APPLYING KNOWLEDGE BEADS FOR AUTOMATED

NEGOTIATION

Zhuang Yan, Francis Yan and Simon Fong

Faculty of Science and Technology, University of Macau

Shi Meilin

Department of Computer Science, Tsinghua University, Beijing, China

Keywords: Automated Negotiation, Agents, Knowledge.

Abstract: A methodology called Knowledge Beads (KB) that was proposed in our previous paper features object-

oriented formatting and expressive power in defining a complex bill-of-material. In this paper, we show

how this knowledge representation methodology could be used in the design of automated negotiation

systems and how KB helps in giving a unified approach for representing the data throughout the process that

includes evaluation, negotiation, and post-negotiation. We identify two types of knowledge namely General

Knowledge and Negotiation knowledge, in the negotiation process. A methodology is proposed on how

these two types of knowledge can be used in automated negotiation.

1 INTRODUCTION

Previous research (Reeves et al, 2000) attempted

fusing knowledge into agent communication

languages and negotiation functions. They mostly

based on rule-based and/or logic-based approaches.

However, these techniques show their advantages in

individual applications. Knowledge management is

important in many scenarios where agent negotiation

is performed based on knowledge instead of rules

and logics alone. Furthermore those agents do not

have self-learning ability if they cannot interpret

knowledge.

Therefore, we opt for a model which can capture

the key concepts and elements involved in multi-

bilateral multi-attribute e-Procurement negotiation

traces. Especially, these include the relationships

among multiple negotiation parties, negotiation

strategies for trade-off on multiple procurement

attributes, and decision-action rules that drive an

automated negotiation system (Jennings et al.,

1996).

In essence, to automate the agent negotiation

process, it has been widely accepted that two

important technical tasks must be done: Firstly

formulate the negotiation process; and secondly

incorporate necessary negotiation knowledge.

Formalization of the negotiation process enables the

software to automate the process by following some

pre-defined algorithms. Incorporation of expert

negotiation knowledge enables a negotiation system

to conduct automated negotiations effectively and

intelligently on behalf of its users. The knowledge of

the human negotiation experts can be captured in

some form of requirements, constraints, events,

strategic rules, and preference scoring and

aggregation methods. However, due to the diverse

and complex nature of negotiation, the lack of

knowledge interoperability and knowledge-reuse has

posed certain difficulties to automated negotiation.

Through this research work, we anticipate more

efficient e-Commerce could be achieved with the

support of the knowledge in automated negotiation.

This paper sheds some light on knowledge

empowered automated negotiation in the multi-

bilateral multi-attribute e-Procurement environment.

The research aims are to formulate a complete

negotiation life cycle with a knowledge framework

for constructing both the negotiation context and the

negotiation expertise; and to develop a model of

automated agent negotiation based on the knowledge

framework.

Yan Z., Yan F., Fong S. and Meilin S. (2007).

APPLYING KNOWLEDGE BEADS FOR AUTOMATED NEGOTIATION.

In Proceedings of the Second International Conference on e-Business, pages 90-93

DOI: 10.5220/0002114500900093

 SciTePress

2 KNOWLEDGE IN

AUTOMATED NEGOTIATION

In a negotiation session, there is plenty of data

which can be collected, manipulated and utilized.

Besides, domain knowledge and negotiation

expertise are crucial for defining negotiation

strategies, plans of actions, and preference scoring

and aggregation methods. Both negotiating parties

are responsible for collectively maintaining their

own data, rules and knowledge repository.

2.1 Knowledge Taxonomy

The goal of a corporate taxonomy is to provide a list

of authorized terms in knowledge management and

information seeking (Conway 2002), as well as the

mapping between concepts to connect negotiation

parties with the right knowledge at the right time.

Mainly two categories of knowledge are addressed

in the proposed framework: (1) general knowledge

and negotiation knowledge. General knowledge

provides the specification of different categories of

objects in the e-Commerce domain, which are the

fundamental knowledge. An object can be a RFQ, a

trader, a deal, or any object that associated with

manipulation methods. (2) Negotiation knowledge,

or negotiation expertise comprises knowledge of, or

skill in observation of experience gained through

negotiation in e-Procurement process. The concept

of experience generally refers to know-how or

procedural knowledge, which is the knowledge of

how to perform certain tasks.

2.2 General Knowledge

General knowledge in the negotiation life cycle

contributes to the formation of the fundamental

knowledge framework of the current negotiation

context. It mainly includes buyer’s RFQ, supplier’s

quotes, negotiators’ profiles, and negotiation traces.

Table 1: General knowledge in a negotiation context.

A negotiation trace is a log recording all the

messages exchanged between two negotiation

partners in a negotiation process. For successful

negotiation in which an agreement is produced in the

end, the negotiation trace contains useful knowledge

describing the nature and progress of the

negotiation.

2.3 Negotiation Knowledge

While general knowledge describes the whole

negotiation context, negotiation knowledge provides

the necessary knowledge used to carry out the

negotiation in an automated way. It comprises

business intelligence for negotiation including a

variety of negotiation strategies and business

conventions, as depicted in the following Table 2.

Table 2: Negotiation knowledge in negotiation context.

Negotiation knowledge are formulated and

specified as procurement rules and constraints by

negotiation experts. There are two types of

constraints. The fundamental type of constraint is to

define a valid range for an individual attribute, or an

inter-attribute relation for multiple attributes within

the same knowledge item. Another type of constraint

is defined between a particular knowledge item and

other relevant ones. This kind of constraint usually

exists in a Bill-of-Material (BOM) consisting of

multiple RFQs defined for different product items

respectively. Constraints are usually used in quotes

evaluation and ranking phase for screening qualified

quotes. They are also used in negotiation to screen

for attributes that fall within predefined ranges.

Besides constraints, rules are widely used to

describe the negotiation knowledge about

relationships.

2.4 Knowledge Representation

Knowledge Bead (KB) as an object-oriented

knowledge representation scheme was defined in

(Fong and Zhuang, 2002), as an encapsulation of

definition, behavior, and data: KB = Definition +

Behavior + Data.

APPLYING KNOWLEDGE BEADS FOR AUTOMATED NEGOTIATION

A KB can be a composite object, or a simple,

atomic part object in most cases; each has their own

methods and data. Definition means a static unique

description; this can be a UPC (Universal Product

Code) or a unique index implemented at the

ontology databases for referencing this KB.

Behavior is described by a set of possible methods

and rules manipulating KB’s and their attributes.

Some typical ones include KB formation,

duplication, attribute alteration, pruning and linking

to other KB’s. They are analogous to class functions

in object-oriented programming, and can be

inherited from base classes. Data consists of a group

of attributes defined for the KB. Associated with

each attribute, a weight is given as a relative priority

indicating how important this attribute is in the

current KB.

The use of KB in representing general

knowledge about the negotiation context is shown in

Figure 1. KB is created first by the user through

some user interface. The data on the submitted web-

page (form) is extracted into the construction of a

KB object that resides on the server. The KB object

is then used in the quote evaluation and negotiation

processes. Note that KB is a general data

representation format that can define the general

knowledge items, and that can be implemented in

object-oriented languages such as Java.

Figure 1: Representation of General Knowledge in KB.

Every general knowledge item can be

represented in a certain template. The main

categories of domain correspond to the types of

general knowledge items including RFQs, quotes,

proposals, agreements, profiles, and traces as listed

in Table 1.

The product space is represented as a labeled,

directed graph with two types of nodes: a leaf node

and a category node, as depicted in Figure. 2.. Every

leaf node in the product space is represented in a KB

template developed by the system. It inherits

attributes and behaviors from its parent category

node, along with new features and operations added.

Each KB template has an identity number composed

of the category name and the name of the leaf node.

The category name provides the basic domain

information about a general knowledge item which

makes use of the KB template. It is represented as a

sequence of labels corresponding to the edges in the

path, e.g:

/ProductCategories/Electroics&Computers/Came

ras&Photo/DigitalCameras

Product Categories

Electronics & ComputersHome

Cameras & Photo

Digital Cameras

Electronics

Software

Lamps & Lighting

Bedding

Clothing & Assessories

Men

Women

Uniforms

RFQ 1

Figure 2: Part of the product space of KB templates.

3 METHODOLOGY OF KB’S FOR

AUTOMATED NEGOTIATION

Figure 3: Methodology of KB’s for automated negotiation.

As general knowledge items are represented by

KB’s and negotiation knowledge are specified as

ICE-B 2007 - International Conference on e-Business

constraints and rules that defined on attributes in

KB’s, the classification and clustering of KB’s helps

to manipulate and present the knowledge whenever

it is needed by the system. Figure 3 shows how

negotiation knowledge and general knowledge are

used respectively for assisting the user to create a

RFQ, and for the automated negotiation process. At

the end of the process, log files are generated and

added to the general knowledge database.

To make use of the knowledge contained in

KB’s, the negotiator first identifies the function that

he need to do in the negotiation process. Then it’s

the knowledge agent who provides a concrete plan

of utilizing the appropriate knowledge in the specific

function. The proposed model also provides the

negotiator the flexibility to adjust the weight of the

knowledge factors which affect the function result.

To our knowledge, most current automated

negotiation systems lack the ability of specifying the

explicit use of knowledge in a systematic way, thus

lack an efficient knowledge assisted automatic

negotiation process. For this purpose, we define

meta-KB as a meta-object for describing the

procedural knowledge necessary to perform a certain

task in the e-Procurement context. It contains the

meta-knowledge about KB’s, which is knowledge

about knowledge. The function which makes use of

the meta-KB determines its discipline. Like an

ordinary KB, a meta-KB contains attributes forming

the knowledge. The attributes are either inherited

from an existing KB or defined especially for the

specific function, depending on the meta-KB’s

discipline. For each attribute, the meta-KB specifies

how the attribute value is obtained.

Several typical functions are executed many

times in different phases or in parallel during the

multi-bilateral negotiations. These functions include

supplier credit evaluation, quote evaluation, and

negotiation result assessment.

The meta-KB for evaluation of a supplier inherits

the attributes from the KB comprising knowledge

about a supplier’s credit as shown in Table 3. It is

illustrated in the following table.

Table 3: Meta-KB for supplier evaluation.

The tag ‘Meta-KB’ denotes it a meta-KB, and the

use of the meta-KB is declared at the top of the

table. It then specifies from which KB template that

the meta-KB inherits its attributes. The value of

Base Reputation is input from a Negotiation Expert

manually. The attribute Number of Contracts Made

has a returned function value evaluated on the

negotiation log. The function is denoted by f in the

table. The attribute Average Utility also has a

returned function value evaluated on the negotiation

log. The function is denoted by g in the table. The

negotiation log is a log containing all the past

successful deals committed with the particular

supplier. Weights associated with attributes are also

inherited from the supplier credit profile, which are

not shown here. Detials of the evaluation functions f

and g can be found in (Zhang, 2006).

4 CONCLUSION

We discussed issues of applying Knowledge Beads

(KB) into automated negotiation for e-Commerce. A

methodology that is based on Knowledge Bead, an

object-oriented ontology-based building block for

knowledge representation, is proposed. Using KB

and its methodology, quote specification and

bargaining process can be streamlined, and data

resulted from negotiation can be reused as

knowledge in future negotiation. This provides a

foundation for the knowledge management life cycle

designed for coexisting with the negotiation life

cycle.

REFERENCES

Reeves, D., Grosof, B., Wellman, M., and Chan, H., 2000.

Towards a Declarative Language for Negotiating

Executable Contracts. IBM Watson Research.

Jennings, N., Faratin, M., Johnson, T., Norman, J.,

O’Brien, P., and Wiegand, E., 1996. Agent-based

Business Process Management. International Journal

of Cooperative Information Systems, 5(2&3), 105-130.

Conway, S., and Sligar, C., 2002. Unlocking Knowledge

Assets. Knowledge Management Solutions. Microsoft

Corporation.

Fong, S., and Zhuang, Y., 2002. Enabling Agent

Negotiation in e-Trading Environments Through

Knowledge Beads. IEEE International Conference on

Intelligent Engineering Systems.

Zhang, Y., 2006. Knowledge-Empowered Automated

Negotiation System for B2B e-Commerce. PhD

Dissertation. Tsignhua University. Dec 2006.

APPLYING KNOWLEDGE BEADS FOR AUTOMATED NEGOTIATION