Where can I study agent-based modeling

Modeling and simulation

BSc Computer Science, German

Modeling and simulation play a central role in almost all scientific and engineering disciplines. In computer science, too, modeling and simulation are central to developing autonomous, concurrent, self-organizing software. Methods and tools need to be developed to meet the challenges of these different areas of application. The lecture gives an overview of basic methods and techniques of modeling, the efficient execution of simulation models, and the design of simulation experiments.

Extract from the table of contents:

  • Dynamic systems: discrete-step-by-step, discrete-event-oriented and continuous
  • Formal simulation models: syntax and semantics
  • Modeling approaches: cellular automata, discrete-event-oriented system specification (DEVS), and hybrid automata
  • Dealing with Uncertainty: Stochastic Petri Nets and Process Algebras
  • Efficient execution: data structures and parallel, distributed simulation algorithms
  • Experiment design: input and output analysis

Further information: 3V + 1Ü, 6LP, SS

Parallel and distributed event-oriented simulation

MSc Computer Science, english

Parallel and distributed simulation methods are of particular interest when simulating complex and large systems, e.g., to evaluate routing protocols in networks with millions of nodes, to monitor air traffic online, or to predict the spread of epidemics. This module provides knowledge of parallel, distributed algorithms for executing discrete event models efficiently.

Extract from the content:

  • Null message algorithm
  • Deadlock detection and recovery
  • Time warp algorithm
  • Global virtual time and transient messages
  • Incremental state saving
  • Reverse computation
  • Exploiting GPUs

Further information: 3L + 1E, 6 LP, SS

Intelligent software agents

BSc Computer Science, German

The lecture focuses on the question of how software agents can make decisions about target-oriented actions in dynamic environments.
In addition to methods of artificial intelligence in the form of knowledge representation (e.g. modal logics), planning (e.g. distributed planning), and machine learning, especially enhanced learning, concepts from related areas e.g. linguistics (e.g. speech acts) and game theory (e.g. Nash Equilibrium and Pareto Optimum) and their importance for the design of autonomous, intelligent software agents. A project accompanies the lecture in order to deepen the concepts learned in practice.

Extract from the table of contents:

  • Beliefs Desires Intentions: On the Architecture of Deliberative Agents
  • He knows that he knows not: the role of modal logic
  • Markov Decision Making Processes: Reinforcement Learning for Multi-Agent Systems
  • The Prisoners Dilemma and Equilibria: Game Theory
  • Communication: From Speech Acts to ACL
  • Negotiation strategies: between consensus and deception
  • Reputation: First and second hand experience
  • Planning: Distributed plan generation or execution

Further information: 3V + 1Ü, 6 CP, WS

Data-Driven Modeling and Simulation

MSc Computer Science, english

Data play a central role in modeling and simulation.
To calibrate and validate a simulation model, a multitude of different simulation experiments can be executed which rely on various data.
At the same time, these simulation experiments may reveal important information about the data.
The lecture gives an overview about experiment design methods, data analysis methods, and about different types of simulation experiments, including sensitivity analysis, statistical model checking, optimization, parameter estimation and uncertainty quantification.
The lecture includes a student project in which a simulation model shall be re-developed from literature.
The developed simulation model as well as the data shall be extensively probed by analysis and extensive simulation experiments.

Extract from the content:

  • Work smarter not harder: experiment design methods
  • SESSL: a domain-specific language for specifying and executing simulation experiments in Scala
  • Optimization: more than hill climbing
  • Making hypotheses explicit: the virtue of statistical model checking
  • Bayesian: statistical parameter estimation and uncertainty quantification

Further information: 2L + 2E, 6 LP, WS