From ba35158d5de91d4835c40eed862ca4473837227a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Pikach=C3=BA?= Date: Tue, 11 Jul 2023 05:03:00 -0700 Subject: [PATCH] updated animal.ipynb link --- lessons/2-Symbolic/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lessons/2-Symbolic/README.md b/lessons/2-Symbolic/README.md index aefced6d..8dd36392 100644 --- a/lessons/2-Symbolic/README.md +++ b/lessons/2-Symbolic/README.md @@ -149,7 +149,7 @@ Expert systems can be implemented using different tools: ## ✍️ Exercise: Animal Inference -See [Animals.ipynb](Animals.ipynb) for an example of implementing forward and backward inference expert system. +See [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) for an example of implementing forward and backward inference expert system. > **Note**: This example is rather simple, and only gives the idea of how an expert system looks like. Once you start creating such a system, you will only notice some *intelligent* behaviour from it once you reach certain number of rules, around 200+. At some point, rules become too complex to keep all of them in mind, and at this point you may start wondering why a system makes certain decisions. However, the important characteristics of knowledge-based systems is that you can always *explain* exactly how any of the decisions were made.