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DAgents Resource Allocation Model

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Money! Money! Money! MONEY! Money talks, just ask Donald Trump* Market Model ... Payments = money = spending potential. Why not apply same theory to agents? ... – PowerPoint PPT presentation

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Title: DAgents Resource Allocation Model


1
DAgents Resource Allocation Model
  • A Market-Based Model for Resource Allocation in
    Agent Systems

2
Introduction to DAgents
  • Mobile Agent system developed at Dartmouth
    College
  • Sponsored by DARPA (Defense Advanced Research
    Projects Agency) and several other military
    branches.
  • Used mobile agents on mobile devices for a test
    scenario.

3
Problems with Agent Systems
  • Why should a host allow a mobile agent to
    execute?
  • Hosting a mobile agent exposes host to risk of
    malicious agents
  • No incentive to host agents.
  • They cannot be trusted.
  • They are extra processes the host must
    accommodate.

4
How do we provide incentive?
  • We need something to provide an incentive
  • Money! Money! Money! MONEY!
  • Money talks, just ask Donald Trump

5
Market Model
  • We are paid for the labour and/or skills we
    provide our employers or clients.
  • Payments money spending potential
  • Why not apply same theory to agents?
  • Hosts provide a service to agents.
  • Agents provide services to each other and users.

6
Market System for DAgents
  • Agents purchase resource usage from hosts, or
    other agents.
  • Hosts then redistribute revenue to users.
  • Users spend on launching own agents.

7
Currency
  • Agent currency can range from symbolic
  • to real world tender.

8
Secure Transactions
  • Like real world trade, require verifiable
    currency.
  • DAgents implements a mediation protocol.
    (Similar to SafeTrader on Trademe).
  • Verification of the currency can verify the
    spender.

9
Market as a Security Measure
  • Protecting agents from agents tractable.
  • Protecting hosts from agents tractable.
  • Protecting agents from hosts difficult.
  • Tampering with an agent may dissuade agent from
    returning, thus discouraging income. Agents
    will not visit and host will not be able to
    launch agents.

10
Arbiter
  • Arbiter provides a trusted third party (i.e. CA
    for HTTPS)
  • Arbiter collects collateral and information from
    agents.
  • If both agents are satisfied, collateral is
    returned.
  • Otherwise, Arbiter retains collateral until
    resolved.

11
Resource Managers
  • Resource managers handle resource allocation on
    behalf of the host.
  • Allow system administrators to fine tune access
    to system resources.

12
Allocation Mechanism
  • Purpose of the mechanism is to prioritize rather
    than generate revenue.
  • Hosts solicit bids from agents to assess demand
    on system.
  • Assumes agent uses currency to complete its
    tasks.
  • Bid strategy transformed over domain common to
    all bidding agents.

13
Allocation Mechanism
  • Host calculates sum of competing bids and
    allocates a proportion of resources to each
    agent.
  • Provable that there is one bidding level
    satisfying all agents.
  • Game has Nash Equilibrium.

14
Experimental Results
  • Run on Swarm
  • Performance gradually decays as latency
    increases, despite having older information.
  • If load is too high, richer agents are given
    priority.
  • Karl Marx would not approve!

15
Experimental Results
  • Price is log-normally distributed, so price
    will be mostly stable with brief price spikes.
  • Agents will continue to enter system, so price
    will be high after spike.
  • More desirable to know how an agent will perform,
    so further work includes call options and agent
    utility Bredin, Kotz, Rus1999

16
Further Research and Papers
  • Utility Driven Mobile Agent Scheduling
  • Jonathan Bredin, David Kotz and Daniela Rus
    1999
  • http//agent.cs.dartmouth.edu/papers/bredindeman
    d.pdf
  • A Game-Theoretic Formulation of Multi-Agent
    Resource Allocation
  • Jonathan Bredin, Rajiv T. Maheswaran, Cagri
    Imer, Tamer Basar, David Kotz and Daniela Rus
    2000
  • http//agent.cs.dartmouth.edu/papers/bredingame.
    pdf

17
Appendix
  • Note I didnt actually ask, but I would assume
    so.
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