Course Contents Introduction. Agents and Multiagent systems. Agent-based modelling architectures. Examples from air transportation.
Emergence in Multiagent systems. Agent-based simulation. Agent-based modeling and simulation tools.
Agent-based coordination, planning, and scheduling in air transportation.
Nature-inspired approaches to solve optimization problems. Swarm intelligence.
Adaptive behavior and learning in agent-based systems.
Collaborative decision making in air transportation. Negotiation, auctions, game-theoretic approaches.
Agent-based model analysis: sensitivity, uncertainty, robustness. Validation of agent-based models.
Study Goals The student has to be able:
- to formulate a practical air transportation problem as an agent or a multiagent system model;
- to identify appropriate agent-based methods and to apply them;
- to implement agent-based models;
- to perform agent-based simulation, interpret and analyze simulation results;
- to be able to apply agent-based optimization techniques
