Update

docs/concept.md

@@ -31,7 +31,7 @@ Examples of CIDs:
 - `Apple`
 - `Color`
 - `EntityWithDescription{description=EntityDescription,entity&=Entity}`
-- `EntityWithDescription{description=ColorRed,entity&=Apple}```
+- `EntityWithDescription{description=ColorRed,entity&=Apple}`
 - `EntityWithDescription{description=RedColor,entity&=EntityWithDescription{description=BigSize,entity&=Apple}}`
 
 ## Hierarchical Relations of Concepts

docs/pattern_matching.md

@@ -12,8 +12,7 @@ People think about the world in terms of abstract concepts because it substatual
 This process of simplification is called pattern matching because the agent is looking for abstract concepts in the raw signals, trying to find known patterns in the data. If we didn't have pattern matching, then we would need to work with the raw data instead of its simplified representation, which is a lot more resource-intensive.
 
 ## Ambiguity
-The biggest challenge of pattern matching is ambiguity. Text is simpler to work with, so let's start with it.  
-Well known set of sentences showing problems of ambiguity and common-sense knowledge was made by Terry Winograd, it is called [Winograd Schemas](https://cs.nyu.edu/~davise/papers/WSOld.html).  
+The biggest challenge of pattern matching is ambiguity. Text is simpler to work with, so let's start with it. Well known set of sentences showing problems of ambiguity and common-sense knowledge was made by Terry Winograd, it is called [Winograd Schemas](https://cs.nyu.edu/~davise/papers/WSOld.html).  
 Let's take one sentence from it - "Paul tried to call George on the phone, but he wasn't available." Who was not available?  
 The question seems obvious to people, but computers don't know how the process of calling someone works, so without this knowledge it's impossible to understand the sentence.  
 There are different kinds of ambiguity, but mostly they arise from the simplifications people make because they assume that everyone has the same commonsense knowledge.

docs/world_model.md → docs/world_model/entity.md

@@ -1,7 +1,8 @@
 ---
-title: World Model
+title: Entity
 layout: default
-nav_order: "50"
+parent: World Model
+nav_order: "10"
 ---
 
 # World Model

docs/world_model/index.md

@@ -0,0 +1,11 @@
+---
+title: World Model
+layout: default
+nav_order: "50"
+has_children: true
+---
+
+# World Model
+
+## Entity
+To effectively process incoming events, generate novel knowledge, and reason about the environment, an agent requires a robust internal representation of the world. Within the present framework, the agent's world model encompasses entities, each resembling a class instance from Object-Oriented Programming (OOP). Each entity comprises a concept and an assortment of fields. The concept defines which fields it can have and their default value if it's predent. For instance, a conceptualization of a human might incorporate fields such as "name", "age", and "gender". Entities don't have to provide values for all fields.