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At the end of the course, students get acquainted with the fundamental issues of intelligent systems, the most relevant computational models and technologies, and the most effective methods. In particular, students become familiar with the fittest solutions, languages, technologies, architectures, and methodologies to design intelligent systems, and are capable of:
devising the problems requiring artificial intelligence techniques for their solution;
determining the most proper conceptual and methodological approaches;
selecting and integrating the fittest technologies for implementing the solutions detected.
course contents
Case Studies
ChatGPT—Beyond the Turing Test
Autonomy in Biology
Programming Intentional Agents in AgentSpeak(L) & Jason
Natural Language Processing
General Issues of Intelligent Systems
Drivers for Intelligent Systems
On Autonomy. Concepts & Definitions
Agents for Intelligent Systems Engineering
Artificial Intelligence. A Bird’s Eye View
Automated Reasoning
Reasoning Agents
Logic & Computation
Planning for Intelligent Agents
Self-Organising Systems
Nature-Inspired Coordination & Self-Organisation
Interacting with Autonomous Systems: Conversational Informatics
Simulation & Multi-Agent Systems: An Introduction
Scientific Competences
Sources of Scientific Literature for Intelligent & Autonomous Systems
Systematic Literature Review. A Methodology for Scientific Surveys
Technologies for Intelligent Systems
Knowledge Representation
Inference
Perception and Actuation
Planning with STRIPS
Programming Intentional Agents: Exercises in Jason
eXplainable Artificial Intelligence (XAI): A Gentle Introduction
readings / bibliography
The bibliography is made available through the course web site.
teaching methods
Lessons with slides
Examples discussed and built by the teachers
Lab activity
assessment methods
Verification of lab activity
Presentation and discussion of an individual/group project
Intelligent Systems Engineering 2025/2026 • 
