2. L10. Agent Negotiations When Definition and concepts Strategies – negotiation modeling Examples – a buyer-seller negotiation

3. When negotiations occur? Task and resource allocation Recognition of conflicts Improved coherence for agent society Deciding Organizational Structure

4. Definitions of Negotiation Davis&Smith Negotiation is a process of improving agreement (reducing inconsistency and uncertainty) on common viewpoint or plans through the exchange of relevant information 1.Two-way exchange of information (e.g. 2 agents) 2.Individual perspective evaluation of information 3.Possible final agreement

5. Related Elements Negotiation – three main structures 1.Language 2.Decision 3.Process

6. Process Language Decision N EGOTIATION PROCESS

8. Negotiation Problem Domains Three-level hierarchy 1.Task-Oriented –Non-conflicting jobs/tasks –Jobs/tasks can be redistributed among agents (for mutual benefit) 2.State-Oriented Superset of task-oriented domain Goals/jobs/tasks can have side-effects (i.e. Conflicting) Negotiation  joint plans/schedules for agents 3.Worth-Oriented Superset of state-oriented domain Each goal has a rating or value (e.g. Numeric) Negotiation  joint plans/schedules/goal relaxation

9. Postmen Problem Domain Type: task-oriented Situation: Several postmen located at a post office Post arrives to the post office Post is supposed to be delivered by the postmen to private postal boxes which is geographically (spatially) distributed Which postman should deliver which post to where?

10. Postmen Domain Post Office a c d e  2 1     TOD b f

11. Blocks World Problem Domain Type: state-oriented Situation: agents have their own agenda on how to stack various colored blocks. Blocks are a shared resource. How to coordinate the agents actions to solve conflicting block moves?

12. Slotted Blocks World 12 3 12 3 SOD 2 1

13. Multiagent Tile World Problem Domain Type: worth-oriented Situation: agents operate on a grid, there are tiles that needs to be put into holes. The different holes have different values. In addition there are obstacles. How to coordinate the agents actions to solve conflicting tile-moves and get good compromises regarding the agents obtained values?

14. The Multi-Agent Tileworld 22 2 2 5 5 3 4 A B tile hole obstacle agents WOD

15. Building Blocks Domain –A precise definition of what a goal is –Agent operations Negotiation protocol –A definition of a deal –A definition of utility –A definition of the conflict deal Negotiation Strategy –In Equilibrium –Incentive-compatible

16. Task-Oriented Domain – formal description Described by a tuple - T – set of all tasks (all possible actions in the domain) A – list of agents c – a monotonic cost function for each task to a real number

17. Possible Deals 1.({a}, {b}) 2.({b}, {a}) 3.({a, b},  ) 4.( , {a, b}) 5.({a}, {a, b}) 6.({b}, {a, b}) 7.({a, b}, {a}) 8.({a, b}, {b}) 9.({a, b}, {a, b}) The conflict deal

18. Formal Description of a ”Deal” A deal  is a pair (D 1, D 2 ) such that: D 1  D 2 = T 1  T 2 T 1 – Agent 1’s original task T 2 – Agent 2’s original task D 1 – Agent 1’s new task – result of deal D 2 – Agent 2’s new task – result of deal

19. Utility Function Given encounter, the utility of deal  to agent k is: utility k (  ) = c(T k ) – cost k (  )  = c(T k ) is the stand-alone cost to agent k (the cost of achieving its goal with no help) cost k (  ) = c(D k )

20. Example: parcel delivery domain -- utility 11 distribution point ab Utility for agent 1: 1.utility 1 ({a}, {b}) = 0 2.utility 1 ({b}, {a}) = 0 3.utility 1 ({a, b},  ) = -2 4.utility 1 ( , {a, b}) = 1 5.utility 1 ({a}, {a, b}) = 0 6.utility 1 ({b}, {a, b}) = 0 7.utility 1 ({a, b}, {a}) = -2 8.utility 1 ({a, b}, {b}) = -2 9.utility 1 ({a, b}, {a, b}) = -2 Utility for agent 2: 1.utility 2 ({a}, {b}) = 2 2.utility 2 ({b}, {a}) = 2 3.utility 2 ({a, b},  ) = 3 4.utility 2 ( , {a, b}) = 0 5.utility 2 ({a}, {a, b}) = 0 6.utility 2 ({b}, {a, b}) = 0 7.utility 2 ({a, b}, {a}) = 2 8.utility 2 ({a, b}, {b}) = 2 9.utility 2 ({a, b}, {a, b}) = 0 Cost function: c(  ) = 0 c({a}) = 1 c({b}) = 1 c({a,b}) = 3

21. Deals 1.({a}, {b}) 2.({b}, {a}) 3.({a, b},  ) 4.( , {a, b}) 5.({a}, {a, b}) 6.({b}, {a, b}) 7.({a, b}, {a}) 8.({a, b}, {b}) 9.({a, b}, {a, b}) ({a}, {b}) ({b}, {a}) ( , {a, b}) ({a}, {a, b}) ({b}, {a, b}) ({a}, {b}) ({b}, {a}) ({a, b},  ) ( , {a, b}) Invidual rational Pareto optimal ({a}, {b}) ({b}, {a}) ( , {a, b}) Negotiation sets

22. The Negotiation Set Illustrated

23. Named after Vilfredo Pareto, Pareto optimality is a measure of efficiency. An outcome of a game is Pareto optimal if there is no other outcome that makes every player at least as well off and at least one player strictly better off. That is, a Pareto Optimal outcome cannot be improved upon without hurting at least one player.Vilfredo Paretoefficiency Pareto optimality:

24. Negotiation Protocols Agents use a product-maximizing negotiation protocol (as in Nash bargaining theory) It should be a symmetric PMM (product maximizing mechanism) Examples: 1-step protocol, monotonic concession protocol …

25. The Monotonic Concession Protocol Rules of this protocol are as follows … Negotiation proceeds in rounds On round 1, agents simultaneously propose a deal from the negotiation set Agreement is reached if one agent finds that the deal proposed by the other is at least as good or better than its proposal If no agreement is reached, then negotiation proceeds to another round of simultaneous proposals In round u + 1, no agent is allowed to make a proposal that is less preferred by the other agent than the deal it proposed at time u If neither agent makes a concession in some round u > 0, then negotiation terminates, with the conflict deal

26. The Zeuthen Strategy Three problems: What should an agent ’ s first proposal be? Its most preferred deal On any given round, who should concede? The agent least willing to risk conflict If an agent concedes, then how much should it concede? Just enough to change the balance of risk

27. Willingness to Risk Conflict Suppose you have conceded a lot. Then: –Your proposal is now near the conflict deal –In case conflict occurs, you are not much worse off –You are more willing to risk confict An agent will be more willing to risk conflict if the difference in utility between its current proposal and the conflict deal is low

28. Nash Equilibrium Again … The Zeuthen strategy is in Nash equilibrium: under the assumption that one agent is using the strategy the other can do no better than use it himself … This is of particular interest to the designer of automated agents. It does away with any need for secrecy on the part of the programmer. An agent ’ s strategy can be publicly known, and no other agent designer can exploit the information by choosing a different strategy. In fact, it is desirable that the strategy be known, to avoid inadvertent conflicts.

29. A Nash equilibrium, named after John Nash, is a set of strategies, one for each player, such that no player has incentive to unilaterally change her action. Players are in equilibrium if a change in strategies by any one of them would lead that player to earn less than if she remained with her current strategy. For games in which players randomize (mixed strategies), the expected or average payoff must be at least as large as that obtainable by any other strategy.John Nash strategiesplayer Playersmixed strategies Nash equilibrium :

30. base on the original Bazaar model take wholesalers into considerations use game theory in generating initial strategy combine common&public knowledge A Hybrid Negotiation Model

31. Extended bazaar model - a brief description a 10-tuple, – G, a set of players – W, a set of wholesalers – D, a set of negotiation issues – S, a set of agreements over each issue – A, a set of all possible actions – H, a set of history sequences – Ω, a set of relevant information entities – P, a set of subjective probability distribution – C, a set of communication costs – E, a set of evaluation functions

32. Extended bazaar model – in a bilateral case a 10-tuple, – G, a seller and a buyer – W, a wholesaler – D, a single issue-product price – S, price offer/counter offer – A, possible price offers/counter offers – H, a sequence of price offers/counter offers at each negotiation round, (a k | k=1,2,…,K  H) ∩ (L<K) ⇒ (a k | k=1,2,…,L  H) (a k | k=1,2,…,K  H) ∩ (a K  {accept, quit}) ⇒ a k  {accept, quit}| k=1,2,…,K-1

33. – continue … a 10-tuple, – Ω, a set of knowledge entities a seller/buyer has about environment (average price, economic situation, …), counter party (RP, payoff function, type…) – P, subjective probability distribution of hypothesis on a belief x. P [h,1] (x), P [h,2] (x) – C, communication costs for a seller or buyer to continue another negotiation round – E, E i : (P [i, h] (x)|x  Ω i, Pf i, a) → utility(g i ), a  A i, E i  E, i=1,2

34. – continue … a 10-tuple, – E, two evaluation function,one for a seller and one for a buyer. E i : (P [i, h] (x)|x  Ω i, Pf i, a) → utility(g i ), a  A i, E i  E, i=1,2 For any action a, it falls into three types: U i = 1.0 -> {agreement: accept}, U i = 0.0 ->{agreement: quit}, and 0.0 {new agreement }

35. Accept: If price(a k seller ) < RP buyer, then E [1, a k ] =1, a k =accept Quit: If (price(a k seller ) – RP seller RP buyer ), then E [1, a k ] =0, a k =quit fitness: f 1 (s k j )=1-(CP buyer (j)-RP seller )/(RP buyer -RP seller ), RP buyer- C 1 >CP buyer (j)>RP seller s k j =CP buyer (j)  S 1, j=1, 2, …, N p s k j0 is selected as the counter-offer if we have f 1 (s k j0 )=max{ f 1 (s k j )}, j 0  j s k j0 = RP seller +   is regarded as a psychological factor Making a decision over price only

36. Learning with Bayesian rule updating P [h[1,k],1] (B j |h[1,k])= P [h[1,k1],1] (B j )*P [h[1,k],1] (h[1,k]|B j )/(  b j=1 P [h[1,k],1] (h[1,k]|B j )* P [h[1,k-1], 1] (B j ) ) (1) P [h[1,k],1] (h[1,k]|B j )= 1-(|(h[1,k]/(1-  )+WP [1,k] +  wp )/2-B j |)/(h[1,k]/(1-  )+ WP [1,k] +  wp )/2) (2) RP seller =  b j=1 P [h[1,k], 1] ( Bj|h[1,k])* B j – P [h[1,k], 1] (B j | h[1,k]) is posterior distribution – P [h[1,k-1], 1] (B j ) is prior distribution – h[1,k] is newly incoming information – B j is hypothesis on a belief. RP seller

37. Enhanced extended Bazaar model Instead of setting the probability of each hypothesis P k=0 (Bj)=1/b, for each j, P k=0 (Bj) is calculated. collecting public available information (a list of prices) to estimate counter party ’ s possible demand (RP) RP ’ seller =(  GP i +  (WP j +  wp ))/(u+v) (3) finding a solution using the estimated demand max(RP buyer -x)(x-RP ’ seller ), x = (RP buyer + RP ’ seller )/2 (4) initiating the probability distribution P ’ (B j ) = 1-|x-Bj|/x (5) P k=0 (B j ) = P ’ (B j )/  P ’ (B j ) (6)

38. Updating probability distribution K Offer Counte r Offer P(B 1 ) P(B 2 ) P(B 3 ) P(B ) 0--- 0.170.260.330.24 1140107.90.160.220.290.33 2135109.70. 070.180.460.29 3130110.20.030.140.610.22

39. Comparisons The normalized joint utility is defined as: JointUtility=(price agreed -RP seller )*(RP buyer -price agreed )/( RP buyer -RP seller ) 2 (7)

40. – continue …

41. System configuration

42. A Real World Trading Oriented Market-driven Model for Negotiation Agent Yoshizo Ishihara and Runhe Huang Faculty of Computer and Information Sciences, Hosei University, Tokyo, Japan

43. Negotiation Agent Bid Seller Buyer Seller Agent Buyer Agent Negotiation

44. Negotiation Factors Sim’s model is guided by following four negotiation factors: –Trading Opportunity –Trading Competition –Trading Time –Trading Eagerness of the agent itself The spread k’ between an agent’s bid/offer and that of others in the next trading cycle is determined as:

45. Our Improved Model We improved Sim’s model in 2004 using Bayesian updating rule to learn opponent’s eagerness. An agent can make a concession for its opponent’s motivation. The spread k’ is redefined as:

46. A Precondition In both Sim’s and our improved model, a negotiation agent has same behaviors and actions to all trading partners. $800 Same

47. A Real World Trading In fact, a negotiation strategy between a buyer and a seller is kept in secret and unknown to others. ???? Unknown

48. A Revised Model A revised market-driven model takes each trading partner as an individual with different strategies and actions. $850 $750 Different & Unknown

49. The competition factor in the previous model Each trading partner has a same number of competitors. Each seller gets a same number of demands. Each buyer gets a same number of supplies....... a[2]a[m]...... Item b[2] Item b[n] Item Full connected b[1] a[1]

50. Individual Competition (IC) A buyer requests i items. A seller has s supplies and sum(i) = d demands. is the probability that the buyer agent a will become supplied target for requested items from the seller agent b. If (s >= d), then If (s < d), then Item....... Item b[1]b[n] a[1]a[2]a[m]....... Individual connected

51. Apply to Conflict Probability IC = 1 do not affect to previous conflict probability. Lower IC makes higher conflict probability. IC = 0 makes conflict probability as 1. 10 1 0 IC Pc Previous Value Supply Demand ex) Higher demands make higher IC.

52. Individual Opportunity (IO) Learnt opponent eagerness,, will affect to opportunity. The probability that buyer agent a will obtain a utility v, with seller agent b: –If Pc = 0.0 : Pc -> 0.001 –If Pc < 0.5 : –If Pc = 0.5 : –If Pc > 0.5 : –If Pc = 1.0 : Pc -> 0.999

53. Revised Negotiation Strategy To bring close up to, the agent makes an amount of concession based on the time-dependent strategy: –when

54. Relationship among factors Individual Competition Supplies & Demands Individual Opportunity Conflict Probability Spread Plausible Offer Deadline & Present time Learnt Opponent Eagerness Offer Agent Eagerness Time Strategy Next Bid

55. Negotiation Results Each value shows: Bid Price Learnt Opponent Eagerness Individual Opportunity

56. Negotiation Results Each value shows: Bid Price Learnt Opponent Eagerness Individual Opportunity

57. References: http://www.csc.liv.ac.uk/~mjw/pubs/gdn2001.pdf http://www.ecs.soton.ac.uk/~mml/papers/ker99-2.pdf http://crpit.com/confpapers/CRPITV4Rahwan.pdf http://xenia.media.mit.edu/~guttman/research/pubs/amet98.pdf http://www.umiacs.umd.edu/users/sarit/Articles/acai01.pdf http://www-agki.tzi.de/ecai00-mas/lopes.pdf