Multiagent Simulation forms an important application area of agent technology with a set of particular issues to be solved. Multiagent Simulation is not just studying systems consisting of autonomous actors in rich environments, but can also be used as a step in agent-based software engineering for testing agent-based software designs. This course will approach Multiagent Simulation from a computer science point of view. It introduces the basic concepts, categories of multi-agent models, respectively specialties in contrast to multiagent systems, as well as in relation to other forms of modeling and simulation. We will deal with principles and recurring pattern for model design and discuss methodologies for developing models of complex systems. More or less well-known example models will accompany the tutorial.
- What is multiagent simulation?
- Generative simulation and other basic concepts
- Related modeling and simulation paradigms: Cellular Automata, Object-oriented Simulation…
- Differences and shared features to multi agent systems
- A little bit of history
Principles and Pattern for Model Development
- Swarms, Flocks, Schools, Formations
- From random walk processes to food web models
- From agent interaction to emergent organization
- More on feedback loops
- Networks as environmental models
- Robustness and Heterogeneity
- What is done in standard simulation?
- Methodologies inspired by AOSE and beyond
- Modeling individual agents
- Qualitative Guidance
- Technicalities from simulation platforms to update regimes
Examples from different domains Current Developments and Trends
- Multilevel Simulation
- Learning Agents for Simulation
- Augmenting virtual reality