Consensus: Multi-agent Systems (Part1)

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Consensus Multi-agent Systems (Part1)

• Quantitative Analysis How to make a decision?

Thank you for all referred pictures and
information.
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Agenda

• Introduction
• Definitions
• Questions
• Reaching Agreements
• Auction
• Task allocation
• Auction algorithm

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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

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Multiagent Systems, a Definition

• Why we apply multi-agent systems to solve the
  problem?
• A single agent cannot perform parallel tasks
  alone.
• Multi-agent can accomplish given tasks more
  quickly.

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Swarm Intelligence

• Application of Swarm Principles Swarm of
  Robotics
• http//www.youtube.com/watch?featureplayer_embedd
  edvrYIkgG1nX4E!

http//www.domesro.com/2008/08/swarm-robotics-for-
domestic-use.html
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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?

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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

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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
  itself

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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

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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

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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

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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

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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

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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

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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
  themselves
• Argumentation
• how conclusions can be reached through logical
  reasoning
• Including debate and negotiation which are
  concerned with reaching mutually acceptable
  conclusions

http//en.wikipedia.org/wiki/Negotiation
http//en.wikipedia.org/wiki/Argumentation_theory
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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
  use?

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Mechanism Design

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

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Auctions

• 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

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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

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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
  withdraw
• Susceptible to
• winners curse
• shills

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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

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First-Price Sealed-Bid Auctions

• First-price sealed-bid auctions are one-shot
  auctions
• 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

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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
  bid
• Bidding to your true valuation is dominant
  strategy in Vickrey auctions
• Vickrey auctions susceptible to antisocial
  behavior

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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
  collusion
• 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

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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

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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
  tasks.
• 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

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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
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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)

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Auction Algorithm
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Auction Algorithm
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Negotiation

• 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
  components
• 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

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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
  trip).
• 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
  them).
• 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,
1994
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Researches Machines Controlling and Sharing
Resources

• Electrical grids (load balancing)
• Telecommunications networks (routing)
• PDAs (schedulers)
• Shared databases (intelligent access)
• Traffic control (coordination)

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References

• 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,
  2010.
• 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,
  2004.
• B.B. Biswal and B.B. Choudhury, Cooperative task
  planning of multi-robot, systems, 24th
  international Symposiam on Automation Robotic
  in Constructions (ISARC), 2007.