1. Agent-mediated Interaction. From Auctions to Negotiation and Argumentation Carles Sierra IIIA-CSIC Barcelona Utrecht, October 13, 2000 IIIA-CSIC

2. SIKS-dag 2000, Utrecht, 13/10/00 Talk plan Auctions: FISHMARKET Negotiation Argumentation Robot navigation Electronic Institutions

3. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 Introduction Agents inhabiting the same environment need to co-ordinate their activities to improve their individual or collective performance. The aim of DAI is to design intelligent sistems that behave efficiently. A common assumption in many applications, specially in AMEC, is that agents are self-interested and utility maximisers. In others, agents are co-operative. DAI is divided in two big areas: Distributed problem solving, where the designer determines the protocol and the strategy (relation between state and action) of each agent, and Multi Agent Systems, where the agents are provided with an interaction protocol but chose the strategy to follow.

4. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 Auctions

5. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 Auctions Auctions are mechanisms very frequent in MAS. They have been deeply analysed by economists. There are three types: 1) Of private value, e.g. a cake. 2) Of common value, e.g. treasure bonds. 3) Of correlated value, e.g. contracts. Protocols: English. If it is of private value, the strategy is to increase the bids until the reserve price. In those of correlated value the auctioneer may increase the price in predetermined amounts. Sealed bid. There is no dominant strategy. Dutch. Equivalent to sealed bid. They are very efficient. Vickrey. The dominant strategy is to bid for the reserve price.

6. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 Auctions: the Fishmarket Seller’s admitter

7. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 Auctions

8. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 Buyer and Electronic Panel

9. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 Scenes

10. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 Auction protocol

11. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 FM

12. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 eBuyers (browser)

13. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 eBuyers (agent)

14. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 eAuctioneer

15. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 Modems Interagent comprador Agent comprador Interagent venedor Agent venedor LAN Cap Admissió de compadors Gestió de compadors Subhastador Admissió de venedors Gestió de venedors Admissió de peix Auditor LLotja virtual Implementation Servidor

16. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 Tournaments

17. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00

18. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 Monitoring

19. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 FM 1.0: A test-bed for Electronic Auctions Realistic.Grown out of a complex real world application. Multi-user Architecturally neutral Customizability and repeatibility Agent-builder facility (Library of agent templates) Monitoring and Analysis facilities Market scenarios as tournament scenarios.

20. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 Negotiation

21. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 Bargaining In bargaining, agents may make deals that are mutually beneficial, but they are in conflict over which deal to chose. Negotiation mechanisms fall mainly on strategic bargaining. Axiomatic Theory. The desired solutions are not those found in a certain equilibrium, but those that satisfy a set of axioms. Classical axioms are those of Nash: outcome u*=(u 1 (o*), u 2 (o*)) must satisfy: Invariance: The numerical utilities of agents represent ordinal preferences, numerical values don’t matter. Thus, the utility functions must satisfy that for any f linear and increasing: u*(f(o), f(o fail ))=f(u*(o, o fail )) Anonimity: Changing the labels of the players does not affect the outcome. Independence of irrelevat alternatives: if we eliminate some o, but not o*, o* is still the solution. Pareto eficiency: we cannot give more utility to both players over u*=(u 1 (o*), u 2 (o*)).

22. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 Bargaining Strategic Theory: No axioms on the solution are given, the interaction is modelled as a game. The analysis consists on finding which strategies of the players are in equilibrium. It explains the behaviour of utility maximisers better than the axiomatic theory (where the notion of strategy does not make much sense). The theory of negotiation is basically here. Without assuming perfect rationality, the computational costs of the deliberation and the potential benefits of bargaining conflict. AI has many things to say on this task.

23. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 Negotiation Commerce is about interaction –Between buyers and sellers at all stages: finding, purchasing, delivery. First generation –Passive web query –Simple interactions: auctions Second generation –Rich and flexible interactions Negotiation is the key type of interaction –Process by which groups of agents communicate with one another to try and come to a mutually acceptable agreement on same matter. –Many forms exist: auctions, contract net, argumentation. –It is key because agents are autonomous: an acquaintance needs to be convinced to be influenced. –Negotiation is achieved by making proposals, trading options, offering concessions.

24. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 Negotiation components Negotiation objects. Issues of the agreements. Number of them, types of operations on them. Negotiation protocols. Rules that govern the interaction: permissible participants, valid actions, negotiation states. Agents reasoning model. Decision making apparatus. From simple bidding to complex argumentation. Challenges –Trust –Protocol engineering –Reasoning models

25. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 Negotiation object example Real State Agency. Seller b and buyer a. Issues={Address,Surface,Rooms,Brightness,Price,Garage} Negotiation thread:

26. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 Negotiation protocol

27. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 Negotiation reasoning model Each agent a negotiates over a number of issues that have a: 1) Delimited range [min j, max j ] 2) Monotonic scoring function V j a : [min j, max j ]-> [0,1] 3) Relative importance, w j a The utility function for an agent a has the following form: The negotiation protocol consists of an iterative process of offers and counteroffers until a deal is reached.

28. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 Tactic: Concession

29. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 Tactic: Imitative

30. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 Tactic: trade-offs Price:2 Quality:5 Price:9.9 Quality:1.1 ? A B

31. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 Trade-off Mechanism (I) Trade-off is lowering of utility on some issues and simultaneously demanding more on others. Steps: given x (a’s offer) and y (b’s offer) –(1) Generate all / subset of contracts with the same utility (  ) » iso a (  ) = {x | V a (x) =  } –(2) selection of a contract (x´) that agent a believes is most preferable by b. »B a (U b (x´) > U b (x)) »U a (x´) + U b (x´) > U a (x) + U b (x) (maximization of joint utility) »U a (x) = U b (x´) Step (2) is an uncertain evaluation: must model B a

32. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 Fuzzy Similarity Select a contract from iso a (  ) = {x | V a (x) =  } that is “closest” or most similar to y. Implications of this choice: –not the probable choice of the other, but rather, the closeness of two contracts »Not modeling of others but the domain –need a logic of degrees of truth (Zadeh) as opposed to binary truth values of true or false

33. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 Definition of Similarity Sim( ) defined as: Sim(x,y) =  j  J w j Sim j (x j,y j ) Sim j (x j,y j ) =  1  i  m (h i (x j )  h i (y j )) where w j is the agent´s belief about the importance the other places on each issue in negotiation h i ( ) is ith comparison criteria function (e.g warmth)  is the conjunction operator (e.g minimum)  is the equivalence operator (e.g 1-| h i (x j )-h i (y j )|)

34. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 An Example of Similarity D colours {yellow,orange,green,cyan,red,...} Similarity of colours according to different perceptive criteria: »Temperature (warm v.s cold colours) »Luminosity »Visibility »Memory »dynamicity h t = {(yellow, 0.9), (violet, 0.1), (magenta, 0.1), (green, 0.3), (cyan, 0.2), (red, 0.7),...} h l = {(yellow, 0.9), (violet, 0.3), (magenta, 0.6), (green, 0.6), (cyan, 0.4), (red, 0.8),...} h v = {(yellow, 1), (violet, 0.5), (magenta, 0.4), (green, 0.1), (cyan, 1), (red, 0.2),...}

35. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 Similarity of Colours Sim colour (yellow, green) = min( 1- |h t (yellow)- h t (green)|, 1-| h l (yellow)- h l (green)|, 1- |h v (yellow)- h v (green)|)= min(0.4,0.7,0.1) = 0.1 Sim colour (yellow, red) = min( 1- |h t (yellow)- h t (red)|, 1-| h l (yellow)- h l (red)|, 1- |h v (yellow)- h v (red)|)= min(0.8,0.9,0.2) = 0.2 yellow is more similar to red than to green on these criteria sim(yellow,green) and sim(yellow,red) sim colour (colour,colour) =  1  i  m (h i (x colour )  h i (y colour )) i={temperature,luminosity,visibility}

36. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 The Trade-off Algorithm y x x y ? X´ complexity  kn To be beneficial to the other the preference of the other must match the similarity function trade-off a (x,y) = arg max z  iso a (  ) {Sim(z,y)}

37. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 Tactic: Issue-set manipulation

38. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 CASBA general architecture

39. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 Agent Architectures

40. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 Case-based negotiating agent

41. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 Fuzzy Agent

42. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 GA populations

43. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 GA on negotiating agents

44. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 Argumentation

45. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 Argumentation Autonomy leads to negotiation and to argumentation. Many problems cannot be solved by a simple offer/counter offer negotiation protocol. When arguing, agent offers may include knowledge, information, explanations. The dialogue includes critiques on each others proposals. Agents must be able to generate arguments as well as rebutting and undercutting other agents’ arguments. Which argument to prefer may depend on logical criteria or on social considerations. A logically-based approach to building agents seems natural.

46. A B + +  Hang Mirror ++  Hang Picture Hang Mirror ++  Hang Mirror S S

47. A B + +  Hang Mirror ++  Hang Picture ++  Hang Mirror I know agent B has a nail S S

48. A B + +  Hang Mirror ++  Hang Picture ++  Hang Mirror ? S S

49. A B + +  Hang Mirror ++  Hang Picture ++  Hang Mirror ++  Hang Mirror S S

50. A B + +  Hang Mirror ++  Hang Picture ++  Hang Mirror ++  Hang Mirror S S S S ++  Hang Mirror

51. A B + +  Hang Mirror ++  Hang Picture ++  Hang Mirror + +  Hang Mirror ? S S S

52. A B + +  Hang Mirror ++  Hang Picture ++  Hang Mirror + +  Hang Mirror S S S

53. A B + +  Hang Mirror ++  Hang Picture ++  Hang Mirror + +  Hang Mirror ? S S S

54. A B + +  Hang Mirror ++  Hang Picture ++  Hang Mirror + +  Hang Mirror S S S

55. A B + +  Hang Mirror ++  Hang Picture ++  Hang Mirror + +  Hang Mirror ? S S S S

56. A B + +  Hang Mirror ++  Hang Picture ++  Hang Mirror + +  Hang Mirror S S S

57. A B + +  Hang Mirror ++  Hang Picture ++  Hang Mirror + +  Hang Mirror OK!!! S S S

58. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 Multi-context agents Units: Structural entities representing the main components of the architecture. Logics: Declarative languages, each with a set of axioms and a number of rules of inference. Each unit has a single logic associated with it. Theories: Sets of formulae written in the logic associated with a unit. Bridge Rules: Rules of inference which relate formulae in different units.

59. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 planner undercutting module rebutting module resource manager social manager goal manager An argumentative agent

60. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 GOAL MANAGER A module

61. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 DONE:G:goal(X),R:X ==> G:done(X) ASK:G:goal(X),G:not(done(ask(X))),G:not(done(X)),R:not(X),P:not(plan(X,Z)) ==> CU:ask(self/G,self/All,goal(X),[]),G:done(ask(X)) RESOURCE: CU>answer(self/RM,self/G,have(X,Z),[])==> R:X PLAN: CU>answer(self/_,self/G,goal(Z),P)==> P:plan(Z,P) MONITOR: G:goal(X),R:not(X),P:plan(X,P) ==> G:monitor(X,Z) NEW_GOAL: CU>inform(self/_,self/_,newGoal(X),_) ==> G:goal(X) FREE: R:X,GM:not(goal(X,_)) ==> R:free(X) FREE2: R>free(X),R>X ==> CU:free(X) FAILURE_R: R>done(ask(X,Y))FAILURE_P: P>done(ask(X,Y)) [t1] [t2] ==> GM:fail_R(X,Y) ==> GM:fail_P(X,Y) Bridge rules

62. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 Robot navigation

63. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 The problem Outdoor unknown environment navigation Legged robot No precise odometry (or very imprecise one) No location system (GPS) Visual feedback only No distance to objects estimation

64. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 Objectives Landmark based navigation (robust, animal-like) With the aim of leading the robot to an initially given visual target in an unknown environment Qualitative navigation (fuzzy distances) Map generation (topological, landmark based)

65. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 Robot Architecture Navigation System Pilot System Vision System RobotCamera Target information bids actions Look for target Identify landmarks Move to direction bids actions

66. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 Example Obstacle avoidance

67. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 Multiagent Navigation System MMTTRMREDE CO bids bids and illocutions information MM: Map Manager TT: Target Tracker RM: Risk Manager RE: REscuer DE: Distance Estimator CO: COmmunicator

68. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 Example Obstacle avoidance Topological map Landmark regions

69. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 Electronic Institutions

70. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 Electronic Institutions “Institutions are the rules of the game in a society or, more formaly, are the humanly devised constraints that shape human interaction” “The major role of institutions in a society is to reduce uncertainty by establishing a stable (but not necessarily efficient) structure for human interaction” D.C.North: Institutions, Institutional Change and Economic Performance. Cambridge (1990)

71. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 Agent-Mediated Institutions (fundamental elements) Role. Standardized patterns of behaviour required of all agents playing a part in a given functional relationship. Agent. The players of the institution. Each agent may take on several roles. Dialogic Framework. Ontologic elements and communication language (ACL) employed during an agent interaction. Scene. Agent meetings whose interaction is shaped by a well- defined protocol. Each scene models a particular activity. Performative Structure. Complex activities composed of multiple scenes specified as connections among scenes. Normative Rules. Determine both subsequent commitments and constraints on (dialogic) agent actions.

72. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 Performative structure (rationale) Complex activities can be specified by establishing relationships among scenes that: capture causal dependency among scenes; define synchronisation mechanisms involving scenes; establish paralellism mechanisms involving scenes; define choice points that allow roles leaving a scene to choose which activity to engage in next; and establish the role flow policy among scenes.

73. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 Specification tool

74. IIIA-CSIC SIKS-dag 2000, Utrecht, 13/10/00 Final words The study of interaction becomes a cornerstone for intelligent systems. Need for platforms and specification languages to model interaction Challenges for negotiation: –Trust –Protocol standards –Preference modelling Challenges for engineering: –Adaptability and learning –Mobility –Open and closed market design Collaborators: Juan Antonio Rodriguez, Pablo Noriega, Peyman Faratin, Nick Jennings, Simon Parsons, Jordi Sabater, Noyda Matos, Didac Busquets, Ramon Lopez de Mantaras. Papers and software at http://www.iiia.csic.es