1. Vicki Allan 2008 Looking for students for two NSF funded grants

2. 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

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

5. 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. Complexity Algorithm design Abstraction – general purpose rules

6. 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

7. COAL Second project involves multi-agent systems

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

9. 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?

10. 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.

11. 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.

12. Cooperation 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?

13. 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!

14. 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 b > c > d> a a=19, b=24, c=17, d=10 Just counting first ranks isn’t enough.

15. 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 15

16. reasonable???

17. 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

18. 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, second worst becomes winner!

19. Conclusion Finding the correct mechanism is not easy

20. 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

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

22. 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

23. 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)

24. 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

25. 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 }

26. Agent Costs by Level General upward trend

27. 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

28. How do I decide what to propose?

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

30. 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?

31. 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

32. 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

33. 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

34. 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

35. 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

36. 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

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

38. 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

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

40. 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

41. 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?

42. 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

43. 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)

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

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

46. 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

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

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

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

50. 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.

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

52. 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.

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

54. 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.

55. 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

56. 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.

57. 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.

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