Post on 20-Jan-2017
Elicitation of Constraints and Qualitative Preferences in Multi-Attribute Auctions
MSc Thesis Defense
Shubhashis Kumar Shil26 November 2013
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Elicitation of Constraints and Qualitative Preferences in Multi-Attribute Auctions
1. Introduction
2. Proposed MARA Protocol
3. Experiments and Evaluation4. Conclusion and Future Work
1.1 Problem Statement1.2 Motivations1.3 Contributions
Outline
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2.1 Constraint Elicitation2.2 Preference Elicitation2.3 Weight Calculation Automation2.4 Utility Function Calculation Automation2.5 Bid Evaluation
Elicitation of Constraints and Qualitative Preferences in Multi-Attribute Auctions
Introduction Proposed MARA Protocol Experiments and Evaluation Conclusion and Future Work
Eliciting the buyer’s requirements, which consists of constraints andqualitative preferences, adequately
Determining the winner, which has been shown to be computationally complex, efficiently according to the buyer’s requirements
1.1 Problem Statement
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Elicitation of Constraints and Qualitative Preferences in Multi-Attribute Auctions
Often, the buyer is comfortable to express his preferences about the product qualitatively
There should be options for the buyer to specify constraints
The constraints and preferences can both be non-conditional or conditional
It is more efficient for the system to process quantitative data
Provide the buyer with more comfort as well as keep the system efficient
1.2 Motivations
Prefer expressing “Brand attribute is very much important” to “Importance of Brand attribute is 80%”
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Introduction Proposed MARA Protocol Experiments and Evaluation Conclusion and Future Work
Elicitation of Constraints and Qualitative Preferences in Multi-Attribute Auctions
Develop MARA protocol:
Enhance MAUT:
1.3 Contributions
Allowing the buyer to express his qualitative non-conditional and conditional preferences
Allowing the buyer to specify non-conditional and conditional constraints Allowing the constraints and preferences co-exist in the system Assisting both the buyer and sellers with friendly graphical user interfaces Designing a 3-layer software architecture based on multi-agent technique and
Belief-Desire-Intention (BDI) model
Converting qualitative requirements into quantitative ones Automating the MAUT calculation Determining the winner efficiently
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Introduction Proposed MARA Protocol Experiments and Evaluation Conclusion and Future Work
Elicitation of Constraints and Qualitative Preferences in Multi-Attribute Auctions 10
Introduction Proposed MARA Protocol Experiments and Evaluation Conclusion and Future Work
Constraint Elicitation
A process to extract hard constraints from a user that must be satisfied completely
where
(condition1) and/or, ..., and/or (conditionn) => constraint
conditioni : rel (attribute, value of attribute)
constraint : rel (attribute, value of attribute)
rel ε {=, ≠, <, >, ≤, ≥}
Elicitation of Constraints and Qualitative Preferences in Multi-Attribute Auctions 10
Introduction Proposed MARA Protocol Experiments and Evaluation Conclusion and Future Work
Preference Elicitation
A process to extract preferences as soft constraints from a user that are considered as wishes or desires
where
(condition1) and/or, ..., and/or (conditionn) => preference
conditioni : rel (attribute, value of attribute)
preference : attribute (value1 (likeliness), ..., valuem (likeliness) )
rel ε {=, ≠, <, >, ≤, ≥}
Elicitation of Constraints and Qualitative Preferences in Multi-Attribute Auctions 10
Introduction Proposed MARA Protocol Experiments and Evaluation Conclusion and Future Work
Automating Weight Calculation
Importance Level Quantitative Importance Level
Extremely Important 1
Very Much Important 0.75
Important 0.5
Not Much Important 0.25
weightRatepLevelquanrankweight aaa Im
M
aaweight
11/
1 aa positionMrankwhere
M = Number of attributes
positiona = position of attribute a in the attribute list ordered by the buyer
Elicitation of Constraints and Qualitative Preferences in Multi-Attribute Auctions 10
Introduction Proposed MARA Protocol Experiments and Evaluation Conclusion and Future Work
Automating Utility Function (String) Calculation
eutilityRatnessquanLikelirankvUaa vvaa )(
1aa vv positionNrank
where
N = Number of values of an attribute
positionva= position of attribute value va in the list of the
values of that attribute ordered by the buyer
Attribute Value Type
Likeliness Quantitative Likeliness
String HighestAbove AverageAverageBelow AverageLowest
10.80.60.40.2
Numeric HighestLowest
10.2
Elicitation of Constraints and Qualitative Preferences in Multi-Attribute Auctions 10
Introduction Proposed MARA Protocol Experiments and Evaluation Conclusion and Future Work
Automating Utility Function (Numeric) Calculation
0)(;1)( laahaa vUvU
)/()()( lahalaaaa vvvvvU
)( laa vU Second lowest utility value/number of attribute values
whereUa(vha) = Utility value of attribute value for the highest likeliness
Ua(vla) = Utility value of attribute value for the lowest likeliness
va = a value of attribute, a vha = a value of attribute, a of highest likeliness vla = a value of attribute, a of lowest likeliness
Elicitation of Constraints and Qualitative Preferences in Multi-Attribute Auctions
MAUT*
Buyer’s Request of Purchase
Buyer’s Preference Elicitation Buyer’s Constraint Elicitation
Qualitative Preference of Attributes (Importance Levels)
Qualitative Preference of Attribute Values (Likeliness)
Constraint Checking
Conversion of Attribute Preferences
Conversion of Attribute Value Preferences
Quantitative Preference of Attributes
Quantitative Preference of Attribute Values
Calculation of Attribute Weights
Calculation of Attribute Utility Function
Weight of Attributes Utility Function Value of Attributes
MAUT Calculation
Overall MAUT Utilities
Valid Bids
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Requirements Elicitation and Bid Evaluation in MARA System
Introduction Proposed MARA Protocol Experiments and Evaluation Conclusion and Future Work
A Summary
Elicitation of Constraints and Qualitative Preferences in Multi-Attribute Auctions
Presentation Layer
Graphical User Interface Agent
Buyer’s Preference Elicitation
Buyer’s Constraint Elicitation
Seller Bidding
Bid Score and Status Display
Business Logic Layer
Winner Determination AgentAdmin Agent Constraint
CheckingBid Evaluation with MAUT*
Data Access LayerAuction
DatabaseProduct
Database
Presentation Layer
Business Logic Layer
Data Access Layer
Stores two databases: Auction and Product
Graphical User Interface Agentinteracts with user
Winner Determination Agent determines the winner with thehelp of Admin Agent
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MARA 3-Layer Software Architecture
Introduction Proposed MARA Protocol Experiments and Evaluation Conclusion and Future Work
Elicitation of Constraints and Qualitative Preferences in Multi-Attribute Auctions
BDI Model
Admin Agent
Graphical User Interface (GUI) Agent
Winner Determination (WD) Agent
15 plans
15 plans 18 GUI windows
2 plans
Jadex
Agent simulation framework
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Jadex Control Center GUI
Introduction Proposed MARA Protocol Experiments and Evaluation Conclusion and Future Work
Elicitation of Constraints and Qualitative Preferences in Multi-Attribute Auctions
A reverse auction of a television consists of 10 attributes : Brand, Customer Rating, Display Technology, Model Year, Price, Refresh Rate, Resolution, Screen Size, Warranty and Weight
1 buyer and 20 sellers
4 non-conditional constraints, 3 conditional constraints, 7 non-conditional preferences and 3 conditional preferences
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Case Study
Introduction Proposed MARA Protocol Experiments and Evaluation Conclusion and Future Work
Elicitation of Constraints and Qualitative Preferences in Multi-Attribute Auctions
Non-Conditional Constraints (NCC)(ncc1) NULL → Model Year ≠ 2011(ncc2) NULL → Warranty ≥ 2(ncc3) NULL → Refresh Rate ≥ 120(ncc4) NULL → Screen Size ≥ [30 - 39]
Conditional Constraints (CC)(cc1) (Refresh Rate ≤ 240) → Price ≤ [900 - 999.99](cc2) (Brand = Panasonic) and (Resolu on = 720p HD) → Weight ≤ [5 - 5.9](cc3) (Brand = LG) or (Resolu on = 1080p HD) → Screen Size ≤ [40 - 49]
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Figure 6: Constraint Elicitation
Assisting the Buyer to Specify the Constraints via GUIs
Introduction Proposed MARA Protocol Experiments and Evaluation Conclusion and Future Work
Elicitation of Constraints and Qualitative Preferences in Multi-Attribute Auctions
Non-Conditional Preferences (NCP)(ncp1) NULL → Price([300 – 399.99](HS), [1000 – 1499.99](LS))(ncp2) NULL → Refresh Rate(600(HS), 120(LS))(ncp3) NULL → Brand(Bose(BA), Dynex(LS), Insignia(BA), LG(AA), Panasonic(A), Philips(A), Samsung(A), Sharp(BA),
Sony(AA), Toshiba(HS))(ncp4) NULL → Screen Size([50 - 60](HS), [30 - 39](LS))(ncp5) NULL → Model Year(2013(HS), 2012(LS))(ncp6) NULL → Warranty(3(HS), 2(LS))(ncp7) NULL → Customer Rating(5(HS), 3(LS))
Conditional Preferences (CP)(cp1) (Price > [300 – 399.99]) and (Screen Size ≥ [40 - 49]) → Display Technology(LCD(BA), LED(A), OLED(AA), Plasma(HS))(cp2) (Refresh Rate ≥ 120) → Resolution(1080p HD(HS), 4K Ultra HD(AA), 720p HD(A))(cp3) (Screen Size ≥ [30 - 39]) → Weight([4 – 4.9](HS), [6 -7](LS))
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Assisting the Buyer to Specify the Preferences via GUIs
Introduction Proposed MARA Protocol Experiments and Evaluation Conclusion and Future Work
Elicitation of Constraints and Qualitative Preferences in Multi-Attribute Auctions
Disqualified - Does not satisfy constraint(s) completely
Challenged - Satisfies constraints completely but the overall utility value is not the highest
Winner - Satisfies constraints completely and the overall utility value is the highest
Bid Submission, Evaluation & Status
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Introduction Proposed MARA Protocol Experiments and Evaluation Conclusion and Future Work
Elicitation of Constraints and Qualitative Preferences in Multi-Attribute Auctions
Performance Evaluation of MARA
5 Non-Conditional Constraints (NCC)(ncc1) NULL → Model Year ≠ 2011(ncc2) NULL → Warranty ≥ 2(ncc3) NULL → Refresh Rate ≥ 120(ncc4) NULL → Screen Size ≥ [30 - 39](ncc5) NULL → Brand ≠ Dynex
5 Conditional Constraints (CC)(cc1) (Refresh Rate ≤ 240) → Price ≤ [900 - 999.99](cc2) (Brand = Panasonic) and (Resolu on = 720p HD) → Weight ≤ [5 - 5.9](cc3) (Brand = LG) or (Resolu on = 1080p HD) → Screen Size ≤ [40 - 49](cc4) (Model Year = 2013) and (Warranty ≥ 2) → Brand ≠ Bose(cc5) (Customer Rating < 2) and (Model Year ≤ 2012) → Price ≤ [500 - 599.99]
5 Non-Conditional Preferences (NCP)(ncp1) NULL → Price([300 – 399.99](HS), [1000 – 1499.99](LS))(ncp2) NULL → Refresh Rate(600(HS), 120(LS))(ncp3) NULL → Brand(Bose(BA), Dynex(LS), Insignia(BA), LG(AA), Panasonic(A), Philips(A), Samsung(A), Sharp(BA), Sony(AA),
Toshiba(HS))(ncp4) NULL → Screen Size([50 - 60](HS), [30 - 39](LS))(ncp5) NULL → Model Year(2013(HS), 2012(LS))
5 Conditional Preferences (CP)(cp1) (Price > [300 – 399.99]) and (Screen Size ≥ [40 - 49]) → Display Technology(LCD(BA), LED(A), OLED(AA), Plasma(HS))(cp2) (Refresh Rate ≥ 120) → Resolution(1080p HD(HS), 4K Ultra HD(AA), 720p HD(A))(cp3) (Screen Size ≥ [30 - 39]) → Weight([4 – 4.9](HS), [6 - 7](LS))(cp4) (Price ≥ [800 - 899.99]) → Warranty(3(HS), 2(LS))(cp5) (Refresh Rate ≥ 240) or (Screen Size ≥ [30 - 39]) → Customer Rating(5(HS), 3(LS))
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Introduction Proposed MARA Protocol Experiments and Evaluation Conclusion and Future Work
Elicitation of Constraints and Qualitative Preferences in Multi-Attribute Auctions
Attributes Sellers NCC CC NCP CP Execution Time
6 (A1-A6) 20 3 (ncc1, ncc3, ncc5)
2 (cc1, cc5)
4 (ncp1, ncp2, ncp3, ncp5)
0 0.203
7 (A1-A7) 20 3 (ncc1, ncc3, ncc5)
2 (cc1, cc5)
4(ncp1, ncp2, ncp3, ncp5)
1(cp2)
0.328
8 (A1-A8) 20 4 (ncc1, ncc3, ncc4, ncc5)
3 (cc1, cc3, cc5)
5 3(cp1, cp2, cp5)
0.437
9 (A1-A9) 20 5 4 (cc1, cc3, cc4, cc5)
5 4(cp1, cp2, cp4, cp5)
0.438
10 20 5 5 5 5 0.453
Execution time increases with the increment of the number of attributes
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Execution Time of MAUT* by varying Number of Attributes
Introduction Proposed MARA Protocol Experiments and Evaluation Conclusion and Future Work
Elicitation of Constraints and Qualitative Preferences in Multi-Attribute Auctions
Execution time increases with the increment of the number of sellers
Attributes Sellers NCC CC NCP CP Execution Time
10 4 (S1-S4) 5 5 5 5 0.219
10 8 (S1-S8) 5 5 5 5 0.250
10 12 (S1-S12) 5 5 5 5 0.313
10 16 (S1-S16) 5 5 5 5 0.359
10 20 5 5 5 5 0.453
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Execution Time of MAUT* by varying Number of Sellers
Introduction Proposed MARA Protocol Experiments and Evaluation Conclusion and Future Work
Elicitation of Constraints and Qualitative Preferences in Multi-Attribute Auctions
Execution time decreases with the increment of the number of non-conditional constraints
With the increment of the number of non-conditional constraints, it creates more chance for the bids to be disqualified
Attributes Sellers NCC CC NCP CP Execution Time
10 20 1(ncc1)
5 5 5 0.532
10 20 2(ncc1, ncc2)
5 5 5 0.516
10 20 3(ncc1, ncc2, ncc3)
5 5 5 0.500
10 20 4(ncc1, ncc2, ncc3, ncc4)
5 5 5 0.469
10 20 5 5 5 5 0.453
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Execution Time of MAUT* by varying Number of Non-Conditional Constraints
Introduction Proposed MARA Protocol Experiments and Evaluation Conclusion and Future Work
Elicitation of Constraints and Qualitative Preferences in Multi-Attribute Auctions
Execution time decreases or remains the same with the increment of the number of conditional constraints
With the increment of the number of conditional constraints, it creates more chance for the bids to be disqualified
Attributes Sellers NCC CC NCP CP Execution Time
10 20 5 1 (cc1)
5 5 0.578
10 20 5 2(cc1, cc2)
5 5 0.546
10 20 5 3(cc1, cc2, cc3)
5 5 0.546
10 20 5 4(cc1, cc2, cc3, cc4)
5 5 0.484
10 20 5 5(cc1, cc2, cc3, cc4, cc5)
5 5 0.453
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Execution Time of MAUT* by varying Number of Conditional Constraints
Introduction Proposed MARA Protocol Experiments and Evaluation Conclusion and Future Work
Elicitation of Constraints and Qualitative Preferences in Multi-Attribute Auctions
Execution time increases with the increment of the number of non-conditional preferences
Attributes Sellers NCC CC NCP CP Execution Time
10 20 5 5 1(ncp1)
5 0.401
10 20 5 5 2(ncp1, ncp2)
5 0.408
10 20 5 5 3(ncp1, ncp2, ncp3)
5 0.417
10 20 5 5 4(ncp1, ncp2, ncp3, ncp4)
5 0.437
10 20 5 5 5 5 0.453
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Execution Time of MAUT* by varying Number of Non-Conditional Preferences
Introduction Proposed MARA Protocol Experiments and Evaluation Conclusion and Future Work
Elicitation of Constraints and Qualitative Preferences in Multi-Attribute Auctions
Execution time increases with the increment of the number of conditional preferences
Attributes Sellers NCC CC NCP CP Execution Time
10 20 5 5 5 1(cp1)
0.313
10 20 5 5 5 2(cp1, cp2)
0.407
10 20 5 5 5 3(cp1, cp2, cp3)
0.421
10 20 5 5 5 4(cp1, cp2, cp3, cp4)
0.438
10 20 5 5 5 5 0.453
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Execution Time of MAUT* by varying Number of Conditional Preferences
Introduction Proposed MARA Protocol Experiments and Evaluation Conclusion and Future Work
Elicitation of Constraints and Qualitative Preferences in Multi-Attribute Auctions
Our MARA protocol is able to elicit non-conditional and conditional constraints
The system is able to elicit qualitative non-conditional and conditional preferences
Our improved MAUT can take qualitative requirements and convert them into quantitative ones
The system provides automation of the MAUT algorithm
The system can determine the winner efficiently
Conclusion
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Introduction Proposed MARA Protocol Experiments and Evaluation Conclusion and Future Work
Elicitation of Constraints and Qualitative Preferences in Multi-Attribute Auctions
Besides MAUT there are other techniques [9] such as Analytic Hierarchy Process (AHP), Weight determination based on Ordinal Ranking of Alternatives (WORA) and Simple Multi-Attribute Rating Technique (SMART) that can be used
The system can be tested with real world datasets of auction systems
Our MARA system can be improved by allowing the buyer to specify his requirements qualitatively on some attributes and quantitatively on other attributes of the product he is interested in
Future Work
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Introduction Proposed MARA Protocol Experiments and Evaluation Conclusion and Future Work
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Q & A
Thanks
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Elicitation of Constraints and Qualitative Preferences in Multi-Attribute Auctions