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Vicki Allan 2010

Vicki Allan 2010

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Vicki Allan 2010

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  1. Vicki Allan2010 Looking for students for two NSF funded grants Encourage Taking CS6100 (Spring)

  2. Funded Projects 2008-2011 • CPATH – Computing Concepts • Educational • Curriculum Development • COAL – Coalition Formation • Research in Multi-agent systems

  3. CPATH • 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.

  4. Create a library of multi-function Interactive Learning Modules (ILMs) • Showcase computational thinking • De-emphasize programming • Website: CSILM.USU.EDU • The following are examples created by our students:

  5. Algorithm design Abstraction

  6. Boolean Expressions

  7. Need Students • Good programmers to program interactive learning modules in Java. • Students with ideas for how to revitalize undergraduate education

  8. COAL Second project involves multi-agent systems Prefer to hire someone who has taken (or is taking) CS6100 – multi-agent systems. AI experience is an advantage

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

  10. Monetary Auction • Object for sale: a five 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?

  11. Give Away • Bags 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.

  12. 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.

  13. Cooperation • Hiring a new professor this year. • Committee of three people to make decision • Have narrowed it down to four candidates. • Each person has a different ranking for the candidates. • How do we make a decision? • Termed a social choice function

  14. Binary Protocol One voter ranks c > d > b > a One voter ranks a > c > d > b One voter ranks b > a > c > d

  15. 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 (c, (winner (b, winner(a,d)))=c winner (b, (winner (a, winner(c,d)))=b surprisingly, order of pairing yields different winner!

  16. Suppose we have seven votersHow choose winner? • 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 Are they honest? Who is really the most preferred candidate?.

  17. 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 17

  18. reasonable???

  19. 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

  20. 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, third choice becomes winner!

  21. Conclusion • Finding the correct mechanism is not easy

  22. Coalition Formation Overview • Tasks: Various skills and numbers • Agents form coalitions • Agent types - Differing policies • How do policies interact?

  23. 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

  24. Scenario 1 – Bargain Buy(supply-demand) • 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

  25. Scenario 2 – selecting a spouse(agency) • Bob knows all the characteristics of the perfect wife • Bob seeks out such a wife • Why would the perfect woman want Bob?

  26. Scenario 3 – hiring a new PhD(strategy) • 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

  27. Scenario 4 (trust) What if one person talks a good story, but his claims of skills are really inflated? He isn’t capable of performing. the task.

  28. Scenario 5 The coalition is completed and rewards are earned. How are they fairly divided among agents with various contributions? If organizer is greedy, why wouldn’t others replace him with a cheaper agent?

  29. Scenario 5 You consult with local traffic to find a good route home from work But so does everyone else

  30. 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?

  31. Overview • Tasks: Various skills and numbers • Agents form coalitions • Agent types - Differing policies • How do policies interact?

  32. 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

  33. 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)

  34. 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

  35. 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}

  36. Agent Costs by Level General upwardtrend

  37. 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

  38. How do I decide what to propose?

  39. The setup • Tasks to choose from include skills needed and total pay • List of agents – (skill, cost) • Which task will you choose to do?

  40. Decisions 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?

  41. 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

  42. 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

  43. 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

  44. 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

  45. 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

  46. 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

  47. Some amount of compromise is necessary… We term the fraction of the total possible you demand – the compromising ratio

  48. 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

  49. Task Rich: 2 tasks for every agent My tasks parallel total tasks

  50. 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