agent-mediated interaction. from auctions to negotiation and argumentation carles sierra iiia-csic

Agent-mediated Interaction. From Auctions to Negotiation

and ArgumentationCarles Sierra

IIIA-CSIC

BarcelonaUtrecht, October 13, 2000

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SIKS-dag 2000, Utrecht, 13/10/00

Talk plan

Auctions: FISHMARKET

Negotiation

Argumentation

Robot navigation

Electronic Institutions

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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.

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Auctions

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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.

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Auctions: the Fishmarket

Seller’s admitter

aaa

Goods' registerGoods' show andauctionCredits and goodsdelivery

Buyers' registerSellers' settlementsBO

RR

DR

ARAH

bmsmauctsaba

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Auctions

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Buyer and Electronic Panel

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Scenes

aaa

Goods' registerGoods' show andauctionCredits and goodsdelivery

Buyers' registerSellers' settlementsBO

RR

DR

ARAH

bmsmauctsaba

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Auction protocol

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FM

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eBuyers (browser)

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eBuyers (agent)

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eAuctioneer

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Modems

Interagent comprador

Agent comprador

Interagentvenedor

Agentvenedor

LAN

Cap

Admissió de compadors

Gestió de compadors

Subhastador

Admissió devenedors

Gestió devenedors

Admissió de peix

AuditorLLotja virtual

Implementation

Servidor

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Para ver esta película, debedisponer de QuickTime™ y de

un descompresor Microsoft Video 1.

Tournaments

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Monitoring

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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.

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Negotiation

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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*=(u1(o*), u2(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(ofail))=f(u*(o, ofail))

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*=(u1(o*), u2(o*)).

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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.

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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.

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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

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Negotiation object example

Real State Agency. Seller b and buyer a.

Issues={Address,Surface,Rooms,Brightness,Price,Garage}

Negotiation thread:

a→ b1tx =[?,140m2,4,Very_Bright,$400K]

b→ a2tx =[#21,60m2,4,Slightly_Bright,$400K]

a→ b3tx =[?,120m2,4,Very_Bright,$400K]

b→ a4tx =[#69,120m2,3,Bright,$600K,true]

a→ b5tx =[#69,120m2,3,Bright,$500K,true]

a↔ b5tX ={ a→b

1tx , b→a2tx , a→b

3tx , b→a4tx , a→b

5tx ,accept}

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Negotiation protocol

345Initial stateFinal state

20Prenegotiation1

Issue protocolIssue protocol

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Negotiation reasoning model

Each agent a negotiates over a number of issues that have a:

1) Delimited range [minj, maxj]

2) Monotonic scoring function Vja: [minj, maxj]-> [0,1]

3) Relative importance, wja

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.

aV (x) = jaw j

aV ( jx)i≤j≤n∑

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Tactic: Concession

0,50,511 β=0,1β=0,02β=1β=10β=500,50,51

1β=0,1β=0,02β=1β=10β=50/t tmax

α( )t α( )t/t tmax

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Tactic: Imitative

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Tactic: trade-offs Price:2

Quality:5

Price:9.9Quality:1.1

? AB

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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 ()

» isoa() = {x | Va(x) = }

– (2) selection of a contract (x´) that agent a believes is most preferable by b.

» Ba (Ub(x´) > Ub(x))

» Ua(x´) + Ub(x´) > Ua(x) + Ub(x) (maximization of joint utility)

» Ua(x) = Ub(x´)

• Step (2) is an uncertain evaluation: must model Ba

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Fuzzy Similarity

• Select a contract from isoa() = {x | Va(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

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Definition of Similarity

• Sim( ) defined as:

Sim(x,y) = j J

wj Simj(xj,yj)

Simj(xj,yj) = 1i m

(hi(xj) hi(yj))

• where wj is the agent´s belief about the importance the other places on each issue in negotiation

• hi( ) is ith comparison criteria function (e.g warmth)

• is the conjunction operator (e.g minimum) is the equivalence operator (e.g 1-| hi(xj)-hi(yj)|)

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An Example of Similarity

• Dcolours{yellow,orange,green,cyan,red,...}

• Similarity of colours according to different perceptive criteria:» Temperature (warm v.s cold colours)

» Luminosity

» Visibility

» Memory

» dynamicity

ht = {(yellow, 0.9), (violet, 0.1), (magenta, 0.1), (green, 0.3), (cyan, 0.2), (red, 0.7),...}

hl = {(yellow, 0.9), (violet, 0.3), (magenta, 0.6), (green, 0.6), (cyan, 0.4), (red, 0.8),...}

hv = {(yellow, 1), (violet, 0.5), (magenta, 0.4), (green, 0.1), (cyan, 1), (red, 0.2),...}

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Similarity of Colours

• Simcolour(yellow, green) =min( 1- |ht(yellow)- ht(green)|,

1-| hl(yellow)- hl(green)|,

1- |hv(yellow)- hv(green)|)= min(0.4,0.7,0.1) = 0.1

• Simcolour(yellow, red) =min( 1- |ht(yellow)- ht(red)|,

1-| hl(yellow)- hl(red)|,

1- |hv(yellow)- hv(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)

• simcolour(colour,colour) = 1i m

(hi(xcolour) hi(ycolour))

• i={temperature,luminosity,visibility}

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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-offa(x,y) = arg maxz isoa() {Sim(z,y)}

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a

Sim()X

sim(x1,y)=((0.4 *1 - (0.9 - 0.01)) + ( 0. 3 * 1 - ( 0.3 - 0.1))) = 0.28sim(x2,y)=((0.3*1 - (0.1 - 0.08)) + ( 0.3 * 1 - ( 0.1 - 0.1))) = 0.59sim(x3,y)=((0.3*1 - (0.4 - 0.08)) + ( 0.4 * 1 -( 0.9 - 0.01))) = 0.25

Ships = 12Price = 50X1X2X3YSpain UKShips = 4Price = 80Quantity = 2Price = 50Quantity = 9Ships = 8Quantity = 6

Ships = 10Price = 55Quantity = 10Ships = 8Quantity = 614812162024W_Ships = 0.3h1(ships)1W_Price = 0.4h2(price)708050601246810W_quantity = 0.3h3(quantity)

Tactic: Issue-set manipulation

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CASBA general architecture

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Agent Architectures

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Case-based negotiating agent

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Fuzzy Agent

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GA populations

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GA on negotiating agents

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Argumentation

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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.

A B

+ + Hang Mirror

+ + Hang Picture

Hang Picture Hang Mirror

+ + Hang MirrorSS

SS

A B

+ + Hang Mirror

+ + Hang Picture

+ + Hang Mirror

I know agent Bhas a nail

SS

SS

A B

+ + Hang Mirror

+ + Hang Picture

+ + Hang Mirror

?

SS

SS

A B

+ + Hang Mirror

+ + Hang Picture

+ + Hang Mirror

+ + Hang Mirror

SS

SS

A B

+ + Hang Mirror

+ + Hang Picture

+ + Hang Mirror

+ + Hang Mirror

SS

SS

SS

SS

+ + Hang Mirror

A B

+ + Hang Mirror

+ + Hang Picture

+ + Hang Mirror

+ + Hang Mirror

?

SS

SS

SS

A B

+ + Hang Mirror

+ + Hang Picture

+ + Hang Mirror

+ + Hang Mirror

SS

SS

SS

A B

+ + Hang Mirror

+ + Hang Picture

+ + Hang Mirror

+ + Hang Mirror

?

SS

SS

SS

A B

+ + Hang Mirror

+ + Hang Picture

+ + Hang Mirror

+ + Hang Mirror

SS

SS

SS

A B

+ + Hang Mirror

+ + Hang Picture

+ + Hang Mirror

+ + Hang Mirror

?

SS

SS

SS

SS

A B

+ + Hang Mirror

+ + Hang Picture

+ + Hang Mirror

+ + Hang Mirror

SS

SS

SS

A B

+ + Hang Mirror

+ + Hang Picture

+ + Hang Mirror

+ + Hang Mirror

OK!!! OK!!!

SS

SS

SS

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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.

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planner

undercuttingmodule

rebuttingmodule

resourcemanager

socialmanager

goalmanager

An argumentative agent

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SIKS-dag 2000, Utrecht, 13/10/00 GOAL MANAGERGOAL MANAGER

A module

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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

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Robot navigation

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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

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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)

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Robot Architecture

Navigation

System

Pilot

System

Vision

System

Robot Camera

Target

informationinf

ormati

on

bids

actions

Look for target

Identify landmarks

Move to directionbids bids

actions

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Example

Obstacle avoidance

QuickTime™ and aMicrosoft Video 1 decompressorare needed to see this picture.

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Multiagent Navigation System

MM TT RM RE DE

CO

bids

bids and illocutions

information

MM: Map Manager

TT: Target Tracker

RM: Risk Manager

RE: REscuer

DE: Distance Estimator

CO: COmmunicator

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Example

Obstacle avoidance

Topological map

Landmark regions

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“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)

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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.

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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.

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Specification tool

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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

agent-mediated interaction. from auctions to negotiation and argumentation carles sierra iiia-csic

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