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Vicki Allan 2008. Looking for students for two NSF funded grants. Funded Projects 2008-2011. CPATH – Computing Concepts Educational Curriculum Development Looking for help in the creation of a new introductory course – USU 1360 COAL – Coalition Formation Research in Multi-agent systems.

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Vicki Allan 2008

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Vicki allan 2008

Vicki Allan2008

Looking for students

for two NSF funded grants

Funded projects 2008 2011

Funded Projects 2008-2011

  • CPATH – Computing Concepts

    • Educational

    • Curriculum Development

    • Looking for help in the creation of a new introductory course – USU 1360

  • COAL – Coalition Formation

    • Research in Multi-agent systems



  • There is a need for more computer science graduates.

  • There is a lack of exposure to computer science.

  • Introductory classes are unattractive to many.

  • Women are not being attracted to computer science despite forces which should attract women – good pay, flexible hours, interesting problems.

Create a library of multi function interactive learning modules ilms

Create a library of multi-function Interactive Learning Modules (ILMs)

  • Showcase computational thinking

  • De-emphasize programming

Vicki allan 2008

The balls on the left are to be exchanged

with the balls on the right by a sequence

of moves. Any ball can move into adjacent empty slot. Any ball can jump over a single neighbor to an empty slot.


Algorithm design

Abstraction – general purpose rules

Need students

Need Students

  • Good programmers to program interactives. Using Java or flash.

  • Ideas for how to revitalize undergraduate education

  • TA for next semester to help with USU 1360

Vicki allan 2008


Second project involves multi-agent systems

If two heads are better than one how about 2000

If two heads are better than one, how about 2000?

Monetary auction

Monetary Auction

  • Object for sale: a dollar bill

  • Rules

    • Highest bidder gets it

    • Highest bidder and the second highest bidder pay their bids

    • New bids must beat old bids by 5¢.

    • Bidding starts at 5¢.

    • What would your strategy be?

Give away

Give Away

  • Bag of candy to give away

  • If everyone in the class says “share”, the candy is split equally.

  • If only one person says “I want it”, he/she gets the candy to himself.

  • If more than one person says “I want it”, I keep the candy.

The point

The point?

  • You are competing against others who are as smart as you are.

  • If there is a “weakness” that someone can exploit to their benefit, someone will find it.

  • You don’t have a central planner who is making the decision.

  • Decisions happen in parallel.



  • Hiring a new professor this year.

  • Committee of three people to make decision

  • Have narrowed it down to four.

  • Each person has a different ranking for the candidates.

  • How do we make a decision?

Vicki allan 2008

Binary Protocol

One voter ranks c > d > b > a

One voter ranks a > c > d > b

One voter ranks b > a > c > d

winner (c, (winner (a, winner(b,d)))=a

winner (d, (winner (b, winner(c,a)))=d

winner (d, (winner (c, winner(a,b)))=c

winner (b, (winner (d, winner(c,a)))=b

surprisingly, order of pairing yields different winner!

Vicki allan 2008

If you only wanted to find the first place winner, could you count the number of times a person was ranked first?

  • a > b > c >d

  • a > b > c >d

  • a > b > c >d

  • a > b > c >d

  • b > c > d> a

  • b > c > d> a

  • b > c > d> a

    a=19, b=24, c=17, d=10

Just counting first ranks isn’t enough.

Vicki allan 2008

Borda protocol

assigns an alternative |O| points for the highest preference, |O|-1 points for the second, and so on

  • The counts are summed across the voters and the alternative with the highest count becomes the social choice


Vicki allan 2008


Borda paradox

Borda Paradox

  • a > b > c >d

  • b > c > d >a

  • c > d > a > b

  • a > b > c > d

  • b > c > d> a

  • c >d > a >b

  • a <b <c < d

    a=18, b=19, c=20, d=13

Is this a good way?

Clear loser

Borda paradox remove loser d winner changes

Borda Paradox – remove loser (d), winner changes

  • a > b > c

  • b > c >a

  • c > a > b

  • a > b > c

  • b > c > a

  • c > a >b

  • a <b <c

    a=15,b=14, c=13

  • a > b > c >d

  • b > c > d >a

  • c > d > a > b

  • a > b > c > d

  • b > c > d> a

  • c >d > a >b

  • a <b <c < d

    a=18, b=19, c=20, d=13

When loser is removed, second worst becomes winner!



  • Finding the correct mechanism is not easy

Who works together in agent coalition formation

Vicki Allan – Utah State University

Kevin Westwood – Utah State University

Presented September 2007, Netherlands

(Work also presented in Hong Kong, Finland, Australia, California)

CIA 2007

Who Works Together in Agent Coalition Formation?



  • Tasks: Various skills and numbers

  • Agents form coalitions

  • Agent types - Differing policies

  • How do policies interact?

Multi agent coalitions

Multi-Agent Coalitions

  • “A coalition is a set of agents that work together to achieve a mutually beneficial goal” (Klusch and Shehory, 1996)

  • Reasons agent would join Coalition

    • Cannot complete task alone

    • Complete task more quickly

Skilled request for proposal srfp environment

Skilled Request For Proposal (SRFP) Environment

Inspired by RFP (Kraus, Shehory, and Taase 2003)

  • Provide set of tasks T = {T1…Ti…Tn}

    • Divided into multiple subtasks

    • In our model, task requires skill/level

    • Has a payment value V(Ti)

  • Service Agents, A = {A1…Ak…Ap}

    • Associated cost fk of providing service

    • In the original model, ability do a task is

      determined probabilistically – no two agents alike.

    • In our model, skill/level

    • Higher skill is more flexible (can do any task with lower level skill)

Why this model

Why this model?

  • Enough realism to be interesting

    • An agent with specific skills has realistic properties.

    • More skilled can work on more tasks, (more expensive) is also realistic

  • Not too much realism to harm analysis

    • Can’t work on several tasks at once

    • Can’t alter its cost

Auctioning protocol

Auctioning Protocol

  • Variation of a reverse auction

    • One “buyer” lots of sellers

    • Agents compete for opportunity to perform services

    • Efficient way of matching goods to services

  • Central Manager (ease of programming)

    1)Randomly orders Agents

    2)Each agent gets a turn

    • Proposes or Accepts previous offer

      3)Coalitions are awarded task

  • Multiple Rounds {0,…,rz}

  • Agent costs by level

    Agent Costs by Level

    General upwardtrend

    Vicki allan 2008

    • Agent cost

      • Base cost derived from skill and skill level

      • Agent costs deviate from base cost

    • Agent payment

      • cost + proportional portion of net gain

    Only Change in coalition

    Vicki allan 2008

    How do I decide what to propose?

    The setup

    The setup

    • Tasks to choose from include skills needed and total pay

    • List of agents – (skill, cost)

    • Which task will you choose to do?



    If I make an offer…

    • What task should I propose doing?

    • What other agents should I recruit?

      If others have made me an offer…

    • How do I decide whether to accept?

    Coalition calculation algorithms

    Coalition Calculation Algorithms

    • Calculating all possible coalitions

      • Requires exponential time

      • Not feasible in most problems in which tasks/agents are entering/leaving the system

    • Divide into two steps

      1) Task Selection

      2) Other Agents Selected for Team

      • polynomial time algorithms

    Task selection 4 agent types

    Task Selection- 4 Agent Types

    • Individual Profit – obvious, greedy approach

      Competitive: best for me

      Why not always be greedy?

      • Others may not accept – your membership is questioned

      • Individual profit may not be your goal

    • Global Profit

    • Best Fit

    • Co-opetitive

    Task selection 4 agent types1

    Task Selection- 4 Agent Types

    • Individual Profit

    • Global Profit – somebody should do this task

      I’ll sacrifice

      Wouldn’t this always be a noble thing to do?

      • Task might be better done by others

      • I might be more profitable elsewhere

    • Best Fit – uses my skills wisely

    • Co-opetitive

    Task selection 4 agent types2

    Task Selection- 4 Agent Types

    • Individual Profit

    • Global Profit

    • Best Fit – Cooperative: uses skills wisely

      Perhaps no one else can do it

      Maybe it shouldn’t be done

    • Co-opetitive

    4 th type co opetitive agent

    4th type: Co-opetitive Agent

    • Co-opetition

      • Phrase coined by business professors Brandenburger and Nalebuff (1996),to emphasize the need to consider both competitive and cooperative strategies.

    • Co-opetitive Task Selection

      • Select the best fit task if profit is within P% of the maximum profit available

    What about accepting offers

    What about accepting offers?

    Melting – same deal gone later

    • Compare to what you could achieve with a proposal

    • Compare best proposal with best offer

    • Use utility based on agent type

    Vicki allan 2008

    Some amount of compromise is necessary…

    We term the fraction of the total possible you demand – the compromising ratio

    Resources shrink

    Resources Shrink

    • Even in a task rich environment the number of tasks an agent has to choose from shrinks

      • Tasks get taken

    • Number of agents shrinks as others are assigned

    Vicki allan 2008

    Task Rich: 2 tasks

    for every agent

    My tasks parallel total tasks

    Scenario 1 bargain buy

    Scenario 1 – Bargain Buy

    • Store “Bargain Buy” advertises a great price

    • 300 people show up

    • 5 in stock

    • Everyone sees the advertised price, but it just isn’t possible for all to achieve it

    Scenario 2 selecting a spouse

    Scenario 2 – selecting a spouse

    • Bob knows all the characteristics of the perfect wife

    • Bob seeks out such a wife

    • Why would the perfect woman want Bob?

    Scenario 3 hiring a new phd

    Scenario 3 – hiring a new PhD

    • Universities ranked 1,2,3

    • Students ranked a,b,c

      Dilemma for second tier university

    • offer to “a” student

    • likely rejected

    • delay for acceptance

    • “b” students are gone

    Affect of compromising ratio

    Affect of Compromising Ratio

    • equal distribution of each agent type

    • Vary compromising ratio of only one type (local profit agent)

    • Shows profit ratio = profit achieved/ideal profit (given best possible task and partners)

    Vicki allan 2008

    Note how profit is affect by load

    Achieved/theoretical best

    Profit only of scheduled agents

    Profit only of scheduled agents

    Only Local Profit agents

    change compromising ratio

    Yet others slightly increase too

    Vicki allan 2008


    • Demanding local profit agents reject the proposals of others.

    • They are blind about whether they belong in a coalition.

    • They are NOT blind to attributes of others.

    • Proposals are fairly good

    Vicki allan 2008

    For every agent type, the most likely proposer

    was a Local Profit agent.

    Vicki allan 2008

    No reciprocity: Coopetitive eager to accept Local Profit proposals,

    but Local Profit agent doesn’t accept

    Coopetitive proposals especially well

    Vicki allan 2008

    For every agent type,

    Best Fit is a strong acceptor.

    Perhaps because it isn’t accepted well as a proposer

    Vicki allan 2008

    Load balance seems to affect roles

    Coopetitive agents function better as proposers to Local Profit agents in balanced or task rich environment.

    • When they have more choices, they tend to propose coalitions local profit agents like

    • More tasks give a Coopetitive agent a better sense of its own profit-potential

    Coopetitive Agents look

    at fit as long as it isn’t too bad

    compared to profit.

    Agent rich 3 agents task

    Agent rich: 3 agents/task

    Coopetitive accepts most proposals

    from agents like itself

    in agent rich environments

    Vicki allan 2008

    • Do agents generally want to work with agents of the same type?

      • Would seem logical as agents of the same type value the same things – utility functions are similar.

      • Coopetitive and Best Fit agents’ proposal success is stable with increasing percentages of their own type and negatively correlated to increasing percentages of agents of other types.

    Look at function with increasing numbers of one other type

    Look at function with increasing numbers of one other type.

    What happens as we change relative percents of each agent

    What happens as we change relative percents of each agent?

    • Interesting correlation with profit ratio.

    • Some agents do better and better as their dominance increases. Others do worse.

    Vicki allan 2008

    Best fit does better and better as more dominant in set

    Best fit does better and better as more dominant in set

    shows relationship if all equal percent

    Local Profit

    does better when

    it isn’t dominant

    So who joins and who proposes

    So who joins and who proposes?

    • Agents with a wider range of acceptable coalitions make better joiners.

    • Fussier agents make better proposers.

    • However, the joiner/proposer roles are affected by the ratio of agents to work.



    • Some agent types are very good in selecting between many tasks, but not as impressive when there are only a few choices.

    • In any environment, choices diminish rapidly over time.

    • Agents naturally fall into role of proposer or joiner.

    Future work

    Future Work

    • Lots of experiments are possible

    • All agents are similar in what they value. What would happen if agents deliberately proposed bad coalitions?

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