This is an attempt to resuscitate the Alchemy2 project.

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

By using Alchemy, you agree to accept the license agreement in license.txt

src/ contains source code and a makefile. doc/ contains a change log, and a manual in PDF, PostScript and html formats. exdata/ contains a simple example of Alchemy input files. bin/ is used to contain compiled executables.

Please refer to the change log at http://alchemy.cs.washington.edu/ for the latest changes to Alchemy.