Difference between revisions of "Modeling Techniques for Self-Organizing Systems"
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==Modeling the Structure, Dynamics and Quantification== | ==Modeling the Structure, Dynamics and Quantification== | ||
| − | Techniques for dynamic processes (microscopic rules of behaviour) | + | 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 | * Cellular Automaton | ||
| + | ** Easy to form, broad range of patterns | ||
| + | ** But: Dependence on synchronization | ||
* Evolutionary Algorithms | * 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 modelling (since it designs the solution) | ||
* UML (Modelling specification language) | * UML (Modelling specification language) | ||
* Finite State Machines / Hidden Marcov Models | * 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 modelling capabilities) | ||
Revision as of 14:40, 15 July 2009
Modeling the Structure, Dynamics and Quantification
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 modelling (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 modelling capabilities)
