[ENH] add references in readme

README.md

@@ -20,7 +20,7 @@ ABL-Package encapsulates advanced ABL techniques, providing users with
 an efficient and convenient package to develop dual-driven ABL systems,
 which leverage the power of both data and knowledge.
 
-To learn how to use it, please refer to - [document](https://www.lamda.nju.edu.cn/abl_test/docs/build/html/Overview/Abductive-Learning.html).
+To learn how to use it, please refer to - [document](https://www.lamda.nju.edu.cn/abl_test/docs/build/html/index.html).
 
 ## Installation
 
@@ -62,7 +62,7 @@ We provide several examples in `examples/`. Each example is stored in a separate
 + [MNIST Addition](https://github.com/AbductiveLearning/ABL-Package/blob/Dev/examples/mnist_add)
 + [Handwritten Formula](https://github.com/AbductiveLearning/ABL-Package/blob/Dev/examples/hwf)
 + [Handwritten Equation Decipherment](https://github.com/AbductiveLearning/ABL-Package/tree/Dev/examples/hed)
-+ [Zoo](https://gitub.com/AbductiveLearning/ABL-Package/tree/Dev/examples/zoo)
++ [Zoo](https://github.com/AbductiveLearning/ABL-Package/tree/Dev/examples/zoo)
 
 ## Quick Start
 
@@ -164,6 +164,10 @@ bridge = SimpleBridge(model, reasoner, metric_list)
 Finally, we proceed with training and testing.
 
 ```python
-​bridge.train(train_data, loops=1)
+​bridge.train(train_data, loops=1, segment_size=0.01)
 bridge.test(test_data)
-```
+```
+
+## References
+
+For more information about ABL, please refer to: [Zhou, 2019](https://link.springer.com/epdf/10.1007/s11432-018-9801-4?author_access_token=jgJe1Ox3Mk-K7ORSnX7jtfe4RwlQNchNByi7wbcMAY7_PxTx-xNLP7Lp0mIZ04ORp3VG4wioIBHSCIAO3B_TBJkj87YzapmdnYVSQvgBIO3aEpQWppxZG25KolINetygc2W_Cj2gtoBdiG_J1hU3pA==) and [Zhou and Huang, 2022](https://www.lamda.nju.edu.cn/publication/chap_ABL.pdf).

docs/Examples/MNISTAdd.rst

@@ -376,6 +376,6 @@ Out:
         abl - INFO - Eval start: loop(val) [1]
         abl - INFO - Evaluation ended, mnist_add/character_accuracy: 0.986 mnist_add/reasoning_accuracy: 0.973 
         abl - INFO - Saving model: loop(save) [1]
-        abl - INFO - Checkpoints will be saved to results/20231222_22_25_07/weights/model_checkpoint_loop_1.pth
+        abl - INFO - Checkpoints will be saved to log_dir/weights/model_checkpoint_loop_1.pth
         abl - INFO - Test start:
         abl - INFO - Evaluation ended, mnist_add/character_accuracy: 0.983 mnist_add/reasoning_accuracy: 0.967 

docs/Intro/Quick-Start.rst

@@ -123,7 +123,7 @@ Finally, we proceed with training and testing.
 
 .. code:: python
 
-   bridge.train(train_data, loops=1)
+   bridge.train(train_data, loops=1, segment_size=0.01)
    bridge.test(test_data)
 
 Read more about `bridging machine learning and reasoning <Bridge.html>`_.

docs/README.rst

@@ -52,3 +52,9 @@ For Linux users:
     $ sudo apt-get install swi-prolog
 
 For Windows and Mac users, please refer to the `Swi-Prolog Download Page <https://www.swi-prolog.org/Download.html>`_.
+
+References
+----------
+
+For more information about ABL, please refer to: `Zhou, 2019 <https://link.springer.com/epdf/10.1007/s11432-018-9801-4?author_access_token=jgJe1Ox3Mk-K7ORSnX7jtfe4RwlQNchNByi7wbcMAY7_PxTx-xNLP7Lp0mIZ04ORp3VG4wioIBHSCIAO3B_TBJkj87YzapmdnYVSQvgBIO3aEpQWppxZG25KolINetygc2W_Cj2gtoBdiG_J1hU3pA==>`_ 
+and `Zhou and Huang, 2022 <https://www.lamda.nju.edu.cn/publication/chap_ABL.pdf>`_.