Logical Neural Networks

LNNs are a novel Neuro = Symbolic framework designed to seamlessly provide key properties of both neural nets (learning) and symbolic logic (knowledge and reasoning).

• Every neuron has a meaning as a component of a formula in a weighted real-valued logic, yielding a highly interpretable disentangled representation.
• Inference is omnidirectional rather than focused on predefined target variables, and corresponds to logical reasoning, including classical first-order logic theorem proving as a special case.
• The model is end-to-end differentiable, and learning minimizes a novel loss function capturing logical contradiction, yielding resilience to inconsistent knowledge.
• It also enables the open-world assumption by maintaining bounds on truth values which can have probabilistic semantics, yielding resilience to incomplete knowledge.

Quickstart

To install the LNN:

1. Make sure that the python version you use in line with our setup file, using a fresh environment is always a good idea:

   conda create -n lnn python=3.9 -y
   conda activate lnn
   

2. Install the master branch to keep up to date with the latest supported features:

   pip install git+https://github.com/IBM/LNN
   

Akila Sampath - Certifications & Badges

🎖 Certifications & Badges

Neuro-Symbolic AI Reasoning Badge

As part of coursework on Neuro-symbolic AI, I earned this badge for demonstrating foundational knowledge and the ability to formulate AI reasoning problems within a neuro-symbolic framework. The badge holder has the ability to:

• Create a Logical Neural Network (LNN) model from logical formulas.
• Perform inference using LNNs.
• Explain the logical interpretation of LNN models.

🔗 https://www.credly.com/badges/d2a9e4b2-b718-4267-9c05-6ae8e3c9b935
View Certificate]