MSc_Defense_Presentation

Elicitation of Constraints and Qualitative Preferences in Multi-Attribute Auctions

MSc Thesis Defense

Shubhashis Kumar Shil26 November 2013

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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


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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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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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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Elicitation of Constraints and Qualitative Preferences in Multi-Attribute Auctions 10


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 {=, ≠, <, >, ≤, ≥}



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 {=, ≠, <, >, ≤, ≥}



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



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



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


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


A Summary


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



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



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



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



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



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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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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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



Execution time increases with the increment of the number of sellers


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



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


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



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


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



Execution time increases with the increment of the number of non-conditional preferences


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



Execution time increases with the increment of the number of conditional preferences


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



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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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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Q & A

Thanks

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