1. Vicki Allan 2010 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.EDUCSILM.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 voters How choose winner? a > b > c >d b > c > d> a Who is really the most preferred candidate?. Are they honest?

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 >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 n a > b > c n b > c >a n c > a > b n a > b > c n b > c > a n c > a >b n a <b <c a=15,b=14, c=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. 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

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 = {T 1 …T i …T n } –Divided into multiple subtasks –In our model, task requires skill/level –Has a payment value V(T i ) Service Agents, A = {A 1 …A k …A p } –Associated cost f k 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,…,r z }

36. Agent Costs by Level General upward trend

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 1.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 2.Global Profit 3.Best Fit 4.Co-opetitive

43. Task Selection- 4 Agent Types 1.Individual Profit 2.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 3.Best Fit – uses my skills wisely 4.Co-opetitive

44. Task Selection- 4 Agent Types 1.Individual Profit 2.Global Profit 3.Best Fit – Cooperative: uses skills wisely Perhaps no one else can do it Maybe it shouldn’t be done 4.Co-opetitive

45. 4 th 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. My tasks parallel total tasks Task Rich: 2 tasks for every agent

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

51. 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?

52. 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

53. 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)

54. Achieved/theoretical best Note how profit is affect by load

55. Profit only of scheduled agents Only Local Profit agents change compromising ratio Yet others slightly increase too

56. Note 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

57. For every agent type, the most likely proposer was a Local Profit agent.

58. No reciprocity: Coopetitive eager to accept Local Profit proposals, but Local Profit agent doesn’t accept Coopetitive proposals especially well

59. For every agent type, Best Fit is a strong acceptor. Perhaps because it isn’t accepted well as a proposer

60. 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 Load balance seems to affect roles Coopetitive Agents look at fit as long as it isn’t too bad compared to profit.

61. Agent rich: 3 agents/task Coopetitive accepts most proposals from agents like itself in agent rich environments

62. 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.

63. Look at function with increasing numbers of one other type.

64. 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.

65. shows relationship if all equal percent Best fit does better and better as more dominant in set Local Profit does better when it isn’t dominant

66. 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.

67. Conclusions 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.

68. Future Work Lots of experiments are possible All agents are similar in what they value. What would happen if agents deliberately proposed bad coalitions?