Agent-mediated electronic commerce Carles Sierra IIIA-CSIC Barcelona

Agent-mediated electronic commerce

Carles SierraIIIA-CSICBarcelona

Tutorial plan

ContextAgents and eCommerce

Mechanisms

Example: The fishmarket

Example: Robot Navigation

Negotiation

Argumentation

Electronic Institutions

Future trends

Context

eCommerce evolution

• Electronic malls• Portals: aggregate information and commerce

resources, add services• Auction-centred sites• Vertical markets/portals• Wholesale, consumer aggregation• Differentiation: B2B, B2C, C2C• Latest growth: B2B vertical markets

Internet growth

• 17 million users in 1992

• 195 million users world-wide in 1999

• Disparity: Sweden 40.9%, Italy 8% of usage

• Users already sampled buying over the web (f.i. 40% in the UK)

• Many regular shoppers (f.i. 10% in the UK)

Expenses produced

• Disparity

• Shoppers in Europe• Revenue 1999 • Shoppers 2002• Revenue 2002

• Finland 20 times more than Spain.

• 5.2 million• EUR3,032 million• 28.8 million• EUR57,210 million

Examples• C2C auctions: eBay

– Consumer to consumer auctions– Services: auction engine, reputation management– Revenue source: advertising, auction commissions

• B2C: amazon– Catalog-based buying, auctions– Services: transactions, delivery, recommendation– Revenue source: advertising

• C2B: priceline– Reverse auction

• B2B: chendex, partMiner, metalsite– Catalog, auction– Revenue source: membership, per-transaction fees.

Current eMarkets

• The market is Internet!– Sellers. Usualy electronic, automated sites– Buyers. Not automated, humans.– Third parties. Few. Limited services (shopbots)

• Buying is based on buyers visiting sellers. eMarketplaces

• Protocols:– Single attribute auctions (several types)– Buying from catalog– Reverse auctions

Buyers/Sellers relationship• One buyer, one seller (1:1)

– A buyer interacts with a single seller at a single site– Trade has no regard to other buyers and sellers– Example: buying a book at amazon.com

• Many buyers, one seller (N:1)– Many buyers visit a single sales site– Trade depends on other buyers (auctions)

• One buyer, many sellers (1:M)– A buyer visits multiple sites simultaneously– Negotiation is possible. No regards to other buyers

• Many buyers, many sellers (N:M)– Many buers visit many sites– Coordination is possible– Many buyers and sellers visit a single site (exchange)

eMarketplace assumptions

• Single point: a market is a single meeting point for buyers and sellers

• Time spending: buyers are willing to spend time.• Limited privacy: buyers are willing to surrender

private information to sellers• Price dominance: price is the main affector of

buying decisions.• No collusion: buyers/sellers do not collude.• Familiarity: buyers can locate needed sellers.• Interoperability: all sites understand each other

Do assumptions hold?

• Single point: electronic trade takes place at multiple sites, possibly inter-correlated; buyers may have myriad alternative markets.

• Time spending: buyers prefer to reduce time spent.• Limited privacy: buyers may prefer not to reveal private

information.• Price dominance: other attributes are important too: delivery,

quality recommendations, etc.• No collusion: human players may not collude (but electronic ones

may).• Familiarity: buyers do not necessarily know sellers and how to

find them across markets.• Interoperability: each site is developed by a different company

with possibly different ontologies.

• Buyers’ mechanisms for participating in multiple markets (1:M), selection of better ones

• Efficient mechanisms for locating markets, sellers, other buyers• Interoperation standards: language, protocol, ontology• Buyer tools for time-efficient buying• Seller tools for dynamic pricing, promotion• Buyer, seller negotiation protocols and strategies• Enforceable, or self-enforceable contracts• Trust mechanisms• Means for payment and goods’ transaction• Means for secure transactions• Mechanisms for keeping players’ privacy• Tools for analyzing market performance• Protocols and tools for N:M interaction and trade.

Agents and eCommerce

Why Agents may help EC

• Autonomy. Agents work proactively, reactively and independently of human intervention. They can wait for good deals without diverting our attention.

• Personalisation. Agents can be equipped with a personal profile to reflect preferences.

• Social ability. The communication ability of agents can be used to negotiate over prices, services and transactions.

• Intelligence. Agents can learn and hence perform better over time. In EC scenarios this may equate to making more money. Many AI techniques can be applied.

Agents in ecommerce

• Product brokers:– Jango– PersonaLogic– Firefly

• Merchant brokering– Bargainfinder, Jango, Kasbah

• Buyer broker– Eyes.

• Negotiation through trusted third parties– Kasbah, Auctionbot, Fishmarket

Basic limitation: Only price brokering. No product differentiation.

Two examples

Jango & PersonaLogic

Personalized seller

• A personal seller is (usually) an animated character that uses conversational interactions to help a customer to use an electronic commerce site.

• It suggests products acording to a user profile and to his/her preferences.

• It helps as an answer to a user demand or proactively.

• It can be adapted through learning.

MAS for eCommerce

• Ad-hoc applications– Contract allocation. MAGNET.– Power load management as a computational market.– Control of shipment processes (EDI). Maquiladora.

• Generic applications– Auctions

• Auctionbot• Fishmarket

– Virtual markets• Kasbah• Bazar• Metamall

Mechanisms

Voting

Auctions

Bargaining

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 systems 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.

Protocol Avaluation Criteria

Social Welfare: Is the addition of the utilities of all the agents for a given solution. It is a global measure a bit controversial given the difficulty in comparing the different utility functions.

Pareto efficiency: A solution x is pareto efficient if there is no other x’ such that some agent improves without anyone else losing utility. It is also a global measure. Solutions that maximise the social welfare are a subset of those pareto efficients.

Individual rationality: The participation of an agent in a negotiation is rational if the benefit it gets from the negotiated solution is not smaller that the benefit of not negotiating. A mechanism, or protocol, is individualoly rational if the participation is rational for all agents. Only such mechanisms are feasible.

Stability: A protocol is stable if it is designed in such a way that motivates (selfish) agents to behave in a particular way. Those behaviours are called dominant strategies. When the preferred strategy for an agent depends on the strategies of others we have other criteria for stability:

Nash equilibrium: Strategies S*(A)=<S*(1), ...,S*(|A|)> are in equilibrium if for each agent i, S*(i) is the best strategy suposing that the others follow <S*(1), ...,S*(i-1),S*(i+1), ...,S*(|A|)>

In many ocasions there is no equilibrium, in others, there are several. Moreover, agents can form coallitions to deviate from the behaviour in the equilibrium. Also, eficiency and stability may conflict. For example (prisoner’s dilema):

D3,3 0,5

5,0 1,1

Computational eficiency: Mechanisms should be designed in a way that gives the minimum computational cost to the agent.

Distribution and communication: Distributed protocols are preferred to centralised ones because they are more robust. A trade-off with the amount of communication must be found.

In this introduction we’ll survey three coordination mechanisms:

• Votings• Auctions• Bargaining

VotingsIn a voting all agents give an input to a mechanism and the result of

the mechanism is a solution for all agents. A, Agents, O, Outcomes, every agent has a preference relation over O, (>1, ..., >|

A|). We want a >* that represents the social preference, and that

satisfies:

1) >* should exist for all sets of inputs.

2) >* Should be defined for all pairs o,o’ d’O.

3) >* Asimetric and transitive.

4) The result should be pareto efficient: if for all i o >i o’ then o >* o’.

5) The schema should be independent of irrelevant alternatives.

6) No dictators! That is, there must be no i such that o >i o’ implies

o >* o’ independently of the others.

Theorem [Arrow] No election rule satisfies all requirements.

The different voting mechanisms relax some of the points. Unfortunately the sixth is very often relaxed.

Usually the first one is relaxed. By relaxing the third we have the plural protocol (the usual in democratic systems). By introducing an irrelevant alternative we may get that a less prefered outcome wins.

The binary protocol, on top of this problem it also gives different results depending on the order of the pairings.

More relaxAnother example is the Borda protocol, that assigns |O| points to

the most preferred alternative, |O|-1 to the next, and so up to the last. Points are added up and the alternative with more points wins. With this protocol we can also have some paradoxes when we eliminate one alternative.

The design of social mechanisms tries to define them in a way

that no one cheats. For instance, random choice.

AuctionsAuctions 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.

Auctions

• A centralized protocol: one auctioneer and many buyers

• The auctioneer puts a good for sale. Goods can sometimes be bundled or may have different attributes

• The buyers make offers

• The auctioneer determines who wins

Auctions: pros and cons

• Usually easier to prevent bidder lying

• Simple protocol

• Centralized: a single point of failure

• Allows collusion “behind the scenes”

• May favour the auctioneer

English

• Bidders free to raise their bid• End: no more raises, winner: highest bidder at bid• Strategy: a series of bids, based on private value,

estimates of others’ valuations, their past bids• Dominant strategy: bid a small amount more than

current highest bid, stop when private value reached

• For correlated value, auctioneer increases price by constant or other rate

First-price sealed-bid auction

• Each bidder submits a bid not knowing others’• Highest wins, pays his bid• Strategy: function of private value and beliefs about

others’ valuations• No dominant strategy. Best: bid less than true value• How much less? Nash is computable if probability

distribution of agents’ values is known• Example: n agents, uniform value distribution, agent

i has value vi, there is Nash if each agent i bids vi(n-1/n)

Dutch auction

• The auctioneer lowers the price until a bidder takes it

• The first bidder to speak takes it

• Strategy: equivalent to first-price sealed bid

• Advantage: auctioneer can do it fast!

Vickrey (second-price sealed-bid)

• Each bidder submits one bid, not knowing others’• The highest bid wins but pays the second price• Strategy: base bid on private value and beliefs about

others’ values• Dominant strategy: bid true valuation

– If bids more and this increment made him win, the agent may end up with a loss, since it may pay more than its true value

– If it bids less, there is a smaller chance of winning, and the winner may end up paying less than his true value

• Therefore: bid true value regardless of others

Which auction to choose

• Computation criterion. Auctions with dominant strategies (Vickrey and English) are more efficient - no need to speculate regarding other bidders.

• Auctioneers revenue:– Second-price is less than the true price, however first

price bidders under-bid. Which effect is stronger?– For risk-neutral bidders with private independent values,

the effects are equivalent– For risk-averse bidders, dutch and first-price sealed-bid

auctions maximize auctioneer’s revenue

• So, are revenues equivalent?

Real auctions

• In real auctions, values are not private• As a result, for 3 or more bidders, English

auctions provide auctioneer revenue higher than Vickrey does

• Explanation: when it observes other bidders increasing their bid, a bidder increases its own valuation

• Both English and Vickrey are better for the auctioneer than Dutch and first-price sealed-bid.

Collusion

• Bidders can coordinate their bids to lower them• In English and Vickrey auctions collusion is

dominant!• Example.

– Agents a, b and c values of the good are 10, 10, 12, respectively

– They can agree to bid 5, 5, 6, respectively

– If one defects, all observe that, and can increase to real value, so there is no benefit from defection

Avoiding collusion

• In the first-price sealed-bid and Dutch auctions, bidder collusion is not dominant, but possible:– In the previous example, after a, b, c decided on 5,5,6 it is

beneficial for a, and b to bid more than 5. For any bid of c below 10 they can bid and win.

• In sealed bid, Dutch and Vickrey all bidders must identify each other and collude jointly. External bidder can win.

• In the English auction identifying is through bidding. Computerized anonymization can prevent identification and collusion.

Insincere Auctioneer

• Private value auctions– Vickrey: auctioneer can overstate the second

highest bid to the winner– Solution: electronic signature

• Non-private value– English: auctioneer can use shills that bid in the

auction to increase real bidders valuation– Any auction: auctioneer may bid, to guarantee a

minimum price

Revenue

In private or common value auctions, the four types of protocol are pareto eficient, they assign the resource to the buyers that value them most. English and Vickrey are more efficient because they have a dominant strategy. Buyers don’t need to think on what the others are going to do.

Theorem (revenue equivalence) The four protocols produce the same revenue to the auctioneer in auctions of private value with the values distributed independently and with risk-neutral buyers.

The protocols are not completely protected against buyer coalitions, although sealed bid and Dutch do not favor collution. The electronic versions of the protocols go against collutions because they may avoid the mutual identification of buyers.

BargainingIn 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.

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*)).

Nash bargaining solution:o*=arg maxo [u1(o)- u1(ofallback) ] [u2(o)- u2(ofallback) ]

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 (and Agents) has many things to say on this task.

BargainingIn 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*)).

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.

An application: The FishMarket

Fish Auction in Blanes ‘Llotja’

ACBuyers registration

Fish show and auction

Fishermen payments

Fish and sellers registration

Fish delivery and payment

BUYERS’ ADMISSION SELLERS’ ADMISSION

AUCTIONEER

SELLERS’ SETTLEMENTSBUYER’S SETTLEMENTS

Virtual fish auction

Auction bossActivates the FishMarket and controls all auctioning process. It may intervene talking to other agents. Closes the auction and shuts down the program.He customizes the program

Sets the auction parameters

Auction boss

Controls the auction and closes the it whenall processes are dead.

Auction boss

Fish admission

The fish admissor interacts with the program

through a browser i has the following

functionalities• Input the fish characteristics for its

identification and packaging• classification in boxes• Initial price setting

Auctioneer

In FM0.9 the auctioneer agent interacts with the system through a browser in

which the actual information of the auction is displayed: which buyers and

sellers participate, which round is the auction in, what product is being

auctioned, initial pice, etc. The browser offers the following functionalities:

•Control the auctioning process

•Select the box to auction at any time

•Change the starting price

•Start the round

•Decide on multiple collisions

•Expell buyers due to insuficent credit

•Etc.

Auctioneer

Buyers

Buyers can interact with the auction house (buyers’ admitter, buyers’ settlements and auctioneer) through a browser:

Buyer identification

Messages to and from the other agents in the auction house

Where in the auction house is

the buyer

Catalogue of the products to be

auctioned

Buyers in the auction house

Buyer credit

Round number

Auctioned product

Seller ID

Seller name

Initial price

Buyers

Bidding price

Winning price

Credit update

To bid

Buyers

Information on the products sold

To go to different places in the auction house

To bid To update the credit

To leave the auction house

Information in the evolution of the

auction

Sellers

Sellers interact with the auction house (sellers’ admitter, sellers’ settlements) through a browser :

Seller ID

Messages from the other agents

in the market

Sold products

Session earnings

To go to different places in the auction house

To include products in the market catalogue

To leave the market

Sellers

Modems

Buyer agent

Human buyer

Seller agent

Human sellet

Buyers’ admitter

Buyers’ settlements

Auctioneer

Sellers’ admitter

Sellers’ settlements

Fish admitter

AccountantLLotja virtual

Implementation

Servidor

Monitoring

Para ver esta película, debedisponer de QuickTime™ y de

un descompresor Microsoft Video 1.

Tournements

eBuyer

Animation

un descompresor Vídeo.

Robot navigation

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

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)

Robot Architecture

Navigation

System

Vision

System

Robot Camera

Target

informationinf

ormati

actions

Look for target

Identify landmarks

Move to directionbids bids

actions

Multiagent Navigation System

MM TT RM RE DE

bids and illocutions

information

MM: Map Manager

TT: Target Tracker

RM: Risk Manager

RE: REscuer

DE: Distance Estimator

CO: COmmunicator

Example

Obstacle avoidance

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

Example

Obstacle avoidance

Topological map

Landmark regions

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.

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

Negotiation object exampleReal 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}

Negotiation protocol

345Initial stateFinal state

20Prenegotiation1

Issue protocolIssue protocol

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∑

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

Tactic: Imitative

Tactic: trade-offs Price:2

Quality:5

Price:9.9Quality:1.1

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

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

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)|)

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),...}

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}

The Trade-off Algorithm

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)}

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

Agent Architectures

Case-based negotiating agent

Fuzzy Agent

GA populations

GA on negotiating agents

Argumentation

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.

+ + Hang Mirror

+ + Hang Picture

Hang Picture Hang Mirror

+ + Hang MirrorSS

+ + Hang Mirror

+ + Hang Picture

+ + Hang Mirror

I know agent Bhas a nail

+ + Hang Mirror

+ + Hang Picture

+ + Hang Mirror

+ + Hang Picture

+ + Hang Mirror

+ + Hang Picture

+ + Hang Mirror

+ + Hang Picture

+ + Hang Mirror

+ + Hang Picture

+ + Hang Mirror

+ + Hang Picture

+ + Hang Mirror

+ + Hang Picture

+ + Hang Mirror

+ + Hang Picture

+ + Hang Mirror

+ + Hang Picture

+ + Hang Mirror

+ + Hang Picture

+ + Hang Mirror

OK!!! OK!!!

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.

planner

undercuttingmodule

rebuttingmodule

resourcemanager

socialmanager

goalmanager

An argumentative agent

GOAL MANAGERGOAL MANAGER

A module

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

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)

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.

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.

Specification tool

Future trends

Challenges

• Relate ideal and actual agent behaviour• Relate roles of agents and their inherent

qualities• Generate ontologies, interaction standards

and social conventions• Generate new products, services and

practices

Interaction

• Dramatic change of supply chains

• Decrease of customer’s prices

• Old markets will change

• New markets for commodities soon– Power, telephone, bandwidth, …

• Auctions intermediated with agents

Negotiation

• Soon: Applications where– Interactions are very fast– Interactions are repeated– Each trade is of relative small value– The process is repeated over long times

• Need for a significant value

• Preference elicitation complex. Need for learning

– The product is relatively easy to specify

Challenges for negotiation

• Trust– Confidentiality, integrity, authentication and

non-repudiation.– Safe payment and delivery– Supervised interaction first– Reputation (e.g. via Chambers of Commerce)

• Protocol standards

Challenges for negotiation

• Preference modelling– Dynamics of preferences– Different ontologies– Fuzziness– Learning

• Argumentation• Protocols

– From fixed to dynamic– Negotiation of protocols

Applications

• Travel agencies

• Retail industry:Sainsbury and Otto Versand

• Auction house for general trade

• Procurement applications (Bangeman’s challenge winners)

Engineering

• Adaptability• Mobility• Trust modelling• Legal issues• Open vs closed markets• Electronic Institutions: Heterogeneity, trust

and scalability, exception handling and societal change

Other challenges

• New products: market places and agent servers

• New standards: FIPA, W3C, P3P. FIPA and OMG signed agreement

• Security: preferences, public key servers and signature management by agent servers

• Privacy protection according to EU 95/46 directive

Vision

• Mobile devices

• Context perception

• Deregulation

• Disappearing computer

That is:To put the customer at the heart of

the business

http://www.iiia.csic.es/~sierra

sierra@iiia.csic.es

A tool: Islander

Basic intuitionsThere are situations where individuals interact in ways that involve

Commitment, delegation, repetition, liability and risk.These situations involve participants that are:

Autonomous, heterogeneous, independent, not-benevolent, not-reliable, liable.

These situations are not uncommon: Markets, medical services, armies and many more.

It is usual to resort to trusted third parties whose aim is to make those interactions effective by establishing and enforcing conventions that standardize interactions, allocate risks, establish safeguards and guarantee that certain intended actions actually take place and unwanted situations are prevented.

These functions have been the basis for the development of many human institutions. They are even more necessary when interaction is among agents.

Concerns of an (electronic) institution

• Manage the identity of participants

• Define and validate requirements on participant capabilities

• Establish interaction conventions

• Facilitate effective interactions

• Enforce satisfaction of commitments

Three Fundamental Elements

Dialogic Framework: Linguistic and ontological conventions to make efficient communication among agents.

Performative Structure: Complex activities specified as connections among scenes (agents meetings whose interaction is shaped by a well-defined protocol).

Norms: Consequences of agent actions within scenes.

Dialogic Framework

• We define a dialogical framework as a tuple DF = <O, I, L, RI, RE, RS> where:

– O stands for the ontology;

– I is the set of illocutionary particles;

– L stands for a representation language;

– RI is the set of internal roles;

– RE is the set of external roles; and

– RS is the list of relationships over roles;

Communication Language• CL expresions are formulae of the form

(i (αi ri) β ) where:– i is an illocutionary particle in I; αi can be either an agent variable or an agent identifier;– ri can be either a role variable or a role identifier in RI RE; β represents the adressee(s) of the message and can be:

• (αk rk) the message is addressed to a single agent.• rk the message is addressed to all the agents playing the role rk.

• “all” the message is addressed to all the agents of the scene. is an expression in the content language. can be either a time variable or a value time stamping the illocution

FM dialogic framework

FM dialogical framework

• Scene is a pattern of multi-agent conversation.• Scene is specified by a finite state oriented graph where

the nodes represent the different states and oriented arcs are labelled with illocution schemes or timeouts.

• During the execution new agents can join the scene or some of the participants can leave the scene at definite states depending on their role.

• An scene can be multiple-instantiated and played by different groups of agents.

• The graph has a single initial state non reachable once left.• Set of final states not connected to other states. • For each role is defined a set of access and exit states.• Minimum and maximum number of agents per role• Final states must be exit states for each role.• Initial state must be an access state for each role which

minimum is greater than zero.

Auction room scene

rra. 1(inform (?x auctioneer) (buyer) open_auction(?n))

2(inform (!x auctioneer) (buyer) (open-round(?r))

3(inform (!x auctioneer) (buyer) to-sell(?good-id))

4(inform (!x auctioneer) (buyer) buyers(?buyers_list))

5(inform (!x auctioneer) (buyer) offer(!good-id,?price))

6(inform (!x auctioneer) (buyer) offer(!good-id,?price))

7,8(commit (?y buyer) (!x auctioneer) bid(!good_id, ?price))

9(inform (!x auctioneer) (buyer) withdrawn(!good-id))

10(inform (!x auctioneer) (buyer) collision(?price))

11(inform (!x auctioneer) (buyer) sanction(?buyer-id))

12(inform (!x auctioneer) (buyer) expulsion(?buyer-id))

13(inform (!x auctioneer) (buyer) sold(!good-id,?price, ?buyer-id))

14,15(inform (!x auctioneer) (buyer) end-round(!r))

16(inform (!x auctioneer) (buyer) end-auction(!n) )

Auction room scene

Performative Structure

• Complex activities can be specified by establishing relationships among scenes that:

– capture causal dependency.

– define synchronisation mechanisms.

– establish parallelism mechanisms.

– define choice points that allow roles leaving a scene to choose which activity to engage in next.

– establish the role flow policy.

Performative Structure

• A performative structure can be seen as a network of scenes.• We introduce transitions to mediate between connections of

scenes.• Arcs connecting scenes and transitions labelled with

constraints.• The specification allows to express that an scene can be

running simultaneously multiple-times at execution time. • Determines wether agents moving between scenes join

current executions of the target scene(s) or whether new executions are started.

FM performative structure

Norms• The normative rules defines the consequences of agents actions within the

institution.

• Such consequences are:

– some actions can impose obligations to agents.

– can vary the paths that agents can follow.

• We set out the predicate Obl for the notion of obligation.

– Obl(x,,s): meaning that agent x is obliged to do in scene s.

• Norms are specified by three elements:

– Antecedent: the actions that provoke the activation of the norm and restrictions over illocution scheme variables.

– Defeasible antecedent: the actions that agents must carry out in order to fulfill the obligations.

– Consequent: the set of obligations

• For instance, a buyer winning a bidding round is obliged to go later on to the buyers settlement scene to pay for the good.

Electronic Institution

An electronic institution is defined as a tuple EI = <DF, PS, N> where:

– DF stands for a dialogic framework.

– PS stands for a performative structure.

– N stands for a set of norms.

ISLANDER

• Textual specification language for electronic institutions.

• ISLANDER editor: specification and verification tool for electronic institutions.

• Combines textual and graphical specifications.• Verification

– Integrity– Liveness– Protocol Correctness– Norm Correctness

Islander

LoadImportFiles

Edition

Verification

GraphEditor

EditionPanels

SaveExportFiles

Islander

Textual

errorscorrect?

ISLANDER modules

Infrastructure

The infrastructure is in charge of:– allowing agents interaction– giving agents the information they need for participating in

the institution– checking that agents do not violate the institution rules.

Social infrastructure composed of:– Institution Manager– Governor Creator– Scene Managers– Governors

Governor

• Agent developed to mediate between the institution and participating agents.

• Each participating agent is connected to a governor that controls that it behaves according to the institution specification.

• Facilitates to the agent some information about the state of the institution.

• Coordinates with other agents of the infrastructure for the correct execution of the institution.

Institution Execution

Scene Execution

Institutions for AMEC: FM+

FM+ MAS

User 1

Buyer agents creation

Virtual auctions federation

Virtual auction 1

Buyerscoordination

User 2

User k

Real auction 1

Real auctionsUsers

... ......

Global DBexternal data

Virtual auction 2

Virtual Auction N

Real auction 2

Real auction N

Institutional Agents• Good Register (GR) Provides the virtual environment with all the

events related to the product entry from the real auction place. One agent for each auction place.

• Remote Control (SRC) Performs all the tasks to participate in an specific auction under the management of an agent with buyer role. It is an institution object, one for each buyer.

• Auction Broker (AB) Transmits the events related to the auction of the real world to the virtual agents who take part in the auction and to the DB. One agent for each auction place.

• DB Manager (DBM) Manages the database that contains information about all the auction places.

• Auction Admitter (aad) Controls the access of the buyers to the auction place. One agent for each auction place.

• Buyer Admitter (bad) Controls the access of the buyers to the auction place federation. One agent for the whole federation.

MASFIT Institution (Islander)

Scene example1. Auction-Broker: Inform(StartAuction)2. Auction-Broker -> RemoteControl:

Inform(StartRound)3. Auction-Broker ->RemoteControl:

Inform(Good to Sell,Price,Boxes)4. RemoteControl -> Auction-Broker:

Propose(Bid)5. RemoteControl -> Auction-Broker:

Propose(Bid)6. Auction-Broker -> RemoteControl:

Inform(Result)7. Auction-Broker->RemoteControl

Failure(Collision/Sanction/Expulsion)8. Auction-Broker -> RemoteControl:

Inform(Result)9. Auction-Broker -> RemoteControl:

Failure(Withdrawn)10. Auction-Broker -> RemoteControl:

Inform(EndAuction)

Real world

FM+ real world interaction

Inst. Agent

Auction Broker

Inst. Agent

Good Register

Specific Protocol

Auction Soft(AUTEC)

Human buyers

Virtual world

Agent buyers

Electronic Institution

GovernorGovernor

Agent-mediated electronic commerce Carles Sierra IIIA-CSIC Barcelona

Documents

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

Collaborative Assessment - CEUR-WS.org - CEUR …ceur-ws.org/Vol-1183/ffmi_paper03.pdf · Collaborative Assessment Patricia Gutierrez IIIA-CSIC Campus de la UAB Barcelona, ... marker/referee

VLCLAB - CARLES FAUS BORRAS

Marc Esteva (IIIA) Eva Onaindia (UPV) Sascha Ossowski (URJC) Enric Plaza (IIIA) Juan Antonio Rodríguez (IIIA) WP3: Organisations

On Mesoamerican Mathematics...On Mesoamerican Mathematics (A quick and not too scientific overview for Actuaries) Pablo Noriega B.V. IIIA-CSIC / LANIA . Barcelona / Xalapa .. Spain

Carles Oriach (Monocrom - ES)

digital-csic Digital CSIC

1999a_Luciernaga_Geol... - digital-csic Digital CSIC

Carles Boix

Presentacion alejandro campos carles

Architectural Design IIIA

Arte Islámico - digital-csic Digital CSIC

· A logical approach to fuzzy truth hedgesI Francesc Esteva, Llu´ıs Godo, Carles Noguera Artiﬁcial Intelligence Research Institute (IIIA – CSIC) Campus de la Universitat Aut`o

Clothes_ Carles Susierra

A framework for argumentation-based negotiation - IIIA …sierra/articles/1998/Argumentation.pdf · A framework for argumentation-based negotiation Carles Sierra , ... list based

Presentation iiia

Agent-mediated Interaction. From Auctions to Negotiation and Argumentation Carles Sierra IIIA-CSIC

Eng_computers ( Carles Farrerons )

THE TOURISM REVOLUTION IN THE MEDITERRANEAN, 1950- 2010 Carles Manera carles.manera@uib.es Professor Carles Manera1

Farmacia Iiia