pages
forums
learning outcomes
The course is aimed at illustrating the main computational paradigms, models, technologies and methods for the engineering of autonomous systems. In particular, the courses focuses on the following themes, by experimenting with the corresponding case studies:
- the notion of autonomy in software systems and in artificial systems in general
- case: Autonomic Computing
- intelligence & autonomy: intelligent agents
- case: Web Intelligence, individual & social intelligence in MAS
- adaptivity & self-organisation
- cases: Pervasive Systems, Swarm Intelligence, Stigmergy Coordination, stochastic systems
course contents
The concept of autonomy in software systems and in artificial systems in general
- Autonomy in biology, philosophy, sociology, law
- The different meanings of autonomy in artificial systems and software systems
- Case: Autonomic Computing
Intelligence and autonomy in software systems
- Intelligent agents
- Case: architectures for intelligent agents
- Intelligent agent systems
- Cases: coordinated systems, agents and artifacts, Web Intelligence, Workflow Management, electronic Institutions
- Social and collective intelligence
Cases: Swarm Intelligence, Stigmergy Coordination, stochastic systems - Adaptability and self-organisation
- Cases: Pervasive Systems, Self-Organising Coordination
Technologies for autonomous systems
- Logical agents in tuProlog
- Intelligent agents in JADE, Jason and CArtAgO
- Workflow, coordinated, adaptive, stochastic, and self-organising systems in TuCSoN e ReSpecT
teaching methods
- Lessons with slides
- Examples discussed and built by the teacher and by experts from the field
- Lab activity
assessment methods
The assessment of the learning achievements is organised around the development of an individual/group project, which results in the production of suitable artefacts, either documental or software.
The final test consists in the oral discussion of the project.
course series
Intelligent Systems Engineering 2024/2025 •