Welcome to the Alchemy system! This user's manual is designed for end users wishing to perform learning and inference on Markov logic networks. It consists of the following sections:
The Alchemy package provides a series of algorithms for statistical relational learning and probabilistic logic inference, based on the Markov logic representation. If you are not already familiar with Markov logic, we recommend that you read the papers Markov Logic Networks , Discriminative Training of Markov Logic Networks , Learning the Structure of Markov Logic Networks , Memory-Efficient Inference in Relational Domains  and Sound and Efficient Inference with Probabilistic and Deterministic Dependencies  (mln.pdf, dtmln.pdf, lsmln.pdf, lazysat.pdf and mcsat.pdf in the papers/ directory) before reading this manual.
We welcome your feedback on any aspect of the Alchemy package. Please email us at firstname.lastname@example.org to let us know what you find easy or hard to use, what results you have obtained with Alchemy, the features you wish to have but are not currently provided, and any bugs that you encounter.
Please cite Kok et al. (2008)  if you use the Alchemy system.
Please be aware that this is a beta release. Some aspects of the documentation may not be as clear, and some aspects of its usage may not be as user-friendly, as you would like. We have tested the code but some bugs may inadvertently still remain.
This beta release includes: 0in
In the next release we plan to include: 0in
Alchemy uses: 0in
The development of Alchemy was partly funded by DARPA grant FA8750-05-2-0283 (managed by AFRL), DARPA contract NBCH-D030010 (subcontracts 02-000225 and 55-000793), NSF grant IIS-0534881, ONR grants N00014-02-1-0408 and N00014-05-1-0313, a Sloan Research Fellowship, and an NSF CAREER Award (both of these to Pedro Domingos). The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of DARPA, NSF, ONR, or the United States Government.