Implementations of some classical Artificial Intelligence algorithm by Python 3
The repository provides demo programs for implementations of artificial intelligence algorithms by Python 3. I hope these programs will help people who would like to understand the intelligence theories via implementations.
I will enrich those implementations and descriptions from time to time. If you include any of my work into your website or project; please add a link to this repository and send me an email to let me know.
All implementations are trying to separate algorithms from task domains which means you can levelrage the algorithms implementation into any other tasks you want. The hard code variable is avoid in all implementation. The definition of global variables will define in the very begining of programs.
Your comments are welcome. Thanks,
| Algorithm | Description | Link |
|---|---|---|
| Alpha-Beta Pruning | By measuring early evaluation of each branch of a tree structure, Alpha-Beta pruning can help us to reduce the complexity of computation | Source Code |
| Propositional Logic | How to get inferences (answers) via basic axioms and given restrictions / information? This implementation demostrates how the propositional logical algorithm can help us to answer a resource allocation question. This is an implementation of Resolution KB, logic, PL Resolution and WalkSAT in CNF for a wedding arrangement task . | Source Code |
| Decision Networks | Decisoin Networks aka influence diagrams which contain Bayesian Network as Chance nodes, Action choices via Decision nodes, and Outcome preferences via Utility node. This implementation just completes the Bayesian Network inference. | Source Code |
- Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach (AIMA). Prentice Hall, 3rd Edition
- AIMA reference code, aimacode/aima-python : https://github.com/aimacode/aima-python
Cheng-Lin Li@University of Southern California [email protected] or [email protected]