Emergent algorithm: Difference between revisions
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== See also == |
== See also == |
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* [[AI alignment]] |
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* [[Artificial intelligence detection software]] |
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* [[Emergence]] |
* [[Emergence]] |
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* [[Evolutionary computation]] |
* [[Evolutionary computation]] |
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Latest revision as of 16:25, 18 November 2024
An emergent algorithm is an algorithm that exhibits emergent behavior. In essence an emergent algorithm implements a set of simple building block behaviors that when combined exhibit more complex behaviors. One example of this is the implementation of fuzzy motion controllers used to adapt robot movement in response to environmental obstacles.[1]
An emergent algorithm has the following characteristics: [dubious – discuss]
- it achieves predictable global effects
- it does not require global visibility
- it does not assume any kind of centralized control
- it is self-stabilizing
Other examples of emergent algorithms and models include cellular automata,[2] artificial neural networks and swarm intelligence systems (ant colony optimization, bees algorithm, etc.).
See also
[edit]References
[edit]- ^ Emergent behaviors of a fuzzy sensory-motor controller evolved by genetic algorithm, Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on (Volume: 31, Issue: 6)
- ^ Brunner, Klaus A. (2002). "What's emergent in Emergent Computing?" (PDF). Cybernetics and Systems 2002: Proceedings of the 16th European Meeting on Cybernetics and Systems Research. Vol. 1. Vienna. pp. 189–192. Archived from the original (PDF) on 2011-07-23. Retrieved 2009-02-18.
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