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Artificial Intelligence in Networking Ant Colony Optimization PowerPoint Presentation

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Slide 1 - Artificial Intelligence in Networking: Ant Colony Optimization Matthew Guidry
Slide 2 - Ant Colony Optimization Ants have developed a technique for getting from one point to another this must be efficient this must have the ability to adapt
Slide 3 - Ants aren’t THAT Dumb Ants have evolved techniques for getting to a goal quickly and ways to resolves conflicts when a path is blocked.
Slide 4 - Application in Computer Science Researchers try to apply this in Artificial Intelligence to routing the Internet.
Slide 5 - Types of Routing in the BGP Border Gateway Protocol connects the Global Internet There are types of routing algorithms in the Border Gateway Protocol Circuit Switching Packet Switching
Slide 6 - Circuit Switching Comparable to a telephone call: Make call Receiver picks up Transmission is made (no one else can talk to you that time) It is agreed to end the call Both parties hang up
Slide 7 - Packet Switching Much less organized Packets are not forced to follow the same path - The next node for a packet is determined at each hop Packets are not guaranteed to arrive in a particular order
Slide 8 - Application Circuit Switching Must have knowledge of the layout of the entire network Determines a path before packets are sent, and then sends all packets along that path Packet Switching Does not need knowledge of the entire network Packets determine next hop at each stop A.C.O. is most effective in enhancing Packet Switching but is effective for both
Slide 9 - Ant Colony Optimization Uses very little state and computations Piggy-backs an ant upon a packet that travels the network There are two types of ants in this system Regular Ant Uniform Ant
Slide 10 - Regular Ants Use already established forwarding tables when routing Will take a certain route based on probabilities which increase as a good route is chosen more Will eventually converge to one path Direct packets to the most efficient route, only contain a smaller amount of Artificial Intelligence
Slide 11 - Uniform Ants These are the unbiased ants by forwarding probabilities Explore all paths and report back the times Uniform ants do not need a destination since they only explore the network and report the times. Not all nodes may be know to the host.
Slide 12 - Bad News Travels Fast “Good news travels slow, bad news travels fast.” When a line goes down the algorithm quickly finds a new best path. However, if the currently used path is surpassed by another path it takes a bit longer for the probabilities to correct.
Slide 13 - A.C.O. vs other Algorithms The 2 main Algorithms used by the B.G.P. are Link State (Circuit Switching) and Distance Vector (Packet Switching) A.C.O. requires much less state to be held at each router Ants can be piggy-backed on top of other packets, so this required much less bandwidth than other strategies.
Slide 14 - Citations Ants and reinforcement learning: A case study in routing in dynamic networks (1997) by Devika Subramanian,Peter Druschel,Johnny Chen Proceedings of the Fifteenth International Joint Conf. on Arti Intelligence Website: ” http://www.codeproject.com/KB/recipes/Ant_Colony_Optimisation.aspx” Lawrence Botley, 2008 Website: ” http://www.sciencedirect.com/science?_ob=ArticleURL “ Sara Morin, Caroline Gagné, and Marc Gravel, 2008
Slide 15 - Fin. Any Questions? ~ Matthew Guidry
 

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