Difference between revisions of "Modeling Techniques for Self-Organizing Systems"

Revision as of 14:43, 15 July 2009

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)