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Multi-Agent Coalition Formation for Long-Term Task or Mobile Network. Hsiu-Hui Lee and Chung- Hsien Chen. Proposal. Propose a new architecture which integrates case-based reasoning, negotiation , and reinforcement learning to improve the coalition formation process

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multi agent coalition formation for long term task or mobile network

Multi-Agent Coalition Formation for Long-Term Task or Mobile Network

Hsiu-Hui Lee and Chung-Hsien Chen

proposal
Proposal
  • Propose a new architecture which integrates case-based reasoning, negotiation, and reinforcement learning to improve the coalition formation process
  • Suit for executing long-term task or for accomplishing a task in high mobility networks
ubiquitous and mobile networks
Ubiquitous and mobile networks
  • Ubiquitous networking system was designed by A group of researchers at AT&T Laboratories Cambridge
  • Several devices that have network capability communicate each other to achieve a common goal
  • E.g. locate a person at a building, connection to a personal computer via several devisors
case based reasoning
Case-based reasoning
  • Use to obtain the past coalition case
  • Fuzzy match mechanism -> find similar task in the past
  • If similar case found:
    • Sending looking up request to peer agents who are belong to the solution set in the past
    • If found resources: negotiate
  • If not enough resources found among peers
    • Broadcast requests to search agents who have resources
leaving rate
Leaving Rate
  • The leaving rate of peer agents indicates the probability that peer agents disappear
negotiation
Negotiation
  • Processes in continuous rounds
  • Each round: the agent makes a proposal and send it to the peer agents
    • The peer agent checks the proposal whether it can be accepted or not
  • Strategies:
    • Linear strategy: dropping to its limitation steadily
    • Tough strategy: dropping to its limitation immediately when deadline approaches
negotiation1
Negotiation…
  • The linear strategy -> low leaving rate agents
    • Increases the successful probability of negotiating
  • Tough strategy -> high leaving rate agents
    • More agents with low leaving rate and lesser agents with high leaving rate
negotiation2
Negotiation…
  • About rewards
    • Closer to the idle value -> higher probability to agree
  • Partially formed coalition doesn’t has enough resources, but the system has
  • Agent leave an acting coalition -> fail execute task
reinforcement learning
Reinforcement learning
  • machine learning mechanism
    • An agent perceives the current state to takes action
  • Agent collect experience for better coalition formation
  • For a given goal the computer learns how to achieve the goal by trial-and-error
  • They don’t use this method
temporal difference learning
Temporal difference learning
  • Learning rate – higher, more experience
  • Remove reward made by uncertain agents
  • Similar task in the past