Consensus: Multi-agent Systems (Part1)

Quantitative Analysis: How to make a decision?

Thank you for all referred pictures and information.

Agenda

 Introduction   Definitions Questions  Reaching Agreements  Auction  Task allocation  Auction algorithm 2

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 3

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.

4

Swarm Intelligence

 Application of Swarm Principles: Swarm of Robotics http :// www .

domesro .

com / 2008/08 / swarm robotics for domestic use .

html  http://www.youtube.com/watch?feature=playe r_embedded&v=rYIkgG1nX4E#! 55

Multiagent Systems (MAS)

 Questions In Multiagent Systems:  How can cooperation interested agents?

emerge in societies of self  What kinds of languages/protocols use to communicate?

can agents  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?

6

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 7

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 8

Objections to MAS

 Isn’t 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 9

Objections to MAS

 Isn’t it all just AI?

 We don’t 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 10

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 11

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 12



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 13

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 14

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 15

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?

16

Mechanism Design

 Desirable properties of mechanisms: 

Convergence/guaranteed success



Maximizing social welfare



Pareto efficiency



Individual rationality



Stability



Simplicity



Distribution

17

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 18

Auction Parameters



Goods can have

  

private value publi

c/

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

19

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: 

winner’s curse



shills

20

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 21

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

22

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 23

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 2 nd -highest bid 

Shills

can be introduced to inflate bidding prices in English auctions 24

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 1 2 4 3 25

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 26

Methods of Task Allocation

Methods of Task allocation Centralized Methods Decentralized Methods Distributed Methods • • • • • Pros Cheaper and easier to build the structure.

Fit to manage tasks for each agent, then ease to work.

Reduce conflict of actions.

No single point of failure Each of agent has capability to coordinate their actions by themselves. • • • • • Cons A single point of failure.

Limited Bandwidth.

Congestion of transportation.

Conflict of assignment.

Collecting information of each sub-decision making through the center. • • • local information exchanging among neighbors Support Dynamic network topology Support Large-scale network No global information 27

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) 28

Auction Algorithm

29

Auction Algorithm

30

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 31

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 other’s 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 won’t 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 neighbor’s 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.

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Rules of Encounter

, Rosenschein and Zlotkin, 1994 32

Researches: Machines Controlling and Sharing Resources

 Electrical grids (load balancing)  Telecommunications networks (routing)  PDA’s (schedulers)  Shared databases (intelligent access)  Traffic control (coordination) 33

References

      Micheal Wooldridge, “An Itroduction to Multiagent Systems,”

John Wiley&Sons

, 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. 445 –452, 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.

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