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Consensus: Multi-agent Systems (Part1)


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Title: Consensus: Multi-agent Systems (Part1)

Consensus Multi-agent Systems (Part1)
  • Quantitative Analysis How to make a decision?

Thank you for all referred pictures and
  • Introduction
  • Definitions
  • Questions
  • Reaching Agreements
  • Auction
  • Task allocation
  • Auction algorithm

Multiagent Systems, a Definition
  • A multiagent system is one that consists of a
    number of agents, which interact with one-another
  • Swarm of Robots
  • Exchange information
  • Agents will be acting on behalf of users with
    different goals and motivations
  • Heterogeneous or Homogeneous
  • To successfully interact, they will require the
    ability to cooperate, coordinate, and negotiate
    with each other, much as people do

Multiagent Systems, a Definition
  • Why we apply multi-agent systems to solve the
  • A single agent cannot perform parallel tasks
  • Multi-agent can accomplish given tasks more

Swarm Intelligence
  • Application of Swarm Principles Swarm of
  • http//

Multiagent Systems (MAS)
  • Questions In Multiagent Systems
  • How can cooperation emerge in societies of
    self-interested agents?
  • What kinds of languages/protocols can agents use
    to communicate?
  • How can self-interested agents recognize
    conflict, and how can they reach agreement?
  • How can autonomous agents coordinate their
    activities so as to cooperatively achieve goals?

Multiagent Systems (MAS)
  • How to make a group decision among them? or How
    to achieve the group mission?
  • Find the optimal decision of group
  • Resolve conflicts among individuals
  • Maximize the overall performance of group

Multiagent Systems is Interdisciplinary
  • The field of Multiagent Systems is influenced and
    inspired by many other fields such as
  • Economics
  • Profit, Bargain
  • Game Theory
  • Strategy for decision making
  • Conflict and cooperation between decision-makers
  • Logic
  • Social Sciences
  • Leader, follower
  • Trust
  • This has analogies with artificial intelligence

Objections to MAS
  • Isnt it all just Distributed/Concurrent
    Systems?There is much to learn from this
    community, but
  • Agents are assumed to be autonomous, capable of
    making independent decision
  • they need mechanisms to synchronize and
    coordinate their activities at run time
  • Agents are self-interested, so their interactions
    are economic encounters

Objections to MAS
  • Isnt it all just AI?
  • We dont need to solve all the problems of
    artificial intelligence in order to build really
    useful agents
  • Classical AI ignored social aspects of agency.
  • These are important parts of intelligent activity
    in real-world settings

Social Ability
  • The real world is a multi-agent environment
  • Some goals can only be achieved with the
    cooperation of others
  • Similarly for many computer environments witness
    the Internet
  • Social ability in agents is the ability to
    interact with other agents via some kind of
    agent-communication language, and perhaps
    cooperate with others

Other Properties
  • mobility
  • the ability of an agent to move around an
    electronic network
  • veracity
  • an agent will not knowingly communicate false
    information (only true information)
  • benevolence
  • agents do not have conflicting goals, and that
    every agent will therefore always try to do what
    is asked of it (helps)
  • rationality
  • agent will act in order to achieve its goals, and
    will not act in such a way as to prevent its
    goals being achieved
  • learning/adaption
  • agents improve performance over time

Agents and Objects
  • Main differences
  • agents are autonomous
  • agents embody stronger notion of autonomy than
    objects, and in particular, they decide for
    themselves whether or not to perform an action on
    request from another agent
  • agents are smart
  • capable of flexible (reactive, pro-active,
    social) behavior, and the standard object model
    has nothing to say about such types of behavior
  • agents are active
  • a multi-agent system is inherently
    multi-threaded, in that each agent is assumed to
    have at least one thread of active control

Reaching Agreements
  • How do agents reaching agreements when they are
    self interested?
  • There is potential for mutually beneficial
    agreement on matters of common interest
  • The capabilities of negotiation and argumentation
    are central to the ability of an agent to reach
    such agreements

Definitions Negotiation and Argumentation
  • Negotiation (Compromise)
  • Dialogue between two or more parties
  • intended to reach an understanding
  • resolve point of difference
  • gain advantage in outcome of dialogue
  • to produce an agreement upon courses of action
  • to bargain for individual or collective advantage
  • tries to gain an advantage for
  • Argumentation
  • how conclusions can be reached through logical
  • Including debate and negotiation which are
    concerned with reaching mutually acceptable

Mechanisms, Protocols, and Strategies
  • Negotiation is governed by a particular
    mechanism, or protocol
  • The mechanism defines the rules of encounter
    between agents
  • Mechanism design is designing mechanisms so that
    they have certain desirable properties
  • Given a particular protocol, how can a particular
    strategy be designed that individual agents can

Mechanism Design
  • Desirable properties of mechanisms
  • Convergence/guaranteed success
  • Maximizing social welfare
  • Pareto efficiency
  • Individual rationality
  • Stability
  • Simplicity
  • Distribution

  • An auction takes place between an agent known as
    the auctioneer and a collection of agents known
    as the bidders
  • The goal of the auction is for the auctioneer to
    allocate the good to one of the bidders
  • Resource allocation
  • The auctioneer desires to maximize the price
    bidders desire to minimize price

Auction Parameters
  • Goods can have
  • private value
  • public/common value
  • correlated value
  • Winner determination may be
  • first price
  • second price
  • Bids may be
  • open cry
  • sealed bid
  • Bidding may be
  • one shot
  • ascending
  • descending

English Auctions
  • Most commonly known type of auction
  • first price
  • open cry
  • Ascending
  • Dominant strategy is for agent to successively
    bid a small amount more than the current highest
    bid until it reaches their valuation, then
  • Susceptible to
  • winners curse
  • shills

Dutch Auctions
  • Dutch auctions are examples of open-cry
    descending auctions
  • auctioneer starts by offering good at
    artificially high value
  • auctioneer lowers offer price until some agent
    makes a bid equal to the current offer price
  • the good is then allocated to the agent that made
    the offer

First-Price Sealed-Bid Auctions
  • First-price sealed-bid auctions are one-shot
  • there is a single round
  • bidders submit a sealed bid for the good
  • good is allocated to agent that made highest bid
  • winner pays price of highest bid
  • Best strategy is to bid less than true valuation

Vickrey Auctions
  • Vickrey auctions are
  • second-price
  • sealed-bid
  • Good is awarded to the agent that made the
    highest bid at the price of the second highest
  • Bidding to your true valuation is dominant
    strategy in Vickrey auctions
  • Vickrey auctions susceptible to antisocial

Lies and Collusion
  • The various auction protocols are susceptible to
    lying on the part of the auctioneer, and
    collusion among bidders, to varying degrees
  • All four auctions (English, Dutch, First-Price
    Sealed Bid, Vickrey) can be manipulated by bidder
  • A dishonest auctioneer can exploit the Vickrey
    auction by lying about the 2nd-highest bid
  • Shills can be introduced to inflate bidding
    prices in English auctions

Applying to Algorithms
  • Node is represented an agent
  • Edge indicates the corresponding agents that have
    to coordinate their actions
  • Only interconnected agents have to coordinate
    their actions at any particular instance

Task Allocation
  • Task Allocation Method in term of multi-agent
    system is given into two meanings for achieve
    the common goal involve one task or more than one
  • Task Allocation problem
  • The goal of task allocation is, given a list of n
    tasks and n agents, to find a conflict-free
    matching of tasks to agents that maximizes some
    global reward.
  • Behaviors of Task allocation
  • Commitment
  • Agent stay focus on a single task until the task
    is over
  • Opportunism
  • Agent can switch tasks if another task is found
    with greater interesting or priority
  • Coordination
  • Coordination is linked to communication, the
    ability of agents to communicate about who should
    service which task
  • Individualism
  • Agent have no awareness of each other.
  • Communication is used to prevent multiple agents
    from trying to accomplish the same task

Methods of Task Allocation
Methods of Task allocation Pros Cons
Centralized Methods Cheaper and easier to build the structure. Fit to manage tasks for each agent, then ease to work. Reduce conflict of actions. A single point of failure. Limited Bandwidth. Congestion of transportation.
Decentralized Methods No single point of failure Each of agent has capability to coordinate their actions by themselves. Conflict of assignment. Collecting information of each sub-decision making through the center.
Distributed Methods local information exchanging among neighbors Support Dynamic network topology Support Large-scale network No global information
Auction Algorithm
  • The auction algorithm is an iterative method to
    find a best prices and an assignment that
    maximizes the net benefit, for solving the
    classical assignment problem
  • Task assignment
  • m agents and n tasks, matching on one-to-one
  • Benefit cij (cost function) for matching agent i
    to task j
  • Assigning agents to tasks so as to maximize the
    total benefit
  • Agents place bids on tasks, and the highest bid
    wins assignment
  • A central system acting as the auctioneer to
    receive and evaluate each bid
  • Once all of bids have been collected, a winner is
    selected based on a predefined scoring metric
    (Bid Price)

Auction Algorithm
Auction Algorithm
  • Auctions are only concerned with the allocation
    of goods richer techniques for reaching
    agreements are required
  • Negotiation is the process of reaching agreements
    on matters of common interest
  • Any negotiation setting will have four
  • negotiation set possible proposals that agents
    can make
  • protocol
  • strategies, one for each agent, which are private
  • rule that determines when a deal has been struck
    and what the agreement deal is
  • Negotiation usually proceeds in a series of
    rounds, with every agent making a proposal at
    every round

Negotiation in Task-Oriented Domains
  • Imagine that you have three children, each of
    whom needs to be delivered to a different school
    each morning.
  • Your neighbor has four children, and also needs
    to take them to school.
  • Delivery of each child can be modeled as an
    indivisible task.
  • You and your neighbor can discuss the situation,
    and come to an agreement that it is better for
    both of you (for example, by carrying the others
    child to a shared destination, saving him the
  • There is no concern about being able to achieve
    your task by yourself.
  • The worst that can happen is that you and your
    neighbor wont come to an agreement about setting
    up a car pool, in which case you are no worse off
    than if you were alone.
  • You can only benefit (or do no worse) from your
    neighbors tasks. Assume, though, that one of my
    children and one of my neighbors children both
    go to the same school (that is, the cost of
    carrying out these two deliveries, or two tasks,
    is the same as the cost of carrying out one of
  • It obviously makes sense for both children to be
    taken together, and only my neighbor or I will
    need to make the trip to carry out both tasks.

--- Rules of Encounter, Rosenschein and Zlotkin,
Researches Machines Controlling and Sharing
  • Electrical grids (load balancing)
  • Telecommunications networks (routing)
  • PDAs (schedulers)
  • Shared databases (intelligent access)
  • Traffic control (coordination)

  • Micheal Wooldridge, An Itroduction to Multiagent
    Systems, John WileySons, May 2009.
  • S. Sodee, M. Komkhao and P. Meesad Consensus
    Decision Making on Scale-free Buyer Network.
    Intl. J. Computer Science pp. 1554-1559, 2011.
  • S. Sodsee, M. Komkhao, Z. Li, W.K.S. Tang, W.A.
    Halang and L. Pan Discrete-Time Consensus in a
    Scale-Free Buyer Network. In Intelligent
    Decision Making Systems, K. Vanhoof, D. Ruan, T.
    Li and G. Weets (Eds.), pp. 445452, Singapore
    World Scientific 2010.
  • S. Sodsee, M. Komkhao, Z. Li, W.A. Halang and P.
    Meesad Leader-following Discrete-time Consensus
    Protocol in a Buyer-Seller Network. Proc. Intl.
    Conf. Chaotic Modeling and Simulation, Greece,
  • T. Labella, M. Dorigo, and J. Deneubourg,
    Self-Organized Task Allocation in a Group of
    Robots, Proceedings of the 7th International
    Symposium on Distributed Autonomous Robotic
    Systems (DARS04). Toulouse, France, June 23-25,
  • B.B. Biswal and B.B. Choudhury, Cooperative task
    planning of multi-robot, systems, 24th
    international Symposiam on Automation Robotic
    in Constructions (ISARC), 2007.
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