Intelligent Systems (ISs) are systems with the capability to sense the environment and react to it in order to attain given goals. Typically, intelligent systems have two main components: one focusing on learning the environment behavior on the basis of the acquired measurements (Machine Learning component) and another one focusing on planning the best course of actions given what has been learned so far (AI component).

Examples of intelligent systems are software controlling autonomous vehicles (e.g., cars, drones, a swarm of drones, satellites, vessels, robots, etc.); software controlling autonomous systems in general (e.g., bio-medical devices, trading robots, automatic video compression, decision support systems, etc.).

Intelligent systems are often mission-critical (i.e., their failure leads to a loss of money, e.g., a trading robot, a satellite, a swarm of drones) or
safety-critical (i.e., their failure may lead to a loss of human lives, e.g., bio-medical devices, autonomous driving)

Designing and verifying intelligent systems is very challenging because of the wide set of operational scenarios (environment behaviors) they are supposed to withstand and the fact that the system behaviors changes (through learning) as a function of the inputs received.

We will see that the design and verification of an intelligent system can be both casted as
(quite large) optimization problems that we will solve using a suitable blend of AI and Machine Learning (ML) techniques.

The proposed activities have the following goals:

1) Learn how to model ISs, their environment, and their requirements. Modelica will be used as an example of an open standard, widely used modeling language.

2) Learn how to use Statistics and ML techniques to automatically build an environment model from historical (sensor) data and background knowledge. Such a model will be used as an adversarial scenario generator to support the design and verification of ISs.

3) Learn how to use AI and ML techniques to develop Key Performance Indicators (KPIs) defining functional as well as non-functional requirements for ISs.

4) Learn how to use AI, ML and Statistical Model Checking techniques to support simulation-based design and verification of ISs.