The pre-release version of Alchemy 2.0 is available
for download. Documentation will be available shortly.
Welcome to the Alchemy system! Alchemy is a software package providing
a series of algorithms for statistical relational learning and
probabilistic logic inference, based on the Markov logic representation.
Alchemy allows you to easily develop a wide range of AI applications,
If you are not already familiar with Markov logic,
we recommend that you first read the paper
Unifying Logical and Statistical AI.
- Collective classification
- Link prediction
- Entity resolution
- Social network modeling
- Information extraction
The beta version of Alchemy is now available.
- Discriminative weight learning (Voted Perceptron, Conjugate Gradient, and Newton's Method)
- Generative weight learning
- Structure learning
- MAP/MPE inference (including memory efficient)
- Probabilistic inference: MC-SAT,
Gibbs Sampling, Simulated Tempering, Belief Propagation (including lifted)
- 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
In the next release we plan to include:
Email us with questions, bug reports, feature requests, etc. at
<the system name> at cs dot washington dot edu.
- Online learning
- Exact inference for small domains
- Specification of probabilities instead of weights for formulas in an MLN,
and of probabilities for ground atoms in a database
- More extensive documentation
Alchemy is funded by: