On the Use of Optimization Techniques for Strategy Definition in Multi Issue Negotiations
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Transcript of On the Use of Optimization Techniques for Strategy Definition in Multi Issue Negotiations
ON THE USE OF OPTIMIZATION TECHNIQUES FOR STRATEGY DEFINITION IN MULTI ISSUE
NEGOTIATIONS
Κυριακή ΠαναγίδηΚυριακή Παναγίδη
Επιβλέπων Καθηγητής: Ευστάθιος Χατζηευθυμιάδης
Εθνικό και Καποδιστριακό Εθνικό και Καποδιστριακό Πανεπιστήμιο ΑθηνώνΠανεπιστήμιο Αθηνών
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Contents
Definitions Electronic Commerce Intelligent Software Agents Electronic Marketplaces Negotiations
Problem DefinitionStrategy DefinitionProposed AlgorithmsExperimentsConclusions Future Work
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“Optimizationas ageless
as time” …
Electronic Commerce
Electronic Commerce (E-Commerce) is defined by the Electronic Commerce Association as: “any form of business or administrative transaction or information exchange
that is executed using any information and communications technology” . “business practice related to buying and selling goods, products or services, in
the Internet”
Consumer Business
Consumer Consumer-to-Consumer
Example: Ebay
Consumer-to-Business
Example: PriceLine
Business Business-to-Consumer
Example: Amazon, Dell
Business-to-Business
Example: IBM, SAP
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Intelligent Software Agents - IAs
Intelligent software agents are programs acting on behalf of their human users”
“Intelligent software contains features as perception, interpretation of natural language, learning and decision making”
“A piece of software which performs a given task using information gleaned from its environment to act in a suitable manner so as to complete the task successfully. The software should be able to adapt itself based on changes occurring in its environment, so that a change in circumstances will still yield the intended result.”
“Software agents carry out certain operations on behalf of a user or another program with some degree of independence or autonomy combined with a set of goals or tasks for which they are designed”
“Intelligent Agents are computerized servants, it is software that communicates, cooperates and negotiates with each other. They have the ability to take over human tasks and interact with people in human like ways. They are bringing technology into a new dimension simplifying the use of computers, allowing humans to move away from complex programming languages creating a more human interaction” As a third
partyDegree of reasoning
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Intelligent Software Agents - IAs
Characteristics what the user needs
how is going to satisfy user
the IA should have the ability to modify the human user requests and ask for additional information or clarifications
Accept the user’s statement of goals and carry out the task delegated to it
take initiatives
Try to do what is asked for and act in order to achieve the user’s goals
recognize the user’s preference
interact with other IAs, programs or
humans
dynamically assess which actions to
execute and when
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Intelligent Software Agents- IAs
Types
CollaborativeIAs
Mobile IAs
Personal IAs
Network IAs
Desktop IAs
ApplicationDomains
Adaptive UserInterfaces
E-Commerce
Workflow andAdministrativeManagement
Information Accessand Management
Mail andMessaging
Collaboration
Mobile Access/Management
Systems andNetwork
Management
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Intelligent Software Agents- IAs
Barriers:• IAs should have access to their catalogues. • User goals have to be specified. • Users have to obtain information such as prices,
product’s issues, returning policies, delivery time, • Security problems may occur when submitting
sensitive information
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Electronic Marketplaces
“Virtual location where entities that are not known in advance can cooperate in order to achieve common goals. These entities have their own preferences and strategies”
Most of the proposed E-marketplace’s models are classified in the following two categories:
1. Direct transactions among providers and consumers2. IA-based brokered transactions
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Automated Negotiations
“a decentralized decision-making process used to search and arrive at an agreement that satisfies the requirements of two or more parties in the presence of limited common knowledge and conflicting preferences.”
“the process where entities try to agree upon the exchange of a product or as a mean of compromise, in order to reach mutual agreements.”
1. Electronic automated negotiation systems (EANSs)2. Negotiation support systems (NSSs)
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Electronic Negotiations
Properties in Automated Negotiations:1.Simplicity2.Efficiency3.Distribution4.Symmetry5.Stability6.Flexibility
CheckMarket
situation
Decidewhat to do
Search foroffers
Search foroffers
Post an offerand wait untila counter offer
Make acounter-offer
Make acounter-offer
Negotiate
Want tocomplete thenegotiation?
Complete the negotiation
No action
Start
Buy Sell
No offers
No
Yes
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Electronic Negotiations
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ManipulationStrategies
OptimizedPatient
Patient
Desperate
UsersStrategy
Argumentation
current bestsolution
opponent’sbehavior
Decisionselection
Mechanismfollowed
Number ofparticipants
Issuesinvolved
Negotiations
Many-to-Many
One-to-Many
One-to-One
Clearing-Middle member
Driven
Auctioning-Seller Driven
Bidding-Buyer Driven
Bargaining-Buyer driven
Many
Single
Electronic Negotiations-Problems
Real Life Negotiation ProblemsReal Life Negotiation Problems
Ill definedInformation not
equally distributedParticipants with
partial knowledge Communication is
ambiguous or imprecise
Complexity of Human behaviorComplexity of Human behavior
Multiple issues negotiation
Similar product suggestion
Correlated product suggestion
UltimatumNegotiation costLearning
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Problem Definition
Simultaneous
No Coordinator
No knowledge
Buyer Driven
One-to-many
Problem Definition
Product has a number of issues that increase or decrease each
player’s utility. An example : Price Delivery time Quality of Service (QoS) Seller’s trust
Problem Definition
Simultaneous
No Coordinator
No knowledge
Multi Issue
Buyer Driven
One-to-many
Problem Definition
Goal : Choose the best agreement
Problem is rising:“How do we evaluate two or more deals with
different issues/sets?”
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Problem Definition
Buyer i is in “worst case”1. Price2. QoS
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Problem Definition
“How do we evaluate two or more deals with different issues/sets?”
Answer: Utility
Restrictions:1. Proportional/ Not Proportional2. Ultimatum
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issuesmj
buyerssubNi
jv
m
jj
wiU
0
0 ,
1
Weights DefinitionWeights Definition Space ConvergingSpace Converging
Solve our problem like a mathematical problem, in which we change the weights of issues involved in negotiation
Studied algorithms:HeuristicSimplexAnalytical Hierarchy Process
Strategy Definition
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Solve Like in nature we assume our buyers like particles moving in space
Studied algorithms: Combination of Particle Swarm Optimization and Virtual Force
Weights Definition- Heuristic Method
Comparison between the values of issues of buyeri and the values of issues of the agreement.
Each issue then is characterized as an issue that needs a change or not
CI cI
ciI ciI
Weights Definition- Simplex Method
Maximize
InputOutput
Vi, Vagreement Wi
n)1,2,...,(j
0j w3.
n
1j agreementUjwjo .2
n
1j1jw 1.
j
wn
1j jo
Restrictions
Weights Definition- Analytic Hierarchy Process
A=
Feature’s Name Min Max Negotiabl
e
Proportionate Value
Price 10 100 True False 60
Trust 0 1 False True 0.6
Delivery 0 10 True False 5
Relevancy 0 1 True True 0.6
Weights Definition
Space Converging-PSO with VFA
o Uses a number of IAs
(particles) that constitute a
swarm moving around in the
search space looking for the
best solution
o Each particle in search space
adjusts its “flying” according
to its own flying experience as
well as the flying experience
of other particles
Space Converging-PSO with VFA
Each particle adjusts its travelling speed dynamically corresponding to the flying experiences of itself and its colleagues
Each particle modifies its position according to:
• its current position
• its current velocity
• the distance between its current position and pbest
• the distance between its current position and gbest
Space Converging-PSO with VFA
Space Converging-PSO with VFA
Space Converging-PSO with VFA
Space Converging-PSO with VFA
Space Converging-PSO with VFA
Space Converging-PSO with VFA
Space Converging-PSO with VFA
Space Converging-PSO with VFA
Space Converging-PSO with VFA
Particles = Buyers bargaining a set of productCannot be presented by a set o 2 coordinates (x,y)VFA algorithm
Every product is a vector [V1,V2,…Vn]Particle is moving in N-dimensional space
Space Converging-PSO with VFA
next position xi(t+1) depends from the velocity vi(t), which is equal to
where and c1, c2 are random generated values.
Price
QoS
Lb
Global best (Gb)Local best (Lb)
Current Position(CP)
CP
Gb
NP
Next Position (NP)
(ti
xgi
(P2
c(t))i
xli
(P1
c(t)i
v
Experiments - Performance Metrics
The agreement ratio (AG)
Average Buyer Utility (ABU)
Average Seller utility (ASU)
R
SNAG
||
)max( iF UU ||
||
1
SN
UABU
SN
kFk
||
||
1
SN
UASU
SN
kSk
Experiments - Performance Metrics
Average Rounds (AR)
Number of successful thread (Pt)
Fairness (F)
),min(
*
sb TT
tAR
H
HSH
||R
SHP
R
kk
t
1
cV
cVp
F
|2
|2
*
Experiments
Set of experiments1. 300 negotiations NT = 50, I = 4 and V in [10, 300]
(450.000)2. 300 negotiations , V = 100 NT = 50 and I=2k, where
k=2,…,5. 3. 500 negotiations: V = 100, I = 4 and NT 5 in [5,50].
*Seller’s cost is randomly selected in the interval [10, 50].
Experiments- AG
Experiments- ABU
Experiments- ASU
Experiments- AR
Experiments- Pt
Experiments- F
Conclusions
The basic idea :an algorithm which can deal with one-to-many, concurrent, dynamic with limited knowledge negotiations
Heuristic, Simplex and AHP methods, redefine the weights of product’s
Moving IAs in the N-dimensional space applying the Particle Swarm Optimization algorithm (PSO) combined with VFA.
The average utility gained by the buyer in all methods is above 50%.
PSO algorithm can handle excellent a large number of issues and a large number of IAs.
Future Work
Relevant function for dynamically change of weights for the seller’s part
The following step for PSO algorithm is to study whether the behavior of particles will change, if the weights of issues can be dynamically defined again during the negotiations.
The comparison of our results with real data would give us more realistic perspective between the developed methods providing us with the “closest-to-human-behavior” methodology
Thank you for your attention!
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Questions;
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