Alchemy System
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An Alchemy System is a Markov Logic Software System.
- AKA: Alchemy.
- Example(s):
- Counter-Example(s):
- See: ML Toolkit.
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:
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/
- … 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).