Alchemy 2.0 is a software package for inference and learning in Markov logic networks (MLNs). As the name suggests, Alchemy 2.0 is based on the Alchemy software package. The novelty in Alchemy 2.0 is that it includes several lifted probabilistic inference algorithms. Alchemy allows you to easily develop a wide range of AI applications, including:

• Collective classification
• Link prediction
• Entity resolution
• Social network modeling
• Information extraction

Features

Alchemy 2.0 includes the following algorithms from the original Alchemy system:

• Discriminative weight learning (Voted Perceptron, Conjugate Gradient, and Newton's Method)
• Generative weight learning
• Structure learning
• propositional MAP/MPE inference (including memory efficient)
• propositional and lazy Probabilistic inference algorithms: MC-SAT, Gibbs Sampling and Simulated Tempering
• Lifted Belief propagation
• Support for native and linked-in functions
• Block inference and learning over variables with mutually exclusive and exhaustive values
• EM (to handle ground atoms with unknown truth values during learning)
• Specification of indivisible formulas (i.e. formulas that should not be broken up into separate clauses)
• Support of continuous features and domains
• Online inference
• Decision Theory

The key new feature of Alchemy 2.0 is lifted inference algorithms (both exact and sampling-based). Specifically, it includes the following inference algorithms:

• Probabilistic theorem proving (lifted weighted model counting)
• Lifted importance sampling
• Lifted Gibbs sampling

Information

• GitHub: https://github.com/PhDP/alchemy2
• Community: https://github.com/PhDP/alchemy2/issues