Agent-mediated Interaction.
From Auctions to Negotiation
and Argumentation
Carles Sierra
IIIA-CSIC
Barcelona
Utrecht, October 13, 2000
IIIA-CSIC
IIIA-CSIC
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 M AS. 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
AR
RR
ba
sa
B uyers' regi ster
Goods' regist er
AH
auct
Goods' show and
auct ion
Credi ts and goods
del ivery
Seller’s admitter
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S ell ers' sett lem ents
bm
sm
DR
BO
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Auctions
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Buyer and Electronic Panel
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Scenes
AR
RR
ba
sa
B uyers' regi ster
Goods' regist er
AH
auct
Goods' show and
auct ion
Credi ts and goods
del ivery
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S ell ers' sett lem ents
bm
sm
DR
BO
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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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Implementation
LAN
LLotja virtual
Auditor
Modems
Cap
Servidor
Admissió de
compadors
Gestió de
compadors
Subhastador
Admissió de
venedors
Admissió
de peix
Gestió de
venedors
Interagent
comprador
Interagent
venedor
Agent
comprador
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Agent
venedor
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Tournaments
Para ver esta película, debe
disponer de QuickTime™ y de
un descompresor Microsoft Video 1.
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Para ver esta película, debe
disponer de QuickTime™ y de
un descompresor Microsoft Video 1.
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Monitoring
Para ver esta película, debe
disponer de QuickTime™ y de
un descompresor Microsoft Video 1.
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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: X tab  {xtab , xtba , xtab , xtba , xtab ,accept}
5
xt
1
a b
xt
2
b a
xt
3
a b
xt
4
b a
xt
5
a b
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1
2
3
4
 [?,140m2 ,4,Very _ Bright ,$400K]
 [# 21,60m2 ,4,Slightly _ Bright ,$400K]
 [?,120m2 ,4,Very _ Bright ,$400K]
 [# 69,120m2 ,3, Bright ,$600K,true]
 [# 69,120m2 ,3, Bright ,$500K,true]
5
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Negotiation 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:
V
a
(x) 
w V ( x )
a
j
a
j
j
i j n
The negotiation protocol consists of an iterative process of offers
and counteroffers until a deal is reached.
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Tactic: Concession
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Tactic: Imitative
Tactic: trade-offs
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Price:2
Quality:5
B
?
Price:9.9
Quality:1.1
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A
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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) = 1i  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
• sim(yellow,green) and sim(yellow,red)
• simcolour(colour,colour) = 1i  m(hi(xcolour)  hi(ycolour))
• i={temperature,luminosity,visibility}
• 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
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The Trade-off Algorithm
To be beneficial to the other the preference of the other must
match the similarity function
y
trade-offa(x,y) = arg maxz iso () {Sim(z,y)}
a
y
?
x
complexity  kn
X´
x
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Tactic: Issue-set manipulation
Y
Ships = 4
Price = 80
Quantity = 2
Spain
UK
Ships = 10
Price = 55
Quantity = 10
X
X1
Ships = 8
Quantity = 6
X2
Price = 5 0
Q uan tity = 9
Sh ips = 8
Q uan tity = 6
Sh ips = 12
Price = 5 0
X3
Sim()
W_Ships = 0.3
1
h1(ships)
4 8 12 16 20 24
W_quantity = 0.3
W_Price = 0.4
1
h2(price)
50
60 70
1
80
h3(quantity)
2 4 6 8 10
s i m ( x 1 , y ) = ( ( 0 . 4 * 1 - (0.9 - 0.01)) + ( 0. 3 * 1 - ( 0.3 - 0.1) )) = 0.28
s i m ( x 2 , y ) = ( ( 0 . 3 * 1 - (0.1 - 0.08) ) + ( 0.3 * 1 - ( 0.1 - 0.1))) = 0.59
s i m ( x 3 , y ) = ( ( 0 . 3 * 1 - (0.4 - 0.08)) + ( 0.4 * 1 - ( 0.9 - 0.01))) = 0.25
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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.
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S
+
S+
-> Hang Mirror
+
+
-> Hang Picture
A
Hang Picture
+
Hang Mirror
+
-> Hang Mirror
B
S
+
S+
-> Hang Mirror
+
+
-> Hang Picture
A
I know agent B
has a nail
+
+
-> Hang Mirror
B
S
+
S+
-> Hang Mirror
+
+
-> Hang Picture
A
+
?
+
-> Hang Mirror
B
S
+
S+
-> Hang Mirror
+
+
-> Hang Picture
A
+
+
+
-> Hang Mirror
+
-> Hang Mirror
B
S
+
S+
-> Hang Mirror
+
+
-> Hang Picture
A
+
S
+
+
S
+
->
Hang Mirror
+
->
Hang Mirror
+
-> Hang Mirror
B
S
+
S+
-> Hang Mirror
+
+
-> Hang Mirror
+
+
-> Hang Picture
+
S+
-> Hang Mirror
A
?
B
S
+
S+
-> Hang Mirror
+
+
-> Hang Mirror
+
+
-> Hang Picture
+
S+
-> Hang Mirror
A
B
S
+
S+
-> Hang Mirror
+
+
-> Hang Mirror
+
+
-> Hang Picture
+
S+
-> Hang Mirror
A
?
B
S
+
S+
-> Hang Mirror
+
+
-> Hang Mirror
+
+
-> Hang Picture
+
S+
-> Hang Mirror
A
B
S
+
S+
-> Hang Mirror
+
+
-> Hang Mirror
+
+
-> Hang Picture
+
S+
-> Hang Mirror
A
S
?
B
S
+
S+
-> Hang Mirror
+
+
-> Hang Mirror
+
+
-> Hang Picture
+
S+
-> Hang Mirror
A
B
S
+
S+
-> Hang Mirror
+
+
-> Hang Mirror
+
+
-> Hang Picture
+
S+
-> Hang Mirror
A
OK!!!
OK!!!
B
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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
resource
manager
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An argumentative agent
goal
manager
rebutting
module
social
manager
undercutting
module
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A module
GOAL MANAGER
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Bridge rules
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,_)) ==>
FREE2:
R>free(X),R>X ==> CU:free(X)
FAILURE_R:
R>done(ask(X,Y))
[t1]
==> GM:fail_R(X,Y)
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R:free(X)
FAILURE_P:
P>done(ask(X,Y))
[t2]
==> GM:fail_P(X,Y)
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Robot navigation
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• 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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The problem
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Objectives
• Landmark based navigation (robust, animal-like)
• Qualitative navigation (fuzzy distances)
• Map generation (topological, landmark based)
With the aim of leading the robot to an initially given
visual target in an unknown environment
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Robot Architecture
Target
Navigation
System
bids

Move to direction
Pilot
bids
bids
Vision
System
actions
actions
System
Robot
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Camera

Look for target

Identify landmarks
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Example
Obstacle avoidance
QuickTime™ and a
Microsoft Video 1 decompressor
are needed to see this picture.
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Multiagent Navigation System
MM
TT
RM
RE
bids and illocutions
DE
MM: Map Manager
TT: Target Tracker
RM: Risk Manager
RE: REscuer
CO
DE: Distance Estimator
CO: COmmunicator
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information
bids
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Obstacle avoidance
Landmark regions
Topological map
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Example
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Electronic
Institutions
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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)
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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 welldefined 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
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