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

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** Game theory (high complexity once N>2)
 
** Game theory (high complexity once N>2)
 
** Swarm behaviour / intelligence (scalable, but has limited modeling capabilities)
 
** Swarm behaviour / intelligence (scalable, but has limited modeling capabilities)
 +
 +
==Group Work - Group 4==
 +
 +
* Modeling Techniques
 +
** Use differential/difference equations to model Self Organizing Systems that correlate at some point e.g. for synchronization
 +
*** Lyapunov-Exponent – evaluate how different start points change the systems evolution through a phase space
 +
*** Sensitivity to the initial conditions of the differential equations
 +
** (Hidden) Markov models
 +
* Properties
 +
** Emergent properties are often not easy to measure/understand
 +
** Sensitivity to the initial conditions of the differential equations might help with adaptivness
 +
** Adaptivness/goal-orientedness: in Markov models we can remove some nodes and re-evalute. Can give us an idea about effectiveness etc.
 +
* Limitations/Potentials
 +
** “classical methods” are well researched and evaluated but not specifically tailored towards SOS
 +
** very hard to measure

Revision as of 16:00, 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
    • Genetic 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)

Group Work - Group 4

  • Modeling Techniques
    • Use differential/difference equations to model Self Organizing Systems that correlate at some point e.g. for synchronization
      • Lyapunov-Exponent – evaluate how different start points change the systems evolution through a phase space
      • Sensitivity to the initial conditions of the differential equations
    • (Hidden) Markov models
  • Properties
    • Emergent properties are often not easy to measure/understand
    • Sensitivity to the initial conditions of the differential equations might help with adaptivness
    • Adaptivness/goal-orientedness: in Markov models we can remove some nodes and re-evalute. Can give us an idea about effectiveness etc.
  • Limitations/Potentials
    • “classical methods” are well researched and evaluated but not specifically tailored towards SOS
    • very hard to measure
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