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?feature=player_embedded&v=rYIkgG1nX4E#!

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 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

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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

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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: winner’s 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 AllocationMethods of Task allocation Pros ConsCentralized 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 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.

--- Rules of Encounter, Rosenschein and Zlotkin, 1994

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Researches: Machines Controlling and Sharing Resources

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

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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.