+probabilistic tasks in an intuitive and declarative manner. Just like in traditional
+probabilistic logic programming (PLP), programs in dPASP are written in terms of
+probabilistic facts or rules, allowing for uncertainty to play a role in the knowledge
+description of the problem. Notably, our framework further extends PLP by leveraging the
+expressiveness of neural networks for describing probabilities in possibly hybrid domains.
+Further, by natively embedding neural expressions within the language, dPASP offers
+end-to-end training of sophisticated models and loss functions while requiring minimal user
+knowledge of deep learning system's inner workings.
+
+## Getting Started 
+
+dPASP is mostly written in C with a front user layer in Python. Although a Python API
+is exposed for manipulating programs and their output, dPASP can be used as a
+standalone language and interpreter. 
+
+The easiest way to get started is by reading the tutorial [Learning dPASP Through Examples](http://kamel.ime.usp.br/pages/learn_dpasp).
+
+## Acknowledgment
+
+This software is being developed by the KAMeL group of the University of São Paulo and the Center for Artificial Intelligence.
+If you use this software, please acknowledge by citing the paper below:
+
+  https://arxiv.org/abs/2308.02944