Difference between revisions of "Modeling Techniques for Self-Organizing Systems"
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* Evolutionary Algorithms | * Evolutionary Algorithms | ||
| − | * | + | * Genetic Algorithms |
** Efficient search through high dimensional state | ** 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) | ** However: Need to specify a "fitness landscape" a priori (need to know what to find a good solution to) | ||
Revision as of 15:50, 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)
