Consensus: Multi- agent Systems (Part1) Quantitative Analysis: How to make a decision? you for all referred pictures and information.
Dec 28, 2015
Consensus: Multi-agent Systems (Part1)
Quantitative Analysis: How to make a decision?
Thank you for all referred pictures and information.
2
Agenda
Introduction Definitions Questions
Reaching Agreements
Auction Task allocation Auction algorithm
3
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
4
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.
55
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
6
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?
7
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
8
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
9
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
10
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
11
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
12
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
13
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
14
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
15
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
16
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?
17
Mechanism Design
Desirable properties of mechanisms: Convergence/guaranteed success Maximizing social welfare Pareto efficiency Individual rationality Stability Simplicity Distribution
18
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
19
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
20
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
21
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
22
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
23
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
24
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
25
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
26
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
27
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
28
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)
29
Auction Algorithm
30
Auction Algorithm
31
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
32
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
33
Researches: Machines Controlling and Sharing Resources
Electrical grids (load balancing) Telecommunications networks (routing) PDA’s (schedulers) Shared databases (intelligent access) Traffic control (coordination)
34
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.