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Agent-Organized Networks for Dynamic Team Formation

Agent-Organized Networks for Dynamic Team Formation. Gaton, M.E. and desJardins, M., In Proceedings of AAMAS-2005 , pp. 230-237. Seo, Young-Woo. Introduction. Agent-Oriented Network (AON)

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Agent-Organized Networks for Dynamic Team Formation

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  1. Agent-Organized Networks for Dynamic Team Formation Gaton, M.E. and desJardins, M., In Proceedings of AAMAS-2005, pp. 230-237. Seo, Young-Woo

  2. Introduction • Agent-Oriented Network (AON) • An organizational network structure, or agent-to-agent interaction topology that is the result of local rewiring decisions made by the individual agents in a networked multi-agent system. • Goal of the individual agents: • Increase the collective performance of the agent organization by rewiring of an initial (arbitrary) network topology instead of creating and removal of connections • Strategy Design Issues • Local estimation of global performance • When/How to perform network adaptation (or rewiring) • Effectiveness of a Strategy • Learning rate, stability, global structure

  3. Dynamic Multi-Agent Formation • A Simple Model of Multi-Agent System • Team of agents forms a structure (or a topology) on the fly in decentralized fashion • Individual decision making is based on local information • Tasks are generated periodically and broadcasted globally

  4. uncommitted committed active • si: States of an individual agent ai on the • team formation • uncommitted: available, but not assigned to any task • committed: assigned to a task, • but the team works on the skill fulfillment on a task • active: the team is working on the task

  5. A valid team is a set of agents that induce a connected subgraph of the agent Social network and whose skill set fulfills the skill requirements for a given task. An uncommitted agent is only eligible to a task either initiate a team or join a team 1 2 i |Mk|

  6. Rewiring Strategies (1/2) • Structure-based • Preferential attachment • “Rich-Get-Richer”: A connectivity phenomenon observed in a scale-free network • Probability of connecting to a given node in a network is proportional to that node’s degree

  7. Rewiring Strategies (2/2) • Performance-based • Consider the local performance and referral • Rewire if the local performance is below the average of its immediate neighbors’ performance measures • Disconnect from the neighbor that has the lowest performance • Establish connection to neighbor with the highest performance by requesting referrals

  8. Experiments • Setting • Random geographic graph • Randomly placing N agents in the unit square and connecting two agents if they are within a predefined distance d • Actions of an individual agent • Adapt the network / join teams • Parameters • N=100,  =  = σ = |T|=10, μ=2

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