Update README.md

Improved grammar and fixed usage guide link

Author: simon-kohaut

README.md

@@ -5,17 +5,17 @@
 # Probabilistic Mission Design
 
 This repository implements Probabilistic Mission Design (ProMis).
-ProMis allows the user to formalize their knowledge about local rules, such as traffic regulations, to constrain an agent's actions and motion. 
-To do so, we employ probabilistic first-order logic, a mathematical framework combining formal reasoning with probabilistic inference.
+ProMis enables users to formalize their knowledge of local rules, such as traffic regulations, to constrain an agent's actions and movements. 
+To achieve this, we employ probabilistic first-order logic, a mathematical framework that combines formal reasoning with probabilistic inference.
 This provides a weighted belief whether the encoded rules are satisfied for a state or action. 
 
 Using ProMis, we pave the way towards Constitutional Agents. 
-Such agents can give reason for their actions and act in a principled fashion even under uncertainty. 
-To this end, ProMis gives high-level, easy-to-understand, and adaptable control over the navigation process, e.g., to effortlessly integrate local laws with operator requirements and environmental uncertainties into logical and spatial constraints. 
+Such agents can give reasons for their actions and act in a principled fashion even under uncertainty. 
+To this end, ProMis provides high-level, easy-to-understand, and adaptable control over the navigation process, for example, to seamlessly integrate local laws, operator requirements, and environmental uncertainties into logical and spatial constraints. 
 
-Using ProMis, scalar fields of the probability of adhering to the agent's constitution across its state-space are obtained.
-These can then be utilized for tasks such as path planning, automated clearance granting, explaining the impact of and optimizing mission parameters.
-For instance, the following shows ProMis being applied in a diverse set of scenarios, with a high probability of satisfying all flight restrictions being shown in blue, a low-probability being shown in red, and unsuitable spaces being transparent. 
+Using ProMis, scalar fields of the probability of adhering to the agent's constitution are obtained across its state space.
+These can then be utilized for tasks such as path planning, automated clearance granting, explaining the impact of, and optimizing mission parameters.
+For instance, the following shows ProMis being applied in a diverse set of scenarios, with a high probability of satisfying all flight restrictions being shown in blue, a low probability being shown in red, and unsuitable spaces being transparent. 
 
 <p align="center">
   <img src="https://github.com/HRI-EU/ProMis/blob/main/images/landscapes.png"/>
@@ -24,7 +24,7 @@ For instance, the following shows ProMis being applied in a diverse set of scena
 # Usage
 
 To install ProMis, please follow the instructions [here](https://hri-eu.github.io/ProMis/installation.html).
-For an in-depth walkthrough on applying ProMis in your own applications, you can check our [usage guide](https://hri-eu.github.io/ProMis/usage.html).
+For an in-depth walkthrough on applying ProMis in your own applications, you can check our [usage guide](https://hri-eu.github.io/ProMis/notebooks/usage.html).
 An [interactive version](https://github.com/HRI-EU/ProMis/blob/main/examples/usage.ipynb) of the usage guide is also available in this repository. 
 
 # Cite