Constraint and Qualitative Preference Specification in ...

Constraint and Qualitative Preference Specification in Multi-Attribute Reverse Auctions Samira Sadaoui and Shubhashis Kumar Shil* Department of Computer Science, University of Regina, Regina, SK, Canada {sadaouis,shil200s}@uregina.ca

Abstract. In the context of Multi-Attribute and Reverse Auctions (MARAs), two significant problems need to be addressed: 1) specifying precisely the buyer's requirements about the attributes of the auctioned product, and 2) determining the winner accordingly. Buyers are more comfortable in expressing their preferences qualitatively, and there should be an option to allow them describes their constraints. Both constraints and preferences may be non-conditional and conditional. However for the sake of efficiency, it is more suitable for MARAs to process quantitative requirements. Hence, there is a remaining challenge to provide the buyers with more facilities and comfort, and at the same time to keep the auctions efficient. To meet this challenge, we develop a MARA system based on MAUT. The proposed system takes advantage of the efficiency of MAUT by transforming the qualitative requirements into quantitative ones. Another benefit of our system is the complete automation of the bid evaluation since it is a really difficult task for buyers to determine quantitatively all the weights and utility functions of attributes, especially when there is a large number of attributes. The weights and utility functions are produced based on the qualitative preferences. Our MARA looks for the outcome that satisfies all the constraints and best satisfies the preferences. We demonstrate the feasibility of our system through a 10-attribute reverse auction involving many constraints and qualitative preferences. Keywords: Constraint Specification, Qualitative Preference Specification, Winner Determination, Multi-Attribute Reverse Auctions, MAUT.

1

Introduction

Over the last decades, several companies have started using reverse auctions [21]. According to an annual benchmark e-sourcing survey of the Purchasing magazine, in 2006, 31% of participating companies employed reverse auctions [13]. Procurement software vendors, such as PurchasePro, IBM, DigitalUnion and Perfect, have also adopted reverse auctions [4]. In addition, there are numerous popular service providers of reverse auctions such as eBay.com, Priceline.com, Bidz.com, and AuctionAnything.com. A survey reported that buyers of reverse auctions achieved savings of *

Both authors contributed equally for the work presented in this paper.

A. Moonis et al. (Eds.): IEA/AIE 2014, Part II, LNAI 8482, pp. 497–506, 2014. © Springer International Publishing Switzerland 2014

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15% in cost and up to 90% in time [19]. On the other hand, multi-attribute auctions are considered one of the most valuable procurement systems in which bidders negotiate over the price along with other dimensions such as warranty, quality, sellers' reputation, delivery and payment terms, [5], [20]. This type of auctions guarantees higher efficiency than traditional auctions by exchanging information between the buyers’ preferences and sellers' offers [4]. Multi-attribute auctions have been successfully utilized for the acquisition of a huge amount of specific products [4]. In this paper, we are interested in Multi-Attribute and Reverse Auctions (that we call MARAs). Several research works have been done in the context of MARAs, still two significant problems have to be solved: 1) eliciting precisely the buyers’ requirements containing the constraints and preferences about the product attributes, and 2) determining the winner (the best bid) accordingly. Qualitative preferences and constraints are important features for any multi-attribute auction systems [8]. Indeed, the buyers would like to express their requirements qualitatively and in a friendly and interactive way, which is the most convenient. MARAs should provide these facilities to the buyers so that they can feel that their desires are satisfied at the maximum. Nevertheless, for the purpose of efficiency, it is more suitable for the auction systems to process quantitative requirements. Consequently, there is a remaining challenge to provide the buyers with more features and comfort, and to keep the auctions efficient at the same time. Maximizing the satisfaction of the users may be achieved by considering precisely their preferences and interests [11]. [11] proposes a personalized matchmaking system that determines the best offer for a buyer by evaluating and sorting the sellers’ offerings according to the buyer’s precise interests. Preference elicitation is essential in interactive applications such as online shopping systems [2], [14]. [2] introduces an online shopping system that provides the users with the ability to specify their requirements (constraints and preferences) in an interactive way. [14] enhances the online shopping system developed in [2] with a learning component to learn the users’ preferences, and therefore suggest products based on them. More precisely, the new component learns from other users’ preferences and makes a set of recommendations using data mining techniques. Nowadays, there are several preference-aware commercial systems, such as Amazon, Netflix, SmartClient, Teaching Salesman and PExA [14]. Nevertheless, these systems do not allow the users to express qualitative preferences nor constraints. Formalizing users' preferences accurately also is very important in most decision support systems [6]. These systems rely on preferences to produce effective user models. For instance, [18] discusses and evaluates different methods to elicit preferences in negotiation support systems. [7] proposes a general interactive tool to obtain users’ preferences about concrete outcomes and to learn utility functions automatically based on users’ feedback. [12] develops a variant of the fully probabilistic design of decision making strategies. In [12], the elicitation of preferences is based on quantitative data. Preference elicitation plays an essential role in negotiations such as in automated market places and auctions [9]. Moreover, the acquisitions of high quality users’ preferences are also significant for interactive Web services [10]. The quality of the returned results depends on the capability of the services to acquire the preferences. [10] examines the adaptation and personalization

Constraint and Qualitative Preference Specification

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strategies during the elicitation process for Web services. Preferences and constraints can co-exist together in many domains [1], [20], and it is of great benefit to handle them together in many real-world applications. For instance, [8] employs MAUT to process quantitative preferences and Conditional Preference networks (CP-nets) to formalize qualitative preferences. [1] represents preferences as an instance of CP-nets and the constraints as a Constraint Satisfaction Problem (CSP). [1] also introduces a new algorithm to determine the best outcome based on the arc consistency propagation technique. It performs several experiments to show that the proposed approach is able to save substantial amount of time to generate the optimal solution. [16] introduces an algorithm that processes constraints and preferences and finds the optimal outcomes of a constrained CP-nets. [2] employs C-semiring to describe quantitative preferences, CP-nets for qualitative ones, CSP for constraints. This paper utilizes branch and bound method to provide the users with a list of outcomes. This research proposes a MARA system where the buyer specifies his requirements containing constraints and qualitative preferences about the product attributes. Our system looks for the outcome that completely satisfies all the constraints and best satisfies the preferences. More precisely, we develop a MARA protocol to assist the buyer step by step in specifying conditional and non-conditional qualitative preferences as well as conditional and non-conditional constraints. The specification process is carried out through several friendly and interactive GUIs. The proposed auction system is based on MAUT [3], a widely used technique in multi-attribute decision-making. It takes advantage of the efficiency of MAUT by transforming the qualitative requirements into quantitative ones. Another major contribution of our auction system is the complete automation of the MAUT calculation. Indeed, it is a really challenging task for the buyers to determine quantitatively all the weights and all the utility functions of attributes, especially when there are lots of attributes for the auctioned product. The weights and utility functions are generated based on the qualitative preferences. We may also note that our system can handle multiple rounds to give the sellers a chance to improve their bids and compete better in the next rounds, allow any number of attributes to aid the buyer elicit his interests in a more precise way. Our protocol is semi-sealed because the bidders' offers are kept private during the auction, but at the end of each round, MARA reveals the overall MAUT utilities of the valid bids and statuses of all the submitted bids.

2

The Proposed MARA Protocol

When the buyer requests to purchase a product (e.g. a Television), our system first provides him with possible attributes for the product (generated from the product database). The buyer then selects the attributes he is interested in (e.g. Brand, Weight, Display Technology, Refresh Rate and Price). Subsequently, a reverse auction based on the buyer's attributes is launched. Next, the buyer will specify his requirements about the auctioned product. A. Specifying Constraints on Attributes: first the buyer expresses hard constraints on the attributes of his choice. Constraints are described with the following syntax:

500


(1)

where and both denote a relation between an attribute and its possible values: such that .When the condition clause in (1) is NULL, the constraint is non-conditional i.e. it has no dependencies on other attributes. Assume the buyer submits the following two constraints regarding the TV auction: (c1) NULL => Brand Panasonic (c2) (Weight [5 - 5.9]) or (Display Technology = LED) and (Refresh Rate Price [1000 - 1499.99]

120)

Here (c1) indicates that the TV brand must not be a Panasonic. (c2) means if the TV weight is greater than or equal to [5kg - 5.9kg] (i.e. not less than 5kg), or the display technology is LED, and the refresh rate is less than or equal to 120Hz, then the price must be less than or equal to [$1000 - $1499.99] (i.e. not greater than $1499.99). Since the precedence of 'and' is greater than of 'or', the condition clauses of (c2) is evaluated as: conditionWeight or (conditionDisplayTechnology and conditionRefreshRate). B. Specifying Qualitative Preferences on Attributes: next the buyer should submit the qualitative importance levels for all the attributes. The system assigns equivalent quantitative values of these importance levels as shown in Table 1. Rank is a relative value of an attribute and M the number of attributes. Table 1. Attribute Importance Levels in MARA Qualitative Importance Level ExtremelyImportant (EI) VeryImportant (VI) Important (I) Not VeryImportant (NVI)

Quantitative Importance Level 1 0.75 0.5 0.25

Rank M M-1 M-2 M-3

C. Specifying Qualitative Preferences on Attribute Values: the buyer can also specify qualitative preferences (liking) on the values of some attributes by using the following format: (2)

where

, and

Values can be of two types: string and numeric. Table 2 gives the corresponding quantitative liking where rank is a relative value of an attribute value and N the number of values of an attribute. If the condition clause in (2) is NULL, then the preference is non-conditional.


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Table 2. Attribute Value Liking in MARA Attribute Type String

Numeric

Value

Qualitative Liking

Quantitative Liking

Rank

Highest (H) Above Average (AA) Average (A) Below Average (BA) Lowest (L) Highest (H) Lowest (L)

1 0.8 0.6 0.4 0.2 1 0.2

N N-1 N-2 N-3 N-4

Suppose the buyer submits the following preferences for the TV auction: (p1) NULL => Price ([800 - 899.99] (H), [1000 - 1499.99] (L)) (p2) (Refresh Rate > 120) => Brand (LG (BA), Panasonic (A), Sharp (A), Sony (AA), Toshiba (H)) (p1) expresses [800 - 899.99] as the highest preference and [1000 - 1499.99] as the lowest one for the attribute Price; (p2) means if the value of Refresh Rate is greater than 120, then the liking of each value of Brand is given. (p1) is an example of preference on numeric values whereas (p2) is on string values.

Fig. 1. Requirements Specification and Bid Evaluation in MARA

D. Evaluating Automatically the Bids: the buyer's requirements are displayed to the registered sellers who will compete by following the first sealed bid protocol. In

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MAUT, each attribute in a bid has a value denoted by , and is evaluated through a utility function . An attribute has a weight which shows its importance to the buyer. The MAUT utility of a bid is calculated as the sum of all weighted utilities of the attributes [4]: M

(3)

As illustrated in Fig. 1, after each round, the system deletes from the auction those bids that violate any constraint. For the qualified bids, MARA calculates automatically the weights and utility functions of all the attributes. As shown in Table 3, the weights are produced based on the buyer’s importance levels. The utility function values are generated using the buyer’s liking. If the buyer does not include a liking for an attribute value, then the system assigns 0 to the corresponding quantitative liking. In the case of conditional preferences, if a bid does not satisfy the condition clause, then the system assigns 0 to the quantitative liking of the attribute value for that bid. Finally, MARA informs the bidders about the overall utilities of the valid bids and statuses of all the submitted bids. In the next round, each seller is expected to place a bid with a utility higher than the one of the previous round. Bidding continues until the highest utility values of two consecutive rounds remain the same. Suppose now the following two bids have been submitted: Bid1: Brand = Panasonic; Display Technology = LCD; Price = 1000; Refresh Rate = 240; Weight = 4 Bid2: Brand = Toshiba; Display Technology = Plasma; Price = 1200; Refresh Rate = 600; Weight = 4 The first bid does not satisfy the two constraints (c1) and (c3), however respects (c2) and (c4). In fact, this bid is deleted as soon as it violets (c1) and therefore there is no need to check the other constraints and calculate its MAUT utility value in order to save some processing time. On the other hand, the second bid satisfies all the constraints but does not guaranty to be the best bid. Table 3. Attribute weight and utility function determination Attribute Weight Calculation / Attribute Utility Function Calculation (String) Attribute Utility Function Calculation (Numeric) is the attribute value which has the highest preference is the attribute value which has the lowest preference if

then

else

such that ( is a value or an average value in case of range of values of


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Table 4. Weight and utility function value calculation for Brand Weight calculation

Utility function value calculation

Rank

Attribute Value LG Panasonic Sharp Sony Toshiba

4 Rank 4 3 2 4 5

Quantitative Importance Level 0.75 Quantitative Liking 0.8 0.6 0.4 0.8 1

Weight 0.27 Utility Value 0.32 0.36 0.24 0.64 1

Here, we illustrate the MAUT calculation for Bid2. MAUT (Bid2) = WeightBrand.UBrand (Toshiba) + WeightDisplayTechnology.UDisplayTechnology (Plasma) +WeightPrice.UPrice (1200) + WeightRefreshRate.URefreshRate (600) + WeightWeight.UWeight (4). In Table 4, we show for example how the weight and utility function values are computed by MARA for the attribute Brand.

3

A 10-Attribute Reverse Auction

We fully developed our MARA system using the Belief-Desire-Intention model [17] and the agent-based simulation environment Jadex [15]. We designed MARA as 3layer architecture. The presentation layer contains several GUIs to assist the buyer step by step in specifying the (non)conditional constraints as well as (non)conditional qualitative preferences. It also aids the sellers to submit their bids. The business layer contains the algorithms for constraint checking, weight, utility function value and MAUT calculation. The database layer stores all the information regarding the products and auctions. In this case study, the buyer wants to purchase a TV with ten attributes (given in Fig. 3). The buyer can now submit the constraints for this auction: (c1) NULL => Model Year 2011 (c2) NULL => Warranty 2 (c3) NULL => Refresh Rate 120 (c4) NULL => Screen Size [30 - 39] (c5) (Refresh Rate 240) => Price [900 - 999.99] (c6) (Brand = Panasonic) and (Resolution = 720p HD) => Weight [5 - 5.9] (c7) (Brand = LG) or (Resolution = 1080p HD) => Screen Size [40 - 49] Next, the buyer ranks the ten attributes according to their importance: Brand (VI), Customer Rating (NVI), Display Technology (I), Model Year (NVI), Price (EI), Refresh Rate (NVI), Resolution (I), Screen Size (VI), Warranty (NVI), and Weight (VI). The following are examples of buyer's qualitative preferences on attribute values: (p1) NULL => Price ([300 - 399.99] (H), [1000 - 1499.99] (L)) (p2) NULL => Refresh Rate (600(H), 120(L))

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(p3) NULL => Brand (Bose (BA), Dynex (L), Insignia (BA), LG (AA), Panasonic (A), Philips (A), Samsung (A), Sharp (BA), Sony (AA), Toshiba (H)) (p4) NULL => Screen Size ([50 - 60] (H), [30 - 39] (L)) (p5) NULL => Model Year (2013(H), 2012(L)) (p6) NULL => Warranty (3(H), 2(L)) (p7) NULL => Customer Rating (5(H), 3(L)) (p8) (Price > [300 - 399.99]) and (Screen Size [40 - 49]) => Display Technology (LCD (BA), LED (A), OLED (AA), Plasma (H)) (p9) (Refresh Rate 120) => Resolution (1080p HD (H), 4K Ultra HD (AA), 720p HD (A)) (p10) (Screen Size [30 - 39]) => Weight ([4 - 4.9] (H), [6 - 7] (L))

Fig. 2. Bid Submission in MARA

After the buyer's requirements have been submitted, the sellers can now start bidding. Assume 20 sellers participate and their first bids are depicted in Fig. 2. Five sellers, S2, S3, S5, S13 and S14, are disqualified as they do not satisfy some buyer’s constraints. The remaining bids respect all the seven constraints. Fig. 3 shows the overall MAUT utilities of the valid bids and statuses of all the submitted bids for the first round. It also depicts S18 as the winner and the remaining 14 bids are challenged.

4

Conclusion and Future Work

The proposed auction system is able to assist the buyer in specifying non-conditional and conditional constraints as well as qualitative non-conditional and conditional preferences about the attributes of a product. To determine the winner efficiently, it converts qualitative requirements into quantitative ones. To generate the MAUT utility for each valid bid, MARA calculates automatically the attribute weights and utility functions. The latter are directly calculated from the buyer’s preferences. In this paper, we explored experimentally the feasibility of MARA with a 10-attribute reverse auction involving 7 constraints, 10 preferences and 20 sellers.


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Fig. 3. Overall Utilities and Statuses of Bids

There are several promising future directions for this research work. In the literature, there are approaches to assign weights for the attributes, such as Simple MultiAttribute Rating Technique (SMART) and Weight determination based on Ordinal Ranking of Alternatives (WORA) [5]. We can compare our weight calculation algorithm with these approaches in terms of processing time and best outcome. Another direction is to compare MAUT with other decision analysis techniques [3], such as Analytic Hierarchy Process and Conjoint Analysis, specifically in the domain of reverse auctions. Also, we would like to test the performance of our MARA system on a large dataset involving a large number of attributes, constraints, preferences and sellers. Our system allows the buyer to specify his preferences only qualitatively. To increase the acceptability of MARA, the buyer can be allowed to elicit qualitative preferences on some attributes as well as quantitative preferences on others.

References 1. Alanazi, E., Mouhoub, M.: Managing qualitative preferences with constraints. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds.) ICONIP 2012, Part III. LNCS, vol. 7665, pp. 653–662. Springer, Heidelberg (2012) 2. Alanazi, E., Mouhoub, M., Mohammed, B.: A preference-aware interactive system for online shopping. Computer and Information Science 5(6), 33–42 (2012) 3. Bichler, M.: An experimental analysis of multi-attribute auctions. Decision Support Systems 29(3), 249–268 (2000) 4. Bichler, M., Kalagnanam, J.: Configurable offers and winner determination in multiattribute auctions. European Journal of Operational Research 160(2), 380–394 (2005) 5. Chandrashekar, T.S., Narahari, Y., Rosa, C.H., Kulkarni, D.M., Tew, J.D., Dayama, P.: Auction-based mechanisms for electronic procurement. IEEE Transactions on Automation Science and Engineering 4(3), 297–321 (2007)

506


6. Faltings, B., Pu, P., Zhang, J.: Agile Preference Models Based on Soft Constraints. In: AAAI Spring Symposium: Challenges to Decision Support in a Changing World, pp. 23–28 (2005) 7. Gajos, K., Weld, D.S.: Preference elicitation for interface optimization. In: Proceedings of the 18th Annual ACM Symposium on User Interface Software and Technology, UIST 2005, pp. 173–182. ACM, New York (2005) 8. Ghavamifar, F., Sadaoui, S., Mouhoub, M.: Preference elicitation and winner determination in multi-attribute auctions. In: Murray, R.C., McCarthy, P.M. (eds.) FLAIRS Conference, pp. 93–94. AAAI Press (2011) 9. Guo, Y., Müller, J.P., Weinhardt, C.: Learning user preferences for multi-attribute negotiation: An evolutionary approach. In: Ma ík, V., Müller, J.P., P chou ek, M. (eds.) CEEMAS 2003. LNCS (LNAI), vol. 2691, pp. 303–313. Springer, Heidelberg (2003) 10. Jannach, D., Kreutler, G.: Personalized user preference elicitation for e-services. In: Proceedings of the 2005 IEEE International Conference on e-Technology, e-Commerce and e-Service, EEE 2005, pp. 604–611 (2005) 11. Jiang, W., Sadaoui, S.: Evaluating and Ranking Semantic Offers According to Users’ Interests. Journal of Electronic Commerce Research, JECR 13(1), 1–22 (2012) 12. Karny, M., Guy, T.V.: Preference elicitation in fully probabilistic design of decision strategies. In: 2010 49th IEEE Conference on Decision and Control (CDC), pp. 5327–5332 (2010) 13. Manoochehri, G., Lindsy, C.: Reverse auctions: Benefits, challenges, and best practices. California Journal of Operations Management 6(1), 123–130 (2008) 14. Mohammed, B., Mouhoub, M., Alanazi, E., Sadaoui, S.: Data Mining Techniques and Preference Learning in Recommender Systems. Computer and Information Science 6(4), 88-1-2 (2013) 15. Pokahr, A., Braubach, L., Lamersdorf, W.: Jadex: A BDI Reasoning Engine. In: Bordini, R.H., Dastani, M., Dix, J., El FallahSeghrouchni, A. (eds.) Multi-Agent Programming. Multiagent Systems, Artificial Societies, and Simulated Organizations, vol. 15, pp. 149–174. Springer, US (2005) 16. Prestwich, S., Rossi, F., Venable, K.B., Walsh, T.: Constraint-based preferential optimization. In: AAAI, vol. 5, pp. 461–466 (2005) 17. Seow, K.T., Sim, K.M.: Collaborative Assignment using Belief-Desire-Intention Agent Modeling and Negotiation with Speedup Strategies. Information Sciences 178(4), 1110–1132 (2008) 18. Shao-bin, D., Yu Qiang, F., Kexing, L., Xuncheng, S.: Preference elicitation of the NSS in the ecommerce. In: International Conference on Management Science and Engineering, ICMSE 2007, pp. 135–140 (2007) 19. Talluri, S., Narasimhan, R., Viswanathan, S.: Information technologies for procurement decisions: A decision support system for multi-attribute e-reverse auctions. International Journal of Production Research 45(11), 2615–2628 (2007) 20. Teich, J.E., Wallenius, H., Wallenius, J., Zaitsev, A.: A multi-attribute e-auction mechanism for procurement: Theoretical foundations. European Journal of Operational Research 175(1), 90–100 (2006) 21. Williams, C.L.: An overview of reverse auctions. In: Southwest Decision Sciences Institute Conference (2010)