Facilitating e-Negotiation Processes with Semantic Web Technologies
Dickson K.W. Chiu1, Senior Member, IEEE, S. C. Cheung2 Senior Member, IEEE,
Patrick C. K. Hung3 and Ho-fung Leung4
1
Dickson Computer Systems, Hong Kong
Department of Computer Science, Hong Kong University of Science and Technology
3
Faculty of Business and Information Technology, University of Ontario Institute of Technology, Canada
4
Department of Computer Science and Engineering, The Chinese University of Hong Kong
email: dicksonchiu@ieee.org, scc@cs.ust.hk, Patrick.Hung@uoit.ca, lhf@cse.cuhk.edu.hk
2
Abstract
Semantic Web technologies have recently been maturing
to make e-commerce interactions and crossorganizational processes more flexible and automated.
Ontology has also been developed in various business
domains. However, researches in Semantic Web have
largely focused on the facilitation of successful matchmaking but not much on the negotiation upon matchmaking
failures and exceptions. In this paper, we propose a novel
application of Semantic Web technologies for the facilitation of e-Negotiation processes. We discuss how the elicitation of negotiation issues, alternatives, and tradeoff can
be streamlined. We further propose a novel methodology
for the elicitation of dependencies among negotiation issues so that negotiators can focus on tradeoff among inter-related issues, instead of arguing about single issues.
A negotiation plan can thus be derived to observe negotiation orders across different issues. As a result, negotiators
can have a better cognition of their negotiation tasks and
the overall e-Negotiation process can be streamlined. We
are extending a negotiation support system to demonstrate
the feasibility of our approach, which is the most useful to
repeatable and semi-structured negotiations in businessto-business (B2B) e-Commerce and e-Marketplace environments.
1. Introduction
Negotiation is a decision process in which two or more
parties make individual decisions and interact with each
other for mutual gain (Raiffa 1982). The order of a negotiation process should guide the progress of actual business
interactions. Proposals are sent to the other parties, and a
new proposal may be generated after receiving a counter
proposal. The process continues until an agreement or a
deadlock is reached, or even one or more parties quit.
Each party needs to determine reactions of the other parties and obtain their responses, and each party also needs
to estimate the outcomes that the other parties would like
to achieve.
However, negotiators tend to be ignorant of the others’
values and strategies, especially in a non-cooperative environment. As a result, negotiations may involve high transaction costs and do not always reach the best solution. The
Internet has recently become a global common platform
where organizations and individuals communicate among
each other to carry out various commercial activities and
to provide value-added services. However, as many business activities become automated as electronic transactions, negotiation between human can be a bottleneck. A
major problem of this is its slowness, which is further
complicated by issues of culture, ego, and pride (Raiffa
1982). An automated negotiation system should conduct
negotiation to create value by interacting with different
parties to create mutually acceptable deals. This is particularly applicable to standard business interactions that
could be taken place over the Internet, such as real-estate
transactions, purchase and sale of goods, etc.
During negotiation processes, understanding the interactions between parties is critical (Griffel 1997). Negotiation often involves the evaluation of several issues and of
several alternatives per issue. As a result, a large combination of values has to be evaluated by decision makers,
which is time consuming and could be difficult for decision makers from a cognitive perspective. Thus tradeoffs
can often be identified when two or more issues are considered simultaneously (Druckman 1977). Most of the
related work in negotiations takes the consideration of
issues in isolation. Decision makers are assumed to consider the issues one at a time, in a stepwise fashion, instead of integrating multiple issues into a single package
so that potential tradeoffs can be recognized (Foroughi and
Jelassi 1990).
 The work described in this paper was supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. HKUST6170/03E).
Recently, Semantic Web technologies (Fensel et al.
2001, Daconta et al. 2003) have been maturing to make ecommerce interactions more flexible and automated. The
Semantic Web provides explicit meaning to the information available on the Web for automated processing and
information integration based on the underlying ontology.
An ontology defines the terms used to present a domain of
knowledge that is shared by people, databases, and applications. In particular, ontologies encode knowledge possibly spanning different domains as well as describe the
relationships among them. Ontologies have also been developed in various business domains such as HIPAA
(2003). One can imagine that ontologies can help negotiators to better understand the negotiation process.
However, researches in Semantic Web have mainly focused on the facilitation of successful matchmaking but
not much on the negotiation upon matchmaking failures
and exceptions. Yet, ontologies facilitate mutual understanding of negotiators on the negotiation issues and alternatives. Our previous papers (Cheung et al. 2002, 2003)
discuss inter-dependencies among issues help formulation
of efficient negotiation processes, but do not discuss how
to elicit such dependencies systematically. In this paper,
we further propose a novel methodology for the elicitation
of dependencies among negotiation issues from ontologies
so that negotiators can focus on tradeoff among interrelated issues, instead of arguing about single issues. A
negotiation plan can thus be derived to observe relationships across issues. As a result, negotiators can have a
better cognition of their negotiation tasks and the overall
e-Negotiation process can be streamlined. Further, this
also offers a basis for checking the completeness of issues
and alternatives for the negotiation.
The remainder of this paper is organized as follows.
Section 2 discusses background and related work. Section
3 presents an e-Negotiation concept model based on which
we formulate an methodology of the overall e-Negotiation
process. Section 4 describes a motivating example ontology. Section 5 discusses how ontologies are useful in negotiation. Section 6 describes our system architecture and
some implementation details. This is followed by discussions and summary in Sections 7.
2. Background and Related Work
Computer applications were first employed for negotiation support in the 1960s. In the 1980s, computer-based
Negotiation Support Systems (NSS) emerged, and they
were typically used for training and research in a laboratory environment but rarely used in practice (Delaney et al.
1997). In general, NSS have the following basic features
(InterNeg 2000): (1) a formalism to describe the negotiation activity in terms of choices and outcomes, (2) a way
to generally characterize the associated outcome probabilities, and (3) a methodology for processing the model to
evaluate the expected value of choice alternatives. NSS
normally assist negotiators to assess situations, generate
and evaluate alternatives, and implement decisions.
NEGOTIATOR (Bui and Shakun 1996) is a classical
NSS that seeks to guide negotiators to move their individual goals and judgments to enhance the chance of achieving a common solution. It supports problem adaptation
through information sharing, concession making, and
problem restructuring or re-framing. However,
NEGOTIATOR only helps the negotiators make decisions
without explicit support to other entities involved in negotiation. INSPIRE (InterNeg Support Program for Intercultural Research) (Kersten and Noronha 1999) is a webbased prototype for supporting inter-cultural as well as
intra-cultural negotiations. It can conduct negotiation
anonymously, evaluate the goodness of an offer, and review the history of a negotiation. INSPIRE supports the
communication among negotiators by exchanging messages, but it does not directly deal with the interactions
among different entities.
Analysis by Forrester (2000) research reveal that 18%
of global exports will flow online by 2004 and that crossborder electronic marketplaces (e-Marketplace) trade will
surpass $400 billion. Despite technical challenges, eMarketplaces have emerged to be important trading platforms in recent years. The popularity of e-Marketplaces is
largely attributed to their improvement in economic efficiency, reduction in margins between price and costs, and
speeding up complicated business deals (Feldman 2000).
Although most e-Marketplaces provide adequate support
for product and price discovery, their support for negotiation is far from satisfactory (Cho 2001). Negotiation support is mostly limited to simple bidding functions. There is
a lack of general support for bargaining like the proposed
mechanism in this paper. Despite rapid automation of the
other phases of e-commerce transactions, negotiations are
often done by using emails or traditional manual communication technologies such as phones or face-to-face meeting, causing serious overhead costs (Cho 2001).
As for negotiation support in e-Marketplaces, Yen et
al. (2000) propose an intelligent clearing-house approach
that supports both data and textual information about dynamic markets during negotiation, and develops an agentbased prototype Virtual Property Agency. Cho (2001)
studies various requirements of negotiation support in eMarketplace and evaluates a number of popular eMarketplaces. The work further provides a framework for
designing and evaluating a multi-dimensional auction
model. However, these studies do not cover different
modes of negotiation comprehensively in one complete
framework nor negotiation based on e-contracts. Schoop
and Quix (2001) present the negotiation process as the
exchange of contracts between the parties in an eMarketplace. The contract contents are presented as extensible semi-structured documents. During the negotiation
process, the contract evolves over time until a final agree-
Ontology
Party
*
involves
Negotiation
2..*
Auxiliary
Concept
1..*
1
makes
1..*
resolves
1 1..*
Task
*
1..*
Base
Concept
Issue
1
dri
ve
s
Offer
maps to *
s*
1
1
Accepted Offer
*
Concept
te
la
mu
f or
Plan
1..*
1..*
1 *
precedes
*
*
indivisibly
relates to
has
1..*
Alternative
Value
Accepted Alternative Value
Fig. 1. Conceptual Model of e-Negotiation in UML Class Diagram
ment has been reached or the negotiation is terminated. All
these works do not consider relations among issues of negotiation or other fundamental mechanisms relating to the
effectiveness of contract negotiation.
On the other hand, although Semantic Web technologies are maturing, ontology standards are still forming
(Fensel et al. 2001). Challenges remain for reusing available ontological information and researchers focus on information integration. In the past years, there are different
standardized languages proposed. For example, DARPA
Agent Markup Language (DAML, 2004) is a language
created by DARPA as an ontology language based upon
the Resources Description Framework (RDF, 2004).
DAML-S was designed to serve as the basis for representing descriptions of inverses, unambiguous properties,
unique properties, lists, restrictions, cardinalities, pairwise
disjoint lists, and data types. The Web Ontology Language
(OWL, 2004) is an eXtended Markup Language (XML)
proposed by the World Wide Web Consortium (W3C) for
defining Web ontologies. OWL ontology includes descriptions of classes, properties, and their instances, as well as
formal semantics for deriving logical consequences in
entailments.
Yu and Mylopoulus (1996) consider dependencies of
business goals but not down to the practical details of issue dependencies for negotiation. Phelps et al. (2004) suggest the use of ontology for agent-based negotiation with a
focus on the heuristics of bidding strategies of auctions
instead of negotiation plan for bargaining support. Lee
(2000) points out the use of semantic value and ontology
servers with the help of context agents to avoid inconsistency in the exchange of offers during e-negotiation, but
not further to the formulation of negotiation plans. Ontology negotiation enables users to cooperate in performing
an activity based on different ontologies (Bailin and
Truszkowski 2001). Modeled on the patterns of successful
human communication, ontology negotiation consists of a
series of interpretations and clarifications intended to locate common vocabulary and assumptions (Bailin and
Lehmann 2003). However, these studies concerned with
how consensus of ontologies can be arrived at. They do
not consider further how agreed ontologies can help the
formulation of negotiation processes in general, as our
novel attempt in this paper.
3. e-Negotiation Process Conceptual Model
and our Methodology
Whereas much research has focused on supporting negotiation activities with various information technologies,
the proposed approach in this paper concentrates on the eNegotiation based on ontologies. In this section, we introduce an e-Negotiation concept model and a methodology
to support it.
3.1. e-Negotiation Conceptual Model
Fig. 1 presents an e-Negotiation conceptual model in
the Unified Modeling Language (UML)(OMG 2001) class
diagram based on ontologies. An e-Negotiation is made of
up tasks, each of which aims at resolving an issue or a
collection of co-related issues. Each of these issues maps
to a set of concepts and their relationships based on an
agreed ontology. If an issue is mapped into exactly one
concept in an ontology, we call this concept a base concept for the negotiation. However, if an issue can break
down into several concepts according to an ontology, we
call these concepts auxiliary concepts. The agreed ontologies help identify the inter-relationships among the issues
and concepts, as well as possible alternatives for issues (as
explained in Section 5). An e-Negotiation plan can thus be
formulated based on the relationships across these concepts. The plan presents a strategy to drive and organize
activity
process start
condition
select agreed
relevant ontologies
for each
collection
of co-related
issue
* map issues into
ontology concepts
[not consistent]
[consistent]
derive concept
relations
check consistency
of issues &concepts
synchronization bar
process termination
creation of
agreement
[all issues are
resolved]
Pre-negotiation
phase
formulate
plan
identify issues
[need to revise
tradeoff model]
[need to identify
new issues]
identify
alternatives
evaluate tradeoffs
& make offers
Negotiation
phase
[quit]
Fig. 2. Ontology Based e-Negotiation Process Model in UML Activity Diagram
various tasks in an e-Negotiation. Multiple offers and
counter offers are made in a task until a consensus has
been reached. A task in an e-Negotiation represents some
work that needs to be executed by a set of parties that can
be a negotiator, or even a program such as Negotiation
Support Systems (NSS) to resolve a specific issue.
3.2. Methodology for Ontology based e-Negotiation
Process Formulation
Fig. 2 summarizes our proposed methodology for formulation of e-Negotiation processes based on ontologies
in the notation of UML activity diagram. This negotiation
process is driven by our conceptual for e-negotiation as
described in previous sections. Both parties have to participate in each constituting activity of the process, which
consists of two major phases: pre-negotiation and negotiation. Although the pre-negotiation phase looks complicated, it is based on the most common and logical way of
analyzing the issues with ontologies (as detailed in Section
5). We do not preclude other possible sequences for negotiation plan formulation. In particular, negotiation plans
once elicited can be stored in a repository for reuse and
adaptation. That means, if negotiators agree, they may just
pick a negotiation plan from the repository and starts right
away. Therefore, our approach is the most suitable for
negotiations in e-marketplaces or B2B e-commerce, where
semi-structured negotiations are often repeated and negotiation efficiency is important.
The e-Negotiation process leads either to a successful
creation of an agreement or to nothing. Note that only
through mutual concessions can the negotiation process
reach an agreement. The following steps further elaborate
on our methodology.
1. First of all, the negotiators have to determine the issues
to be negotiated.
2. At the same time, commonly agreed ontologies are selected to help the elicitation of issues.
3. Issues are related to the concepts in the selected ontologies.
4. Check all the dependencies of concepts that constitute
the issues from the (refined) ontology map. Mutually
dependent clusters of concepts determine the indivisible
group of negotiation issues that have to be negotiated
together so that effective tradeoff can be evaluated.
5. Check the consistency of all the concepts, issues, and
their dependencies (Cheung et al. 2002).
6. For a consistent plan, we can proceed to elicit the possible alternatives for negotiation; otherwise we have to
re-iterate from step 3.
7. According to the dependencies, we can formulate a
precedence graph of the issues and issue groups. Based
on the precedence graph, efficient negotiation plan can
be determined to exploit the maximum possible concurrency. The progress of a negotiation can be thus visualized.
8. Negotiators are then ready to make or evaluate offers
and counter-offers based on the plan. An agreement is
successfully created when all issues have been resolved.
9. Should new issues arise during the negotiation phase
(say, due to incomplete specification), repeat from step
3 to analyze the new issue and its relationships to existing ones. In real-life, the formulation of a negotiation
plan may involve several iterations before attaining the
consensus of all negotiators. This reflects the interrelationships among the parameters may not often be
captured precisely in one-shot.
4. Motivating Example
Sale negotiation activities are the most common in ecommerce and particularly in e-marketplaces. Ontologies
help e-commerce activities through mutual understanding
Discount
Total
Amount
{unordered} attributes:
deposit, installment, payupon-delivery, …
Sale
Order
Payment
Terms
*
Order
Line
Quantity
Appearance
{ordered} attributes: small,
medium, large, extra-large
Size
{unordered} attributes:
brick red, crimson, …
Unit
Cost
Shipping
Cost
Payee
Delivery
Delivery
Date
Refunding
Policy
Insurance
Insured
Amount
Premium
Color
Red
Insurer
Purple
{unordered} attributes:
light purple, magenta, …
Fig. 3. An Ontology for Sale Negotiation of Rubber Gloves in UML Class Diagram
and the facilitation of information exchange (Fensel et al.
2001).
Fig. 3 presents an example ontology for the negotiation
over a selection of concepts in a sale order of rubber
gloves. Concepts are represented in rectangular boxes. A
Sale Order may consist of multiple Order Lines, each of
which describes the Quantity to be ordered, Appearance
and Unit Cost. Appearance consists of Size and Color. The
former may attains a value ranging from small to extralarge while the latter can be further classified into different specific color concepts, such as Red, Purple, and so on.
Besides the Order Lines, a Sale Order is characterized by
the information about Payment Terms, Discount, Refunding Policy, and the Total Amount of the order. Delivery
involves three issues: Shipping Cost, Delivery Date, and
the associated Insurance. In addition, directed lines show
the dependent relationships among concepts and lines
without arrows denote bi-directional relationships.
Planning is a critical part of negotiation. There is usually one major issue (e.g., price), and several minor issues
(e.g., insurer) in any negotiation. In negotiation, there are
three types of planning (Lewicki and Litterer 1985): (1)
Strategic planning is used to define long-range goals and
to position oneself toward long-rang goals, (2) Tactical
planning is the process of developing short-range tactics
and plans to achieve long-range goals, and (3) Administrative planning is the process by which both manpower and
information are marshaled to make the negotiation proceed smoothly. Therefore, we have to establish a negotiation plan, depicting an order to discuss those issues. Traditionally, the functions of the negotiating plan (Marsh
1987) are to define the initial strategy with the supporting
arguments. The initial strategy includes the order of issues
to be negotiated. In the next section, motivated by this sale
order example, we discuss how ontology helps formulate a
negotiation plan. Furthermore, we discover different types
of relations among negotiable concepts. Based on these
relations, we can determine a negotiation plan that facilitates collaborative negotiation processes.
5. Are Ontologies Helpful?
In this section, we discuss how ontologies help the
overall formulation negotiation process. Though the use of
ontologies in groupware and collaboration systems is not
new, we show how ontologies can be applied in a much
wider and important scope in negotiation processes.
5.1. Understanding Negotiation Issues from Ontologies
The difficulties during the exchange communication
between users are the inconsistency in the represented
value and how to make the data interchange meaningful.
Thus, ontologies are becoming increasingly important as a
component of online commerce offerings. Ontologies can
present machine-understandable semantics of data to facilitate the negotiation about products, or help automatically
configure products and services according to specified
requirements. In particular, shared and agreed ontologies
provide common definitions of the terms to be used in the
subsequent negotiation processes.
We propose the following methodology extended from
well-known graph search algorithms (Cormen 2001) to
enhance the completeness of issues in requirement elicitation:
Negotiate Size
Negotiate Unit
Cost, Quantity
& Delivery Date
Negotiate Color
Negotiate
Refund
Policy
Negotiate
Shipping Cost
& Payee
Negotiate
Payment
Terms
Negotiate Insurance
Premium, Insured
Amount & Insurer
Negotiate
Discount
Compute
Total
Amount
Fig. 4. A Possible Negotiation Plan for Rubber Gloves Sale in UML Activity Diagram
1. Issues are preliminarily identified in the first round.
2. For each identified issue, check if an issue can be
mapped directly to a concept. If not, see if an issue can
be refined into a set of more specific concepts, which
combined can represent the issue. A typical example is
that a cost can be refined into constituent costs that sum
up to it.
3. Ontologies are often incomplete and therefore subject
to further refinement. New concepts can be introduced
to the ontology upon mutual agreement. However, the
relation of a new concept to existing ones should be
elicited to help understand the concept itself as well as
determine potential dependence of issues for the negotiation.
4. For each identified concept c, examine every un-visited
node n adjacent to c in the ontology map.
5. For each such node n, see if the new concept is relevant
to the negotiation problem.
6. Repeat step 4 and 5 until no more related new concepts
can be identified.
7. Only after successful negotiation do we need to consider combining newly identified concepts back to specify
a more concise agreement, because we advocate negotiation centered on concepts.
5.2. Understanding Dependencies of Issues from Ontologies
As we are mapping issues to concepts in ontologies (as
described in the above sub-sections), we also discover
their inter-relationships at the same time. Based on concepts in databases and artificial intelligence of computer
science, we identify the following typical categories of
dependencies among issues.
 Functional dependency – This is the main type of dependence that motivates this research. The concept is
borrowed from fundamental relational database concepts (Elmasri and Navathe 2000). The alternative for
an issue is determined by the alternatives(s) of other
issue(s). For example, cost of production depends on
delivery date and quantity.
 Computational dependency - This is a more obvious
type of functional dependency, which has a hardwired
computational formula. For example, insurance
amount = percentage * cost of goods.
 Requirement dependency (constraint satisfaction) –
Only after the determinant value is known can viable
alternatives be determined. For example, whether a
customer may pay by credit card, bank draft, or remittance is evaluated according to the total amount.
Therefore, only after the total amount is determined
can the negotiation of payment method take place.
 Classification dependency – This is a special type of
requirement dependency in which the classification of
another issue is dependent on the outcome of an agreed
issue.
5.3. Indivisible Components of Issues for Tradeoff
Evaluation and Negotiation Plan
Some concepts (and therefore issues) have to be negotiated together at the same time. It occurs when there are
cyclic dependencies among the concepts. Such group of
concepts is mutually dependent and therefore must be consider altogether for tradeoff as they cannot be individually
or sequentially considered during negotiation. After eliciting the dependencies, we can therefore draw a precedence
graph (Cormen 2001) of the issues and issue groups for
formulating a negotiation plan.
Note that in the task “formulate plan”, we construct a
detailed process to realize the activity “make offers and
counter offers.” Fig. 4 gives a possible process for a scenario negotiating the sale of rubber gloves. The negotiation starts with the issues Size, Color, and Refunding Policy concurrently. Once the Size and Color are decided, the
issues of Unit Cost, Quantity, and Delivery Date are then
negotiated. The process succeeds with the computation of
the Total Amount of the order.
Multiplatform Support Subsystem
Multi-platform
Devices
WAP
Gateway
SMS
Gateway
Internet
Messenger
Web
Server
bids & offers
e-Negotiation
Executing
Subsystem
e-Negotiation process
e-Negotiation
Session
Manager
e-Negotiation
process
issue
dependency
revised
ontology, issues
e-Negotiation
Data &
Repository
existing
ontology
Issue
Dependency
Editor
Ontology
Maintenance
Subsystem
Ontology
Editor
Ontology
Generator
ontology
task
dependency
Tasks
Organizer
ontology,
issue
e-Negotiation
Matching
Subsystem
e-Negotiation
Process
Generator
ontology
Search
Engine
criteria,
issues
Criteria
& Issues
Editor
Fig. 5. System Implementation Architecture
5.4. Understanding Possible Alternatives for Issues
from Ontologies
Often, alternative for issues cannot be expressed in
numerical values. Alternatives are often in discrete values
by its nature, such as country of origin, shipping company,
and so on. Alternatives for other issues are often not quantized in normal practices because of difficulties in recognizing them. For example, color is specified by its common name or more professionally in a color code, but rarely expressed in the wavelength of constituent light-waves.
In many other occasions, alternatives are not quantized for
simplicity and convenience. For example, alternatives for
size may be just small, medium, or large because either the
issue is not important in the context or a precise value is
not required.
When a complicated issue is decomposed into concepts, the elicitation of options can be much streamlined.
For example, when the issue of appearance is decomposed
into the concepts of size, color, and shapes, the alternatives of each concept can then be easily elicited.
Ontologies not only can provide sets of alternatives for
issues from membership relations, but often also partial or
even total explicit ordering of them (e.g., small < medium
< large < extra-large). In addition, implicit (partial) ordering may be elicited via inheritance (“is-a”) or composition
hierarchies. Thus, such extra knowledge provided by ontologies can further assist negotiators to evaluate offers
against their preferences and determine which counteroffers are decreasing the indifferences rather than increasing them.
6. System Architecture and Implementation
Fig. 5 shows the implementation architecture for our
template driven e-Negotiation support system based on
ontologies. The architecture is designed to support eNegotiation processes instantiated from the e-Negotiation
conceptual model in Fig. 1 and the methodology in Fig. 2.
The design aims to provide flexible and reusable components. The proposed system is acting like a negotiation
embellisher who knows the values, beliefs, and constraints
of both parties. Then, it seeks an efficient contract that
both parties would prefer to the negotiation context they
have created (Raiffa 1982).
The architecture is made up of four subsystems. The
Ontology Maintenance Subsystem allows negotiation parties to specify and edit their negotiation issues and alternatives based on ontologies. The search engine selects the
most appropriate ontologies based on a given set of criteria and issues. The retrieved ontology may be further revised using the Ontology editor to address all major required issues and alternatives. Revised ontologies as well
as the issues and their alternatives thus derived may be
stored in the repository for later retrieval. These data will
be used by the e-Negotiation Matching Subsystem to determine a suitable e-Negotiation process based on the issue
dependency supplied. The selected e-Negotiation process
is then enacted through the e-Negotiation Executing Subsystem. The Multiplatform Support Subsystem provides
front-end supports to multiple platform devices, such as
WAP, SMS, and Web browsers.
[acceptance received]
[failure received]
[ready to make an offer]
Identify the
issue(s) to be next
negotiated in the
plan
Prepare
reservation
prices
[counter-offer
received]
[offer received]
[false]
Have all
issues been
negotiated?
Evaluate offer /
counter-offer
[unacceptable offer]
[false]
Quit?
[acceptable offer]
Notify
counterparty
of acceptance
start a new
negotiation
cycle
Revise
reservation
prices
Make offer /
counter-offer
[true]
Notify
counterparty
of failure
[true]
Successful
negotiation
Fig. 6. UML Activity Diagram for Making Offers and Counter-offers in a Negotiation Session
Fig. 6 depicts our design for maintaining a negotiation
session in UML activity diagram. Each negotiation cycle
starts with the identification of a set of interrelated issues
to be next negotiated, according an agreed negotiation
plan (as discussed in the previous sub-section). Each party
will then prepare the reservation alternatives (reservation
price) of these issues. After that, they may either make an
offer to or wait for some offers from counterparties. If a
party is not satisfied with the (counter-) offer, another
counter-offer or a failure message will be received. A negotiation cycle finishes successfully if an acceptance notification of previous (counter-) offer is received. Finally,
the negotiation process succeeds when all issues have been
successfully negotiated.
Based on this architecture, we have developed an eNegotiation support system with contemporary technologies, including Java applets, Java Server Pages, and Enterprise Java Beans. We are extending the system with support for ontologies with the OWL Web Ontology Language (OWL, 2004) (instead of DAML) because W3C has
designed OWL as a standard (Web-Ontology Working
Group 2004).
OWL has been proposed to provide three increasingly
expressive sub-languages for specific communities of implementers and users, namely, OWL Lite, OWL Description Logics (OWL DL), and OWL full. OWL Lite supports the basic need for a classification hierarchy and simple constraints. For example, while it supports cardinality
constraints, it only permits cardinality values of 0 or 1.
Thus, OWL Lite provides an easier implementation and a
quicker migration path for thesauri and other taxonomies.
OWL DL supports maximum expressiveness while retain-
ing computational completeness (all conclusions are guaranteed to be computed) and decidability (all computations
will finish in finite time). OWL DL includes all OWL language constructs, but they can be used only under certain
restrictions (for example, while a class may be a subclass
of many classes, a class cannot be an instance of another
class). OWL DL is so named due to its correspondence
with description logic, a field of logic that forms the formal foundation of OWL. OWL Full supports maximum
expressiveness and the syntactic freedom of the RDF, but
with no computational guarantees. For example, in OWL
Full a class can be treated simultaneously as a collection
of individuals and as an individual in its own right. OWL
Full allows an ontology to augment the meaning of the
pre-defined (RDF or OWL) vocabulary. Thus, ontology
developers adopting OWL should consider which sublanguage best suits their needs. More specifically, we employ OWL DL because it provides a standard set of elements and attributes with defined semantics, for defining
terms and relationships in ontology. In addition, OWL DL
contains a set of logic-based primitives that are specifically useful in intelligence informatics.
As such, a flexible NSS for different e-Commerce domains and different negotiation plan can be supported,
without modifying the underlying system. The negotiators
only need to define suitable ontology and derive an effective negotiation plan. This tremendously reduces the development time and costs, and therefore provides a big
competition edge under this fast evolving digital economy.
7. Discussions and Summary
In this paper, motivated by the recent maturing of Semantic Web technologies, we have developed a conceptual
model for e-Negotiation and proposed a pragmatic methodology for e-Negotiation processes formation with the
help of ontologies. In particular, we have shown how the
elicitation of negotiation issues, tradeoff, and alternatives
can be streamlined. We further develop a novel way for
the elicitation of dependencies among negotiation issues
so that negotiators can focus on tradeoff among interrelated issues, instead of arguing about single issues. Observing the logical order across different groups of issues,
we can thus formulate an effective negotiation plan with
tradeoff support. By mapping issues onto concepts of
agreed ontologies, negotiators can control the openness of
issues and our algorithm verifies the completeness of elicited negotiation requirements. We have also built a negotiation support system with the support of issue dependencies to demonstrate the feasibility of our approach, and we
are now enhancing it with ontology support.
Through our proposed negotiation support mechanisms, negotiation processes are properly guided, recorded, and managed. It also helps simplify the communication
messages required across organizations during negotiation
activities. Although this could be a serious limitation to
general bargaining activities such as political and governmental negotiations, e-commerce activities are usually
more structural and repeatable, thereby fitting well into
our assumptions. In additional, tradition manual negotiation processes can neither support automation of negotiation (such as through software agents) nor effective integration with Enterprise Information Systems (EIS).
As the participants of e-marketplaces often have to
evaluate a large number of offers with different options
while they are updated frequently of the market news
about substitutive products, ontologies help them better
understand the offers as well as evaluate and specify their
preferences in a stepwise manner.
Most of the tasks in the pre-negotiation phase of our
negotiation methodology can be prepared by emarketplace administrators based on policies and requirements of the e-Marketplace. Ontologies are specified with
reference to relevant industry domains for different categories of products or services. At the same time, common
issues and criteria for negotiation can be identified with
the typical requirements of the target users. Sample negotiation plans can therefore be formulated are then stored in
a repository and available for reuse and user adaptation.
Therefore, users of a well-managed e-Marketplace not
only enjoy convenience but also the pre-programmed
knowledge thus obtained.
Further, the negotiation plan (as shown in Fig. 4) comprising a partitioning of the problem facilitates the tradeoff
evaluation of complex negotiation issues to be partitioned
into separate concerns, each of which may be taken care
by an individual group in a large enterprise. For instance,
the tradeoff evaluation of (i) payment terms and deposit,
and (ii) freight and insurance can be taken care of, respectively, by the purchase department and logistics department of the same enterprise. As such, matching roles or
individuals in an enterprise to negotiation tasks can be
supported as a higher level separate layer upon the negotiation processes. With the integration of the NSS into enterprise information systems (EIS) or workflow management systems (WFMS) (Chiu et al. 1999, 2001), complicated tradeoff evaluation processes with reference to distributed enterprise data can be streamlined. In addition,
management and matchmaking of roles within an organization to negotiation tasks can be taken care by algorithms
and methodologies developed for workflow management
(Chiu et al. 1999). This is an important direction to be
worked out in our continuing research.
We further perceive that our proposed highly modular
eNSS engine can be plugged into different types of existing e-Marketplaces to enhance their capabilities. In this
way, negotiation can be streamlined in a semantic service
grid (Gentzsch 2002) consisting of end-users, eMarketplaces, and enterprises, with our ontology based
negotiation methodology. Sophisticated decision support
and knowledge capturing related to negotiation can be
facilitated with ample opportunities for review, reuse, and
improvements.
This work can be expanded in several directions. From
real-life practices, we discover that negotiation sometimes
involves classification rules rather than just offers and
counter-offers of alternatives. This occurs when negotiator(s) have no alternative for such issues because this is
already pre-determined or constrained by other factors that
are not changeable. For example, in a sale negotiation, a
seller requests the goods to be considered “Hong Kong
made” in order to enjoy certain tariff quota. Negotiation of
this definition may be argued on the percentage of addedvalue in the goods’ manufacturing process that takes place
in Hong Kong. Similar negotiations may also occur when
classification rules have ambiguities and incompleteness.
In this paper, we have only discussed the scenario of
one-to-one (two parties) negotiation of contract. We are
currently investigating other scenarios of one-to-many
(more than two parties at one time) negotiation of contracts. On the other hand, we are looking into further issues of e-Marketplaces, especially those related to mobile
clients (Chiu et al. 2003) and constraint-based negotiation
(Chiu et al. 2004).
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