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- knowledge-sharing: This specifies the type of knowledge sharing the agents employ while walking around the network. The types of knowledge-sharing is as follows:
"None": The agents do not exchange any knowledge with other agents. As a result, the agents end up randomly walking around the network.
"Word-of-mouth": If the agents have found the goal, then they share their knowledge with other agents they meet while returning to their start destination.
"Blackboard": The agents share their knowledge by writing what they know into communal
“blackboards” that are found at each node. This can then subsequently be read by agents who visit the node latter on.
"Combined 1" and "Combined 2": These combine the knowledge of the word-of-mouth and blackboard approaches in slightly different ways to create two new hybrid knowledge sharing schemes. The first scheme checks the blackboard first, then defaults to the word-of-mouth scheme if no knowledge exists in the blackboard. The second scheme does the reverse – it will apply word-of-mouth knowledge first before defaulting to the blackboard knowledge.
- network-type: This selects the type of network that is created when the setup-network button is pressed. The type of networks are as follows:
"P2P-no-super-nodes": This simulates a peer-to-peer network with no super-nodes.
"P2P-has-super-nodes": This simulates a peer-to-peer network with super-nodes.
"P2P-random-single-link": This simulates a peer-to-peer network where each node has only one link with another random node.
"P2P-incremental": This creates the simulated peer-to-peer network by incrementally building the links to other random nodes one at a time.
"P2P-incremental-1": This creates the simulated peer-to-peer network by incrementally building links to other random nodes.
"Star-central-hub": This creates a network with one node (the Kevin Bacon node; i.e.
the goal node) as the central hub and all other nodes linked to it and not to any other node.
"Hierarchical": This creates a tree network with the Kevin Bacon goal node at the root.
- layout-type: This specifies how the network should be laid out when it is visualised.
- no-of-nodes: This is the number of nodes to place in the network.
- no-of-super-nodes: This is the number of super-nodes to place in the network. (These are nodes that usually will have significantly more links than standard nodes as specified by links-per-super-node slider).
- links-per-node: This specifies the maximum number of links a standard node will have.
The actual number chosen for a particular node will be a random number between 1 and this number.
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- links-per-super-node: This specifies the maximum number of links a super-node will have. The actual number chosen for a particular super-node will be a random number between 1 and this number.
- simulation-ticks: This specifies how long the simulation should run for.
- network-update: If non-zero, this will cause the network to be updated at an interval according to a random number from 0 up to the value of this slider.
- nodes-to-add: The number of nodes specified by this slider will be added to the network when the next update occurs according to the network-update slider.
- nodes-to-delete: The number of nodes specified by this slider will be deleted from the network when the next update occurs according to the network-update slider.
- new-walkers-per-tick: This sets the number of walker agents that are added each tick of the simulation.
- time-to-live: This sets how long the walker agents should remain active if they have not found the goal node before being killed off.
- new-walkers-per-node: This sets the number of walkers that are created to continue the search in multiple different directions when each walker visits each node. Setting this number higher than 1 will quickly flood the network in most circumstances.
- percent-in-both: When the network is created, a number of nodes will be created that are connected to the Paul Erdos node (i.e. a path exists that will eventually lead to Paul Erdos), and a number will be created that are connected to Kevin Bacon. This slider controls how many of the nodes will be doubly connected (i.e. paths exist from the node that will lead to both the Paul Erdos and Kevin Bacon nodes).
The model’s Interface plots monitors (shown on the right) are defined as follows:
- Number of walkers: This plot shows the number of walkers per tick.
- Successful searches: This plot shows the number of successful searches (i.e. walkers that have found the goal node) per tick.
- Shortest path distances: This plot is plotted when the go-dijkstra button is pressed. It plots the shortest path distances from each node to the goal node.
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- #Nodes: This monitor reports the number of nodes in the network. It will change if the network is updated when the network-update, nodes-to-add and nodes-to-delete sliders are all non-zero.
- #Walkers: This monitor reports the number of active walker agents in the network.
- #Visited: This monitor reports the number of nodes visited by the walker agents.
- %Success: This monitor reports the percentage of walker agents that have been successful in reaching the goal node.
THINGS TO NOTICE
Notice how well the agents perform at finding the goal node when the knowledge sharing method is set to None. Compare this with the other knowledge sharing methods.
Notice that the blackboard knowledge sharing mechanism usually outperforms the word of mouth mechanism in terms of the percentage of successful searches.
Notice that pre-loading the blackboards with shortest path information gained from execution of Dijkstra’s algorithm can significantly improve the search.
No. 1 in Modern Energy