Alchemy System

An Alchemy System is a Markov Logic Software System.

• AKA: Alchemy.
• Example(s):
  • Alchemy v2.
  • Alchemy LITE.
  • Alchemy v1.
  • …
• Counter-Example(s):
  • FACTORIE.
  • MALLET.
• See: ML Toolkit.

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References

2014

• http://alchemy.cs.washington.edu/
  • 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, including:
    • Collective classification.
    • Link prediction.
    • Entity resolution.
    • Social network modeling.
    • Information extraction

2011

• (Gogate & Domingos, 2011) ⇒ Vibhav Gogate, and Pedro Domingos. (2011). “Probabilistic Theorem Proving.” In: Proceedings of the Twenty-Seventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-2011).

2010

• http://alchemy.cs.washington.edu/tutorial/tutorial.pdf

• http://alchemy.cs.washington.edu/
  • … If you are not already familiar with Markov logic, we recommend that you first read the paper Unifying Logical and Statistical AI.
  • The beta version of Alchemy is now available. It includes:
    • 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:
    • 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

2009

• (Domingos & Lowd, 2009) ⇒ Pedro Domingos, and Daniel Lowd. (2009). “Markov Logic: An Interface Layer for Artificial Intelligence.” Morgan & Claypool. doi:10.2200/S00206ED1V01Y200907AIM007

2006

• (Domingos et al., 2006) ⇒ Pedro Domingos, Stanley Kok, Hoifung Poon, Matthew Richardson, and Parag Singla. (2006). “Unifying Logical and Statistical AI.” In: Proceedings of the 21st national conference on Artificial intelligence (AAAI 2006).

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