The following is what we are using for development. Basically similar versions should run fine.
- python3.7
- tensorflow >=2.0 (for running the core, wrapper etc)
- numpy >= 1.17.2 (for examples and tests)
- matplotlib >= 3.1.1 (for examples)
Installing dependencies is easy. Just use pip install tensorflow numpy matplotlib or use a virtualenv.
- Logic Tensor Networks: Deep Learning and Logical Reasoning from Data and Knowledge, Luciano Serafini and Artur d'Avila Garcez, Arxiv.org
- Learning and Reasoning with Logic Tensor Networks, Luciano Serafini and Artur d'Avila Garces, Proc. AI*IA 2016
- Learning and reasoning in logic tensor networks: theory and application to semantic image interpretation, Luciano Serafini, Ivan Donadello, Artur d'Avila Garces, Proc. ACM SAC 2017
- Logic tensor networks for semantic image interpretation, Ivan Donadello, Luciano Serafini and Artur d'Avila Garces. Proc. IJCAI 2017
Checkout recent tutorials on Logic Tensor Networks (LTN)
- What are “Logic Tensor Networks”?, Lucas Buchberger
- Dagstuhl Seminar “Human-Like Neural-Symbolic Computation, Lucas Buchberger
- Human-Like Neural-Symbolic Computing. 2017, Dagstuhl Seminar 17192
This project is licensed under the MIT License - see the LICENSE file for details
LTN has been developed thanks to active contributions and discussions with the following people:
- Alessandro Daniele (FBK)
- Artur d’Avila Garces (City)
- Francesco Giannini (UniSiena)
- Giuseppe Marra (UniSiena)
- Ivan Donadello (FBK)
- Lucas Brukberger (UniOsnabruck)
- Luciano Serafini (FBK)
- Marco Gori (UniSiena)
- Michael Spranger (Sony CSL)
- Michelangelo Diligenti (UniSiena)