Online business environment represents complex systems where the business transactions between buyers and sellers are carried out based on multiple constraints from buyers and sellers. The complexity of the online business environment between buyers and sellers has encouraged the use of modelling broker to solve allocations. Thus, this paper proposes a novel approach for a broker agent to allocate buyers' requirements to sellers' offers using the method of artificial intelligence. The major contributions of this paper are that (i) a proposed framework for a broker agent is divided in four stages: receiving, calculation, filtering and allocating; (ii) a novel calculation method is to calculate buyers' satisfaction degree as per sellers' offers to determine a constraint satisfaction layer; and (iii) CSP model for trade allocation is built to help a broker agent to find an optimal allocation solution to satisfy buyers and sellers' various preferential requirements. Finally, the results of the simulation experiment demonstrate the proposed approach is flexible and effective under the consideration of different constraints.
E-commerce has recently made remarkable achievements in the era of globalization
together with rapid technological changes. Especially, the emergence of artificial
intelligent technology makes e-commerce systems more intelligent so it is convenient for
buyers and sellers to carry out business transactions via brokers in digital business
environments [
As mentioned above, such approaches have focused on studying brokers as the third party to allocate buyers’ requirements to sellers’ offers in business environments. However, there are few theories and guidelines to help a broker agent in intelligent e-commerce systems to find out optimal allocation solutions using CSP techniques and multiattribute trading. Most current brokers in e-marketplace only provide buyers’ or sellers’ trade information and do not really employ artificial intelligence techniques to allocate buyers to sellers. The lack of a comprehensive optimization approach on allocation could not provide a solid foundation to improve market efficiency in digital business environments. Therefore, how to allocate buyers to sellers using CSP techniques under the consideration of constraints between buyers and sellers is one of the most important challenges for a broker agent. The novel approach in this paper is proposed to solve this challenge.
The rest of this paper is organized as follows. Problem description is presented in Section 2. The proposed approach for a broker agent is introduced in Section 3. An experiment is described in Section 4. Section 5 gives conclusions and points out our future work. 2
Problem description There are three members in the trading process with multi-attribute trading, i.e., buyers, sellers and a broker. The broker is often called a facilitator, who acts as an intermediary between buyers and sellers in multi-attribute trading. The consideration of the sellerbuyer matching problem through a broker agent is shown in Fig. 1.
Buyers
Sellers Requirements
Offers
Broker
Matching results Agent Matching results
The broker agent’s responsibility is to match ( ≥ 1) buyers with ( ≥ 1) sellers for the same commodity with multi-attribute trading in order to satisfy buyer’s
Broker-Based Trade Allocation in Agent-Based E-commerce 211 requirements and it is assumed that each buyer (seller) can buy (sell) commodities for each seller (buyer) at most.
Let a set of buyers = { 1, 2, … , }, a set of sellers = { 1, 2, … , }, buyers’
requirements and sellers’ offers are related to multi attributes which are divided to
attributes with hard constraints and soft constraints [
Based on the above analysis, the key problem is how to help a broker agent to achieve the optimal allocation results between trading (buyers and sellers) agents in agent-based e-commerce using CPS model. Therefore, the proposed allocation approach for a broker agent can solve this problem and is presented in Section 3. 3 3.1
A proposed approach for a broker agent
Framework of the proposed approach The framework of the proposed approach for a broker agent to allocate buyers’ requirements to sellers’ offers using constraint satisfaction problem models is presented in Fig. 2 as follows.
Receive buyers requirements and sellers offers
Calculate buyers satisfaction degree to determine constraint satisfaction layer Filter constraint satisfaction layer based on buyers satisfaction degree
Constraint satisfaction layer
The group of buyers The group of sellers Allocate buyer s requirements to seller s offers • Constraint satisfaction problem models Obtain allocation results
In the framework, trading information related to buyers’ requirements and sellers’ offers is submitted to a broker agent. Then, buyers’ requirements are classified into attributes with three kinds of constraints, i.e., attributes with hard constraints, benefit soft constraints, and cost soft constraints. Based on attribute classification, buyers’ satisfaction degree for each type of attribute is calculated as per sellers’ offers to determine constraint satisfaction layer. After that, a broker agent can filter a constraint satisfaction degree layer based on buyers’ satisfaction degree to achieve a broker agent’s goals.
Finally, a broker agent carries out trade allocation using constraint satisfaction problem models to obtain the optimal allocation results to satisfy buyers’ requirements as per = -1 means that does not match with for attribute ℎ ( ∈ ) and means that matches with for attribute ℎ .
(ii) For an attribute type with benefit soft constraints: if Qjl < il then means that does not satisfy . If Qjl≥ C , then il is calculated as follows: ℎ = { ℎ -1 if Cig≠Qjg 1 if Cig=Qjg =(
Qjl - Qmin-l+φ t max-l - Qmin-l+φ ) , where = ⁄ − (1) =1 =-1. It (2) (3) 3.3
Applying CSP for a broker agent to carry out trade allocations To model CSP for allocation processes, a broker agent needs to determine constraints based on the calculation of buyers’ satisfaction degree. Relationships between buyers and sellers through buyers’ satisfaction degree are presented in Table 1 as follows.
, Qmin-l is the minimal value of seller in the set of values for the attribute and Qmax-l is the maximal value of a seller in the set of values for the attribute . A value t ∈ (0,1] helps a broker agent to carry out comparing a buyer’s satisfaction degree when is used to calculate
. φ= Qmin-l/2, φ helps a broker agent to solve some special cases such as only one seller in e-commerce systems or Qmax-l = Qmin-l. means that matches with for attribute with a buyer’s satisfaction degree . is in-between 0 and 1. If
is near 1, it means that is highly satisfied by for attribute ( ∈ ). that does not satisfy . If Cil≥Qjl, then is calculated as follows: (iii) for an attribute type with cost soft constraints: if Cil<Qjl then =-1. It means =( Qmax-l- Qmin-l+ φ
Qmax-l- Qjl+ φ ) , 1
Broker-Based Trade Allocation in Agent-Based E-commerce 213
is ’s satisfaction degree as per ’s offers under the consideration of multi-attribute trading. If does not satisfy for a certain attribute then = −1.
It means that
does not satisfy and the constraint between and in a CSP model is created for trade allocation processes. Trade allocation through a broker agent between buyers and
sellers under the consideration of constraints in this paper are solved using CSP techniques. Thus, variables, domain of variables and constraints are defined in the CSP model for trade allocation as follows. • Variables and domain of variables = 1
0, ∀ ∈ , ∀ ∈ • Constraints
∑ =1
∑ =1 ≤ 1, = 1,2, … , ≤ 1, = 1,2, … , = 0 = −1 ( = 1,2, … ) = −1 ( = 1,2, … ) Constraints in Equation 4 is decision variable constraints, if buyer matches with seller then = 1; otherwise
= 0. Constraints in Equation 5 and 6 are that each buyer (seller) only matches with each seller (buyer) maximally. Constraints in Equation 7 determine a constraint satisfaction layer. After a broker agent defines variables and determines the domain of variables and constraints, the broker agent uses the CSP techniques to find out the optimal allocation solutions to satisfy buyers’ requirements as per in subsection 4.1 and the experimental results are evaluated in subsection 4.2. (4) (5) (6) (7) 4.1
Experimental setting In the experiments, we generate an artificial data of 10 buyers and 10 sellers in the digital business environments of the real estate in Vietnam. Buyers’ requirements and sellers’ offers for goods contain five attributes, i.e., location (a1), price (a2), building size (a3), number of payment days (a4), property (a5). Based on buyers’ requirements related to five attributes, location (a1) and property (a5) are attributes with hard constraints because their constraints must be satisfied while price (a2) is an attribute with cost soft constraints and building size (a3), number of payment days (a4) are attributes with benefit soft constraints because buyers can be accepted to relax the values of the attributes to achieve their satisfaction degrees.
In the experiments, the proposed allocation approach for a broker agent is employed to find out optimal pair allocations under the different situations in the digital business environments of real estate in Vietnam. More specifically, two representative experiments are presented below to demonstrate the proposed approach effectively. 4.2
Experimental results Experiment 1: Trade allocations without filtering buyers’ satisfaction degree In this experiment, a broker agent applies CSP in Python to find out the trade allocation results under the consideration of 10 buyers’ requirements and 10 sellers’ offers related to 5 attributes and without filtering buyers’ satisfaction degree. In particular, a broker agent calculates each buyer’s satisfaction degree based on each seller’s offers to determine the layer of constraint satisfaction and based on each buyer’s satisfaction degree as per each seller’s offers, a broker agent determines constraints for the CSP model before a trade allocation process is carried out. Based on data of 10 buyers’ requirements and 10 sellers’ offers for goods with 5 five attributes in subsection 4.1, a broker agent finds out 145,421 solutions for trade allocation. The specific results without filtering buyers’ satisfaction degree are presented in Fig. 3.
Based on experimental results, a broker agent considers to select which solutions from allocation results to achieve a broker agent’s goals as per the constraints of buyers or sellers or both. For instance, if a broker agent wants to choose an allocation solution from 145,421 solutions to maximize buyers’ satisfaction degree, solution 128,861 of allocation is perfect because the normalization of buyers’ satisfaction degree in this solution equals 1. The specific allocation result in this solution is 1 ↔ 10, 2 ↔ 3, 3 ↔ 8, 4 ↔ 7, 5 ↔ 9, 6 ↔ 2, 7 ↔ 1, 8 ↔ 6, 9 ↔ 5, 10 ↔ 4.
Experiment 2: The consideration of relationship between buyers’ satisfaction degree and a number of allocation solutions
In the second experiment, the relationship between buyers’ satisfaction degree and a number of allocation solutions was studied. Buyers’ satisfaction degree is set to increase gradually to find out a number of allocation solutions. In particular, 8 levels of buyers’ satisfaction degree are set for this experiment, and a number of allocation solutions as per each level of buyers’ satisfaction degree is found. The specific results are presented in Fig. 4. Based on allocation results in Fig. 4, it is clear that if the requirement of buyers’ satisfaction degree is high, the number of allocation solutions is low and vice versa. It demonstrates that the proposed approach works effectively. In summary, a broker agent can take off flexible allocation solutions to satisfy buyers’ requirements as per sellers’ offers based on the constraints of buyers’ satisfaction degree.
This paper proposes the allocation approach for a broker agent in agent-based ecommerce using the CSP model. The proposed approach is novel because (i) the design of allocation approach for a broker agent is general so it can be applied into broad domains such as real estate, banking, education, etc.; (ii) the formula system is built to calculate buyers’ satisfaction degree as per sellers’ offer for multi-attribute trading; and (iii) a broker agent’s allocation process is implemented based on the constraint satisfaction process using CSP. CSP helps a broker agent to find out an optimal solution by means of satisfying various preferences of buyers and sellers. The experimental results demonstrate the promising performance of the proposed approach in aspects of satisfying buyers’ requirements as per sellers’ offers and finding out optimal allocation results based on constraints. Furthermore, future research would extend the proposed approach to solve complex business environments such as the consideration of negotiation, price policies, etc.