IMPROVING COOPERATIVE MANAGEMENT

Fuzzy Modeling and Proximity Networks

Seyed Shahrestani

School of Computing and Mathematics, University of Western Sydney

Locked Bag 1797, DC NSW 275, Penrith South, Australia

Keywords: Awareness modeling, Cooperative management, Fuzzy logic, Fuzzy proximity network, Network

management.

Abstract: The Internet, and perhaps of more relevance to this work, large enterprise networks are complex ICT

systems of prime business importance. The effectiveness of management of any complex system is heavily

dependent on understanding the functions of its components and their interactions with one another. As

such, quantifying collaboration and awareness levels can play significant roles in improving the

management efficiency. In most cases though, it is impossible to identify precise crisp models describing

the roles, functions, and interactions of such components in a useful manner. This can in turn be related to

the fact that the characterization of these concepts by human beings and managers is heavily based on the

use of linguistic variables. These variables and the communication of perceptions based on them are fuzzy

concepts in nature. This paper further elaborates these issues. To identify solutions, it discusses the relevant

notions of soft computing and explores the ways that the utilization of fuzzy awareness modelling can help

in improving cooperative management effectiveness.

1 INTRODUCTION

Provision of collaborative services requires

cooperation among various entities of an

organization. Associations and collaborations of

humans are partially or fully dictated by their level

of awareness of the ability of others to support them

to fulfil their responsibilities. As such, awareness

modelling and levels can play significant roles in

improving the management efficiency. To facilitate

collaborative services, some way to analyze

cooperation levels is needed. It is well established

that one of the fundamental problems in achieving

robust systematic solutions to problems encountered

in cooperative management environments relates to

the difficulty in quantifying collaboration and

awareness levels, for example see (Basker et al,

2002).

As discussed by several researchers, including

(Grudin, 1994) and (Lim, 2009), associations and

collaborations of humans are partially or fully

dictated by their level of awareness of the ability of

others to support them to fulfil their responsibilities.

One of the basic difficulties in achieving robust

analytical solutions in cooperative management

environments relates to the difficulty in quantifying

cooperation and awareness levels, for instance see

(Wang and Chang, 2004). These models heavily

depend on the use of intelligence. Conventional IT

solutions provide some degree of artificial

intelligence (AI) for processing and filtering the

data. However, human interactions remain essential,

as the data is often incomplete and conflicting or the

information may be irrelevant to the task in hand.

Furthermore, proper implementation and

utilization of AI enabled tools need to be considered.

For example, (Huang et al, 2008) have shown that

AI based network management systems that deal

with the problems at network layer, are mostly based

upon expert system techniques. From a broader

point of view, the ability to handle huge amounts of

information is a prerequisite for management of

complex systems. These issues have been discussed

in our previous works, for instance see (Shahrestani,

2008).

From a practical point of view, the assignment of

the awareness levels for various entities and roles

involved in a given task is more suitably achieved

with the linguistic propositions and words like

minimal or high. As we have shown before

Shahrestani S..

IMPROVING COOPERATIVE MANAGEMENT - Fuzzy Modeling and Proximity Networks.

DOI: 10.5220/0003645000710076

In Proceedings of the 6th International Conference on Software and Database Technologies (ICSOFT-2011), pages 71-76

ISBN: 978-989-8425-76-8

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

(Shahrestani, 2001), this can be related to the fact

that humans prefer to think and reason qualitatively,

which in turn leads to imprecise descriptions,

models, and required actions. Clearly in cooperative

management, the need for exploiting the tolerance

for imprecision and uncertainty to achieve

robustness and low solution costs is evident. This is

in fact, the guiding principle of soft computing and

more particularly fuzzy logic introduced by (Zadeh,

1965). In his break through work (Zadeh, 1994)

introduced the calculus of fuzzy logic as a means for

representing imprecise propositions (in a natural

language) as non-crisp, fuzzy constraints on a

variable.

This work will further discuss the utilization of

fuzzy logic concepts to identify a fuzzy framework

to quantify awareness levels to facilitate their

implementation. In that sense, uncertainty permeates

the entire management process. As we have

discussed before (Shahrestani, 2003), the latter piece

of information can be easily amended and handled

by fuzzy logic based approaches.

The remainder of this paper is structured as

follows. Section 2 establishes the background and

motivations for fuzzy awareness modeling. This is

further expanded in Section 3, where the design

framework incorporating the agent-based

cooperative management concepts are discussed. In

Section 4, notions relevant to fuzzy proximity

networks and execution of the design methodology

through are presented. The last section presents the

concluding remarks.

2 FUZZY AWARENESS MODEL

Awareness modeling is an area that has witnessed

significant research to define various types of

awareness and supporting awareness. In most of

these works, for instance see the pioneering works

(Grudin, 1994), it is argued that an individual’s level

of awareness is increased by insight and awareness

of information about a given event or object, rather

than by actually receiving that information. At any

case, to be of practical value in any collaborative

environment, a design methodology incorporating a

reasonable approach for utilization of awareness

levels is a prerequisite.

In most cases, some researchers such as (Wang

and Cheng, 2004) have argued that awareness levels

of an entity are altered by perception of information

about a given experience or object, rather than by

getting the actual information. Either way, for

effective integrated management, where shared

objectives and collaborations are the norm,

utilization of awareness levels is a requirement. In

this respect, it can be noted that in general it may be

advantageous to describe the awareness levels of any

role using the semantic definitions that are in fact

based on the use of linguistic variables, as first

discussed in (Mamdani, 1977). As stated before, it

can be noted that in general human beings

characterize the awareness levels of any role using

the semantic definitions through the use of linguistic

propositions and variables. For instance, a

supervisor may characterize a technician by simply

stating, “Technician D is the best in our group for

upgrading a particular link.” This can be easily

interpreted as: within the group of people of this

supervisor, D has the maximum awareness level for

that particular job. Such a characterization can be

conveniently modeled through utilization of fuzzy

logic and fuzzy modeling.

The fuzzy modeling is based on the fuzzification

of crisp values. For instance, assume that awareness

level, AL, of a given role, for instance technician D,

is defined in the crisp terms, e.g., between 0 and 4 in

the form

AL (D) = a (where a is crisply defined as a member

of {0, 1, 2, 3, 4}).

This is now replaced by

AL (D) is A.

where A is a fuzzy subset of the universe of the

awareness levels of the technician role. Following

on from the example above, for the most suitable

technician to do the job, the technician’s lowest

awareness level is represented by

AL (D) is maximal.

In this sense, while AL (D) = a, is a particular

description of the possible values of the technician’s

awareness level, the fuzzy set A represents a

possibility distribution. Now, the possibility of the

linguistic variable AL (D) is represented by a

linguistic value as the label of the fuzzy set taking a

particular (numerical) value b given by

Possibility {AL (D) = b} =

(b).

The knowledge about AL of each role for a

given task that is based on linguistic variables can

act as a descriptive and flexible profile for that role.

Given the semantic definitions that are actually

based on the use of linguistic variables this notion of

fuzzy logic is obviously more appropriate. More

specifically, the fuzzy values signify a technician’s

AL that can be used for different purposes. The

profiles can be used for identifying and ranking of

suitable technicians for a given task or conversely,

for a given technician it provides a means for

detection of the additional knowledge that the

technician needs to carry out a given task efficiently.

ICSOFT 2011 - 6th International Conference on Software and Data Technologies

Furthermore, through forming of fuzzy clusters of

profiles, one can establish aggregate profiles. Such

aggregate profiles can be used an overall picture of

the AL of the technicians within the organization.

One can now characterize interactions with fuzzy-

based definition of the awareness levels. These

provide for the description of the complex systems

and interactions using the knowledge and experience

of customers, managers, and others involved using

simple semantics.

3 DESIGN STRUCTURE

This work is based on the proper choice of the

repositories and information as guided by the

collaboration of all the individuals involved in

accomplishing a common goal. This may lead to the

concept of virtual awareness levels. The information

repositories are mostly structured on the utilization

of compound document-centric object architecture

similar to those described in (Umar, 1997).

Compound document architecture, made popular

through Microsoft Active X/DCOM, helps express

structured and unstructured knowledge in the form

of documents with hyperlinks. Over the years, they

have greatly evolved and are the cornerstone of web-

based document systems. In this sense, the required

awareness and the needed information are

essentially provided using compound documents

based on an object-oriented and web-based system

accessible via a browser and search engines.

Given the required awareness levels for the

variety of tasks in complex systems, each human

role is provided with a software agent. Each agent

attempts to provide the required awareness level to

the human role it is serving by interaction with other

agents and by search through the information base.

The implementation of such a multi-agent

framework needs to consider a range of intelligent

techniques, such as case-based reasoning, active

directories, neural networks, and appropriate rules

and policies.

To improve cooperation and efficiency, each role

through its agent, must be capable of determining all

relevant information for the task. In other words, the

task rather than the individual should dictate the

relevance of information and passing them on to the

human role. The information that is passed to the

individuals is built upon the possible connection

among various queries made by all involved

individuals. In this fashion, the overall conduct in

achieving the common goal can benefit from the

combined awareness levels of human roles and local

decisions.

As with normal practice in most human

organizations, to achieve overall coherency a

coordinator role is also considered. The role takes an

overall view of the tasks in hand. For instance to

avoid flooding any role with irrelevant or loosely

relevant information, the coordinator must be able to

grade the suitability of the information for the

individuals in accomplishing their functions in the

project. The correctness of the retrieval of such

information can be defined in the context of the

problem using the notion of membership in a fuzzy

set around the desired keyword.

Many of the current approaches are capable of

retrieving all relevant documents containing the

information that is indexed by the used keywords

and ranking them by some degree of relevance

according to the query made by an individual. In

most of these approaches, the presence or absence of

the keywords in the query and the indexing terms of

the documents form the basis for evaluation of the

relevance of a document to the query.

It can be note that generally speaking, it is easy

to combine multiple keywords within the query

made by an individual as an aggregate fuzzy set

using fuzzy operators. In a similar fashion, one may

propose that queries from several individuals can

also be based on the simplistic approach of

considering them as a single query with multiple

keywords.

However, as pointed out by many researchers,

for instance see (Horng et al, 2008) and (Shrivanian

and Lippe, 2009), basing IR systems on such

approaches will have fundamental shortcomings.

Among the basic deficiencies that need to be dealt

with here, is the lack of ability to express the

linguistic based queries made by humans in a formal

way needed for machine interpretation and

processing. Another and probably more fundamental

problem relates to identifying suitable ways for

representation and inference of concepts and the

context in which they appear. In machines, the

concepts need to be precisely defined, leading to

lack of generalization that in turn causes the number

of cases that need to be dealt with increase rapidly.

4 FUZZY PROXIMITY

NETWORK SCHEME

Within a cooperative environment, an intelligent

system can be built upon the collaborative nature of

IMPROVING COOPERATIVE MANAGEMENT - Fuzzy Modeling and Proximity Networks

Figure 1: Formation of fuzzy proximity network (FPN).

the queries by noting the implicit connection

between the individuals. One of the main

applications of the awareness model of the user (or

its agent) is related to the use of the awareness level

terms as part of the query, resulting in an expanded

query. The intelligent information system will then

be able to elevate the awareness levels of the

individuals by pointing to them the data set items

they might have been missing otherwise.

As it will be shortly discussed, the Fuzzy

Proximity Network (FPN) performs the needed

aggregation. The network achieves the

representation of the fuzzy awareness engine for the

implementation of the multi-agent framework.

ICSOFT 2011 - 6th International Conference on Software and Data Technologies

Through computation with words and the use of

linguistic variables, the solutions need to manage the

inherent fuzziness in human queries, representation

of concepts and coordination, properly and

efficiently.

To address the lack of flexibility in representing

documents and queries, fuzzy systems that deal with

this type of problem for individual users have also

been studied and developed by several researchers.

In such approaches, a fuzzy set will represent each

keyword. The membership value of each piece of

information or document indicates its degree of

relevance to the fuzzy set denoted by the keyword.

In this way, it is easy to use linguistic qualifiers for

computing with words to help the information

retrieval process. While this can help in indexing

and the querying process, users can also employ it to

provide feedback information. Such information can

be used to evaluate the retrieval system and in turn

for evaluation of the awareness agent.

A scheme that is based on fuzzy proximity

networks in line with the work reported in (Shenoi,

1989) can be utilized to build the required intelligent

system. The network is capable of providing

coordination services for cooperating agents. It can

also conveniently take the technicians’ awareness

levels and profiles into account while processing

their queries. The coordinator role and its agent can

evaluate and aggregate the queries from individual

agents to help the cooperating agents in achieving

their common goal. One important aspect of such

coordination relates to connecting the cooperating

agents by pointing information and documents

relevant to their task, even when one agent has not

asked for them. To achieve this, the system needs to

be able to process queries from different cooperating

users as collaborative queries. In this case, each

node i of the fuzzy proximity network represents a

keyword. The weight w(i, j) represents the fuzzy

relevance of the two keywords at nodes i and j. Such

a scheme does emphasize the keyword structures

and connections, rather than focusing on the

keywords themselves. The relevance between the

keywords is based on the co-occurrence of a

keyword or the so-called Miyamoto’s measure,

similar to what is reported in (Miyamoto, 1983).

Stated simply, this measure implies that the more

often two keywords occur simultaneously, the higher

is their relevance to one another.

Consider a fuzzy proximity scheme, partially

shown in Figure1. Here, as in any case of practical

importance, the pieces of information are in several

documents, including a document d denoted by D

(d), where the k

keyword in d is represented by

K(d, k). The keywords within any given document

are considered to be related to each other. For

instance, keywords K (1, 1), K (1, 2), … K (1, m) are

considered to be related, as they appear within the

same document, D (1). The fuzzy relevance of

keywords is represented by the weight w between

their respective nodes. For example, here the fuzzy

relevance between the two keywords K (1, 1) and K

(1, 2) is represented by the weight w (K (1, 1), K (1,

2)). In accordance with the co-occurrence concepts,

if document D (1) refers to another piece of

information in D (2) or is referred to by the

information content of D (3), then the keywords K

(2, 1), K (2, 2), … K (2, n) as well as the keywords K

(3, 1), K (3, 2), … K (3, p) are also considered to be

related to each other, although in a weaker sense.

This type of information will establish the initial

setting of weights in the network model. Obviously,

after this initial stage, the weights can be updated

through adaptive mechanisms and supervised

learning.

For each document, its characterizing attributes

are calculated based on a maximum spanning tree,

see (Sun, 1990). Here, as in several other

applications, a spanning tree is the tree that covers a

given set of nodes, i.e. keywords. The weight of the

tree W (.), is the sum of the weights of the branches

in that tree. A maximum spanning tree is established

as the tree with the maximum weight for a particular

set of nodes. Given a query Q (q), its maximum

spanning tree weight W (Q), is used as the

characterizing measure of the query. The weight of

the maximum spanning tree for the keywords

common between Q (q) and a document D (d)

divided by W (Q) is used to represent the

characterizing attribute measure R (.), of document

D (d) with regard to Q (q). These characterizing

attributes calculated for all of the documents, are

then used for ranking the documents with regard to

their relevance to the query Q (q).

In summary, each human role in a cooperative

management environment is supported by a software

agent that assists the process to collaboration by

helping realize the right level of awareness at the

right time for each collaborating role. Although

conceptually one could use many different

paradigms of artificial intelligence (e.g., case based

reasoning, model-based reasoning, fuzzy logic etc),

this paper discusses the design of an awareness

agent based on fuzzy logic. It is possible to use a

number of ways to involve fuzzy logic in the design

of such systems, as discussed in (Shahrestani, 2005).

Here, the Fuzzy Proximity Network (FPN) has

provided us with a simple example that illustrates

IMPROVING COOPERATIVE MANAGEMENT - Fuzzy Modeling and Proximity Networks

the role of fuzzy logic in the practical deployment of

awareness model in any cooperative information

system design.

Additionally, the previously established levels of

awareness for different individuals involved in a

project are used in conjunction with their queries to

form a joint index set. These can be considered as

the virtually combined queries from several

collaborating individuals. They form the basis for

the retrieval of several inter-related pieces of

information that improve the awareness levels of all

group members cooperating to achieve a common

goal. It is worth noting that the virtual joint query is

not formed through a union of the keywords used in

the queries of the individuals. The joint query is

rather based on reflection of combination of the

keywords, structure and the supposed awareness

levels of the involved individuals. They account for

the connection of keywords that are linked together

to form a structured concept. This is achieved by

using the characterizing features based on the

maximum spanning trees. Given that the information

from various collaborators are being combined, the

amalgamation of the keywords, rather than

emphasizing on the keywords themselves, is highly

beneficial.

5 CONCLUSIONS

This paper has discussed fuzzy awareness modelling

as part of an efficient cooperative management

design framework. In the proposed framework,

higher levels of cooperation are facilitated through

collaborative joint queries. These queries result in

higher awareness levels for the combined roles of all

individuals involved in a given task The framework

is based on multiple agents, where each human role

is supported by an agent. The development of multi-

agent cooperative management systems is based on

the notions of fuzzy logic and processing of

linguistic variables. The development and

implementation of an FPN for management

information retrieval was used as an illustrative

example.

REFERENCES

D. Bakser, D. Georgakopoulos, H. Schuster and

A.Cichocki, 2002. Awareness Provisioning in

Collaboration Management. In Int. Journal of

Cooperative Information Systems, vol. 11.

J. Grudin, J., 1994. Computer supported cooperative work:

history and focus. In IEEE Computer Magazine.

Lim, E., Tam, H., Wong, S., Liu, J., and Lee, R., 2009.

Collaborative content and user-based web ontology

learning system. In Proceedings IEEE International

Conference on Fuzzy Systems, FUZZ-IEEE 2009.

Wang, Q., Cheng, L., 2004. A flexible awareness

measurement and management architecture for

adaptive applications. In Proc. Global

Telecommunications Conference.

Huang, Y., Chen, J., Kuo, Y., and Jeng, Y., 2008. An

intelligent human-expert forum system based on fuzzy

information retrieval technique. In Expert Systems

with Applications, 34 (1).

Shahrestani, S., 2008. Utilization of Soft Computing to

Improve Cooperative Management Efficiency. In

WSEAS Transactions on Circuits and Systems, 7 (7).

Shahrestani,S., 2001. Fuzzy logic and integrated network

management. In Proceedings Fuzzy Logic and the

Internet-FLINT.

Zadeh, L. A., 1965. Fuzzy sets. In Information and

Control, vol. 8.

Zadeh, L. A., 1994. Soft computing and fuzzy logic. In

IEEE Software, vol. 11, no. 6.

Shahrestani, S., 2003. Fuzzy detection and integrated

network management. In Enhancing the power of the

Internet: Studies in Fuzziness and Soft Computing,

Editors: M. Nikravesh, L. A. Zadeh, B. Azvine, and R.

Yagar, Springer.

Mamdani, E., 1977. Application of fuzzy logic to

approximate reasoning using linguistic synthesis. In

IEEE Trans. Computers, vol. 26.

Umar, L., 1997. Client-Server Object-Oriented Internet

Environments, Prentice Hall.

Horng, Y., Chen, S., Chang, Y., and Lee, C., 2008. A new

method for fuzzy information retrieval based on fuzzy

hierarchical clustering and fuzzy inference techniques.

In IEEE Trans on Fuzzy Systems,” 13 (2).

Shirvanian, M. and Lippe, W., 2009. Efficiency increase

of fuzzy query languages by using indexes for selected

operations. In Proceedings IEEE International

Conference on Fuzzy Systems, FUZZ-IEEE 2009.

Shenoi, S., and Melton, A., 1989. Proximity relations in

the fuzzy relational database model. In Fuzzy Sets and

Systems, vol. 31.

Miyamoto, S. S., Miyake, T. and Nakayama, K., 1983.

Generation of a pseudo-thesaurus for information

retrieval based on co-occurrences and fuzzy set

operation. In IEEE Trans. On Systems, Man, and

Cybernetics, vol. 13, no 1.

Sun, C., 1990. An information retrieval model for

coordination systems based on fuzzy proximity

networks. In Proc. IEEE International Conference on

Systems, Man and Cybernetic.

Shahrestani, S., 2005. Exploiting Structure and Concepts

for Enhanced Management of Information and

Networked Systems. In World Scientific and

Engineering Academy and Society Transactions on

Computers, issue7, vol. 4.

ICSOFT 2011 - 6th International Conference on Software and Data Technologies