LEARNING OUTCOMES

The goal of the course is to enable students to understand the Ambient Intelligence computing paradigm, which envisions a world where people (and possibly robots) are surrounded by intelligent sensors/actuators and interfaces embedded in the everyday objects around them.

AIMS AND LEARNING OUTCOMES

At the end of the class, the student will be able to:

• understand characteristics and problems related to Ambient Intelligence applications, and their relations to other areas, including IoT, AI and Robotics;
• understand the methodologies and technological tools for the design of Ambient Intelligence applications;
• expand the acquired knowledge to understand how to use additional methodologies and tools that have not been presented in the class;
• apply methodology and tools to solve problems, in particular for the design of Ambient Intelligence applications.

PREREQUISITES

There are no prerequisites.

TEACHING METHODS

The class includes both lessons and computer exercises. Attendance is warmly encouraged, especially concerning exercises. During the semester, assignments will be given that will be evaluated for the exam.

SYLLABUS/CONTENT

The syllabus includes the following topics:

• Ambient Intelligence
  • Basic principles;
• Localization of persons and devices
  • Sensors for localization
  • Geometrical approaches 
  • Topological approaches
  • Probabilistic localization: Particle Filter
• Knowledge representation
  • Description logics
  • Ontologies: OWL and Protégé
  • SWRL rules
  • Bayesian Networks and Hidden Markov Models
• Context and Context Awareness
  • The Context Toolkit
  • Context Awareness with ontologies
  • Context Awareness with Bayesian Networks
• Middleware for Ambient Intelligence
• Executing Plans: AgentSpeak and Jason

RECOMMENDED READING/BIBLIOGRAPHY

Relevant material (technical reports, slides, papers) will be distributed by the teachers during the lessons