Artificial and Evolutionary IntelligenceModule Artificial and Swarn Intelligence
Academic Year 2026/2027 - Teacher: MARIO FRANCESCO PAVONEExpected Learning Outcomes
The course aims to explore the new AI frontiers on distributed and self-organizing methodologies, focusing on intelligent agents inspired by natural collective behavior. Specifically, it aims to provide a theoretical and practical understanding of how to design, model, and analyze intelligent systems, composed of multiple autonomous entities that learn and interact wiht each other and the environment to achieve common goals.
The goal of the course is to provide to each student:
1) good knowledge on the basic concepts;
2) good knowledge on design and modeling agent-based models, and collective behaviors;
3) excellent ability to design robust, flexible and scalable algorithms.
Course Structure
Classroom-taught lessons. Can be also included external seminars held by expert researchers on related topics.
Should teaching be carried out in mixed mode or remotely, it may be necessary to introduce changes with respect to previous statements, in line with the programme planned and outlined in the syllabus.
Learning assessment may also be carried out on line, should the conditions require it.
Required Prerequisites
Attendance of Lessons
Detailed Course Content
This course investigates the theoretical and computational principles of collective intelligence, exploring how intelligent dynamics emerge from the interaction of many autonomous entities. Through agent-based models, multi-agent systems, and swarm intelligence, students will acquire the tools to design, simulate, and analyze decentralized systems capable of making decisions, adapting to their environment, and solving complex problems without centralized control.
Course content:
- Complex Adaptive Systems
- Agent-Based Modeling
- Multi-Agent Systems
- Collective Decision Making
- Swarm Intelligence
- Emerging Directions
Textbook Information
- M. Mitchell, "Complexity: A Guided Tour", Oxford University Press, 2009
- S.F. Railsback, and V. Grimm, "Agent-Based and Individual-Based Modeling: A Practical Introduction (2nd ed.)", Princeton University Press, 2019
- U. Wilensky and W. Rand, “An introduction to Agent-Based Modeling: modeling, natural, social and engineered complex systems with NetLogo”, MIT Press, 2015
- M. Wooldridge, "An Introduction to MultiAgent Systems (2nd ed.)", John Wiley & Sons, 2009
- G. Weiss, “Multiagent Systems”, MIT Press, 2013
- J.H. Miller, and S.E. Page, "Complex Adaptive Systems: An Introduction to Computational Models of Social Life", Princeton University Press, 2007
- H. Iba, “Agent-Based Modeling and Simulation with Swarm”, CRC Press, 2013
- Eric Bonabeau, Marco Dorigo, and Guy Theraulaz, “Swarm Intelligence: From Natural to Artificial Systems”, Santa Fe Institute Studies on the Sciences of Complexity, 1999.
- K.S. Kaswan, J.S. Dhatterwal and A. Kumar, "Swarm Intelligence: An Approach from Natural to Artificial”, Wiley, 2023
Course Planning
| Subjects | Text References | |
|---|---|---|
| 1 | Complex Systems and Emergent Intelligence: complex adaptive systems; self-organization; positive and negative feedback; adaptation; robustness and resilience; biological and artificial collective systems; emergent intelligence | cap. 1, 2, 12, 14, Melanie Mitchell, Complexity: A Guided Tour - cap. 1, 2, Bonabeau, Dorigo, Theraulaz, Swarm Intelligence: From Natural to Artificial Systems - Supplementary material provided by the lecturer. |
| 2 | Agent-Based Modeling and Simulation: agent-based modeling (ABM); agents, environments, interactions; time and scheduling; cellular automata; agent-based simulation; emergence in agent populations; verification and validation of simulations | cap. 1-9, Railsback & Grimm Agent-Based and Individual-Based Modeling - Supplementary material provided by the lecturer. |
| 3 | Multi-Agent Systems: intelligent agents; reactive and deliberative agents; agent architectures; communication protocols; coordination mechanisms; cooperation and competition; negotiation; distributed problem solving; task allocation; organizational structures. | cap. 1-9, Michael Wooldridge An Introduction to MultiAgent Systems (2nd Edition) - Supplementary material provided by the lecturer. |
| 4 | Collective Decision Making: collective intelligence; collective decision making; consensus formation; rule dynamics; opinion dynamics; information cascades; influence and social contagion; wisdom of crowds; distributed consensus. | cap. 2, 4, 5, 8, 9, Miller & Page, Complex Adaptive Systems: An Introduction to Computational Models of Social Life - cap. 1, 4, James Surowiecki The Wisdom of Crowds - Supplementary material provided by the lecturer. |
| 5 | Swarm Intelligence: Natural Swarms (ant colonies; bee colonies; flocking; schooling; stigmergy); Artificial Swarms (Ant Colony Optimization (ACO); Particle Swarm Optimization (PSO); Artificial Bee Colony (ABC)); Swarm engineering principles | cap. 3-6, Bonabeau, Dorigo, Theraulaz Swarm Intelligence: From Natural to Artificial Systems - cap. 1-8, Iba, “Agent-Based Modeling and Simulation with Swarm”, CRC Press, 2013. - Supplementary material provided by the lecturer. |
| 6 | Emerging Directions in Collective Artificial Intelligence: human-agent collectives; digital societies; computational social systems; socio-technical systems; collective intelligence in online platforms; swarm systems and smart cities; ethical and societal implications of collective intelligent systems. | cap. 1, 2, 6, 7, Thomas Malone, Superminds - Supplementary material provided by the lecturer. |
Learning Assessment
Learning Assessment Procedures
The evaluation is based as follows:
THEORY TEST: written test relating to the topics covered by the course.
PROJECT: developing an agent-based model and/or a swarm intelligence algorithm able to investigate and/or solve a given complex problem.
ORAL INTERVIEW: oral discussion on course theoretical topics and the project developed.
Students with disabilities and/or DSA must contact the teacher, the CInAP representative of the DMI and CInAP well in advance of the exam date to communicate that they intend to take the exam using the appropriate compensatory measures.
To take the final exam, you must have booked on the SmartEdu portal. For any technical issues regarding your booking, please contact the Student Service Office.