Buyer Agent Decision Process Based on
Automatic Fuzzy Rules Generation
Methods
Roi Arapoglou, Kostas Kolomvatsos, Stathes Hadjiefthymiades
Pervasive Computing Research
Group, Department of Informatics
and Telecommunications
University of Athens, Greece
WCCI – FUZZ 2010
Barcelona - Spain
Outline
Introduction
Market Members – Scenario
Buyer Behavior – Decision Process
Buyer Fuzzy Logic System
Fuzzy Rules Generation
Results
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Introduction
Intelligent Agents
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Autonomous software components
Represent users
Learn from their owners
Electronic Markets
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Places where entities not known in advance can negotiate for
the exchange of products
Fuzzy Logic
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Algebra based on fuzzy sets
Deals with incomplete or uncertain information
Enhance the knowledge base of agents
Market Members - Scenario
Buyers
 Sellers
 Middle entities (matchmakers, brokers, market entities)
 Intelligent Agents may represent each of these entities
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Scenario
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Modeled as a finite-horizon Bargaining Game
No knowledge about the characteristics of the opponent (i.e., the other
side) is available
Buyer Behavior – Decision process (1/2)
The buyer stays in the game for a specific number of
rounds
Profit
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A Utility Function is used
U b  V  p , where V is the buyer valuation and p is the product
price
The smaller the price is the greater the profit becomes
Pricing Function

ptb  p0  V  (x  Tb1 )k , where p0
is an initial price,V is the valuation,
x is the number of the proposal, Tb is the deadline and k is a
policy factor (k>1:patient, k<1:aggressive, k=1:neutral)
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Buyer Behavior – Decision process (2/2)
Receives proposals and accepts or rejects them making
its own proposals
Utilizes a reasoning mechanism based on FL
The mechanism results the value of the Acceptance
Degree (AD)
The reasoning mechanism is based on the following
parameters:
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Relevance factor (r)
Price difference (d)
Belief about the expiration of the game (b)
Time difference (t)
Valuation (V)
Buyer Fuzzy Logic System (1/2)
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Architecture
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Contains a set of Fuzzy rules
Rules are automatically generated based on experts
dataset
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Buyer Fuzzy Logic System (2/2)
Advantages of the automatic Fuzzy rules generation
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Mainly, it does not require a lot of time in the developer side
It does not require experience in FL rules definition
It uses simple numbers representing values of basic parameters
Fuzzy rules are automatically tuned
Fuzzy Rules Generation (1/2)
Clustering techniques are used
Algorithms:
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K-means
Fuzzy C-means (FCM)
Subtractive clustering
Nearest Neighborhood Clustering (NNC)
Every cluster corresponds to a Fuzzy rule
Example
If x * (x1* , x *2 ,...,x *n ) is a cluster center the rule is:
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IF x1 is x1* AND x 2 is x *2 ... AND x n-1 is x *n-1 THEN x n is x *n
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Fuzzy Rules Generation (2/2)
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Additional techniques
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Learning from Examples (LFE)
Modified Learning from Examples (MLFE)
Templates for membership functions are defined
Dataset
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They describe the policy that the buyer should have, concernig
the acceptance of a proposal
108 rows of data
Each row contains data for r, d, b, t, and V
Results (1/3)
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Fuzzy rule base creation time
Algorithm
Subtractive
FCM
K-Means
LFE
MLFE
NNC
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Rule Base creation time (ms)
35
2560
25
20
25
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Usage of the generated Fuzzy rule base in a BG
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We use the following parameters
Buyer Parameters
Initial Price
100 MUs[1]
Valuation
255 MUs
[1]
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Seller Parameters
Cost
250 MUs
Initial Profit
250 MUs
MU = Monetary Unit
We examine the Joint Utility in seven agreement zones
(theoretic maximum equal to 0.25)
(P*  C)  (V  P* ) (1)
JU 
,
(V  C)2
where P* is the agreement price, C is the
seller cost and V is the buyer valuation
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(1)D.
Zeng & K. Sycara, ‘Bayesian Learning in Negotiation’, International Journal of Human-Computer Studies, vol(48), no 1, 1998, pp. 125-141.
Results (2/3)
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Agreement zones
Buyer Valuation
255 MUs
260 MUs
270 MUs
300 MUs
500 MUs
700 MUs
1000 MUs
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Agreement Zone
5 MUs
10 MUs
20 MUs
50 MUs
250 MUs
450 MUs
750 MUs
Numerical results
Scenario
No
1
2
3
4
5
6
7
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Agreement
Zone
5 MUs
10 MUs
20 MUs
50 MUs
250 MUs
450 MUs
750 MUs
Average JU
Maximum JU
Algorithm
0.08
0.14
0.16
0.24
0.238
0.208
0.17
0.24
0.24
0.21
0.247
0.24
0.21
0.172
FCM, K-Means
FCM, K-Means
LFE
FCM, K-Means
MLFE
MLFE
MLFE
Results (3/3)
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Performance of algorithms in the BG
Algorithm
Subtractive
FCM
K-Means
LFE
MLFE
NNC
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Agreements Percentage
92%
69%
69%
57%
85%
86%
Average JU
0.217
0.219
0.202
0.223
0.244
0.244
Algorithm
Subtractive
FCM
K-Means
LFE
MLFE
NNC
Average AD Value
80.96
68.91
62.84
72.65
74.52
76.58
Thank you!
http://p-comp.di.uoa.gr
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