Modeling Techniques for Self-Organizing Systems

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Modeling the Structure, Dynamics and Quantification - Group 2

Looked at modeling the topology and connections between agents

Techniques for dynamic processes (microscopic rules of behaviour) and their strengths/weaknesses regarding robustness.

  • Cellular Automaton
    • Easy to form, broad range of patterns
    • But: Dependence on synchronization
  • Evolutionary Algorithms
  • Generic Algorithms
    • Efficient search through high dimensional state
    • However: Need to specify a "fitness landscape" a priori (need to know what to find a good solution to)
    • History dependence (converges to different solutions)
    • Good, but no optimal solutions
    • Discussion mentioned that this may be more design than modeling (since it designs the solution)
  • UML (Modelling specification language)
  • Finite State Machines / Hidden Marcov Models
    • Flexible
    • Model at the system level (macroscopic)
  • Control Theory
    • Real-Time, but has some centralized, global requirements
  • Agent-based approaches
    • Local synch. algorithms
    • Local geometric constructions
    • Game theory (high complexity once N>2)
    • Swarm behaviour / intelligence (scalable, but has limited modeling capabilities)