USING AGENTS TO CONFRONT SOME OF THE CHALLENGES

OF KNOWLEDGE MANAGEMENT SYSTEMS

Javier Portillo-Rodríguez

, Aurora Vizcaíno

, Juan Pablo Soto

and Mario Piattini

ALARCOS Research Group, Information Systems and Technologies Department

UCLM-INDRA Research and Development Institute, Escuela de Informática, Universidad de Castilla-La Mancha

Paseo de la Universidad 4 - 13071 Ciudad Real, Spain

Mathematics Department, University of Sonora

Blvd. Luis Encinas y Rosales s/n, Col. Centro, CP. 83000, Hermosillo, México

Keywords: Agent architecture, Knowledge management systems, Communities of practice, Trust models.

Abstract: The importance of knowledge management has, in recent years, led to the incorporation of Knowledge

Management Systems (KMS) into companies. Some of these KMS could be considered as Recommender

Systems that are able to recommend knowledge, which is part of the company’s intellectual capital.

However, these KMS are not always welcome in the company, since the knowledge is not stored by using a

quality control, or because employees feel that these kinds of systems, rather then helping them, cause them

extra work. In this paper we present an agent architecture combined with a trust algorithm trying to avoid

some of the problems that appear when a KMS is introduced into companies.

1 INTRODUCTION

In recent years, knowledge has become an extremely

important factor (Hansen and Kautz, 2004). Subjects

such as Knowledge Management are, therefore,

currently of particular interest to organizations who

are concerned about their employees’ learning and

competitiveness, since a suitable management of

knowledge can help them to increase their members’

collaboration and encourage them to share

knowledge. At present organizations must operate in

a climate of rapid market change and high

information volume, which increases the necessity to

create knowledge management systems (KMS) that

support the knowledge process. It is possible to

consider certain Recommender Systems as KMS,

however, these kinds of systems are not always

welcomed by a company’s employees because

(Lawton, 2001) on occasions employees waste a

considerable amount of time searching for

information, with regard to this, sometimes there is

no quality control with regard to the KOs

(Knowledge Objects) introduced into the system and

employees may introduce information into the

systems which is not very valuable.

Our work is focused on attempting to reduce the

impact of these problems. We therefore use software

agents to search for information on behalf of users,

and these agents are in charge of recommending the

most suitable knowledge to them.

We pretend to use our proposal in Communities

of Practice (CoPs) which are a natural means of

sharing knowledge, which is considered to be a

critical factor for an organization’s competitive

advantage (Hansen and Kautz, 2004).

However, nowadays, these kind of communities,

due to globalization, are geographically distributed

and there are no face-to-face interactions. If CoP

members are distributed and they do not know the

other members trust between CoP members

decrease. This situation could be a problem because

people in general prefer to exchange knowledge with

“trustworthy people” and if there is not enough trust

among members knowledge exchange could

decrease too. People with a consistently low

reputation will eventually be isolated from the

community since others will rarely accept their

justifications or arguments and will limit their

interactions with them. This issue, plus the problems

pointed out previously, have led us to develop an

agent architecture and a recommendation algorithm

249

Portillo-Rodríguez J., Vizcaíno A., Pablo Soto J. and Piattini M. (2010).

USING AGENTS TO CONFRONT SOME OF THE CHALLENGES OF KNOWLEDGE MANAGEMENT SYSTEMS.

In Proceedings of the 2nd International Conference on Agents and Artiﬁcial Intelligence - Agents, pages 249-252

DOI: 10.5220/0002699202490252

 SciTePress

to encourage the reuse of knowledge in CoPs. In

order to tackle these problems, we have developed

an agent architecture and a trust algorithm with

which to rate KOs and Knowledge Sources (KSs)

that produce these KOs. The software agents will

therefore use this algorithm in order to decide

whether a KO or KS should be recommended to a

particular user.

Therefore in Section 2 the agent architecture is

described and later, in Section 3, a recommender

system and the recommender algorithm used by this

system is explained. . Finally, our conclusions are

outlined in Section 4.

2 AN AGENT ARCHITECTURE

The agent architecture proposed is composed of two

levels: reactive and deliberative-social. The reactive

level is considered by other authors to be a typical

level that an Agent Architecture must have (Ushida,

1998). A deliberative level is often also considered

as a typical level, but a social level is not often

considered in an explicit manner, despite the fact

that these systems (MAS) are composed of several

individuals, the interactions between them and the

plans constructed by them. The social level is only

considered in those systems that attempt to simulate

social behaviour. Since we wish to emulate human

feelings such as trust when working in CoPs, we

have added a social-deliberative level that considers

the social aspects of a community and which takes

into account the opinions and behaviour of each of

the members of that community.

Each of these levels is explained in greater detail

in the following sub-sections.

2.1 Reactive Level

This is the level in charge of perceiving changes in

its environment and responding to these changes at

the precise moment at which they occur, i.e., when

an agent executes another agent’s request without

any type of reasoning.

The components of the reactive level are (see

Figure 1):

Internal Model. This component stores the

individuals’ features. These features will be

consulted by other agents in order to discover more

about the person represented by the User Agent

Beliefs. This module is composed of inherited

beliefs (pre-defined beliefs) and lessons learned

(obtained by interaction with the environment) from

the agent itself.

Figure 1: Reactive Level.

Interests. These are a special kind of beliefs. This

component represents individual interests that an

agent has with regard to a topic or a knowledge

source.

Behaviour Generator. This component is

fundamental to our architecture. It is here that the

actions to be executed by the agent are triggered.

Depending on the information received from the

Interpeter module the agent makes a matching

process to select the correspondent behaviour.

2.2 Deliberative-Social Level

At this level, the agent has a type of behaviour

which is oriented towards objectives, that is, it takes

the initiative in order to plan its performance with

the purpose of attaining its goals.

The components of the deliberative-social level

are (see Figure 2):

Figure 2: Deliberative-Social Level.

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Goals Generator. Depending on the state of the

agent, this module must decide what the most

important goal to be achieved is.

Social Beliefs. This component represents a view

that the agent has of the communities and their

members, for instance, beliefs about other agents.

Social Interests. This is a special type of belief. In

this case it represents interest in other agents.

Intuitions. We often trust more in people who have

similar features to our own. Thus, in this research,

intuition has been modelled according to the

similarity between agents’ profiles: the greater the

similarity between one agent and another, the greater

the level of trust. The agents’ profiles may change

according to the community in which they are

working. This factor will be used in those cases

when the agent doesn’t have enough information to

know if a KS is trustworthy.

Plan Generator. This component is in charge of

evaluating how a goal can be attained, and which

plans are most appropriate to achieve this.

Trust Generator. This module is in charge of

generating a trust value for the knowledge sources

with which an agent interacts in the community. To

do this, the trust generator module considers the trust

model explained in detail in (Soto et al, 2007) which

considers the information obtained from the internal

model and the agent’s intuitions.

3 A RECOMMENDER SYSTEM

A recommender system has been developed in order

to test the trust model and the multi-agent

architecture. In this system each CoP member is

represented by a software agent called a User Agent.

A new community member must first join a

community, which is done by using the “Register”

menu and choosing a community from those which

are available. Once registered, a member can

provide new KOs or use those which are already

available in the community and/or propose new

subjects. One way to obtain KOs in a community is

requesting a KO recommendation. To obtain a KO

recommendation user has to use the “Recommend”

menu and select a topic. To make the

recommendation, the prototype will use a

recommendation algorithm that has been design as

follows.

The input the algorithm is a set of KOs. Each

KO may or may not have been evaluated previously,

signifying that a KO may already have a list of

evaluations (along with the identity of each person

who evaluated it), or it may not have any

evaluations. This aspect will be taken into account

by the algorithm, which therefore distinguishes two

groups:

Group 1 (G1): This group is formed of the KOs

that have already been evaluated. This is the most

important group since if the agents have previous

evaluations of a KO they have more information

about it, which facilitates the task of discovering

whether or not its recommendation is advisable.

Group 2 (G2): these KOs have not been used

previously so the agents do not have any previous

evaluations of them. Let us now observe how each

group is processed by the algorithm.

In G1 the KOs will be ordered by a

Recommendation Rate which is calculated by the

User Agent for each KO. Hence RR

signifies the

Recommendation Rate for a particular KO called k,

and is obtained from:

(1)

where TE

is the pondered mean of the evaluations

determined by the trust that an agent “i” has in each

evaluator (the person who has previously evaluated

that KO). TE

is calculated as:

(2)

Therefore, TS

is the trust value that the User

Agent “i” has in the knowledge source “j”, since in a

CoP the source which provides a KO will usually be

a CoP member. TS

therefore represents the trust

that an agent “i” has in another agent “j” and E

the evaluation that an agent “j” has made with regard

to a particular KO “k”.

The parameter TS

used in Formula (1) similarly

indicates the trust that an agent “i” has in a

knowledge source “k”. In other words, the agent

must take two things into consideration when

calculating the RR

 The other agents’ opinions of a KO “k”

pondered by the trust that agent “i” has in the

person who provided that evaluation.

 The opinion that the agent “i” has in the agent

that has provided the KO “k”.

Both w1 and w2 are weights which are used to

adjust the formula. The sum of w1 and w2 should be

Group 2 will use another formula to calculate the

for each KO since, in this case, there are no

USING AGENTS TO CONFRONT SOME OF THE CHALLENGES OF KNOWLEDGE MANAGEMENT SYSTEMS

251

results of previous evaluations of the KOs. This

formula, not explained due to space problems,

basically uses a pondered mean of the trust values

that other agents have about the KS.

4 CONCLUSIONS

CoPs are a means of knowledge sharing. However,

the knowledge that is reused should be valuable for

its members, who might otherwise prefer to ignore

the documents that a community has at its disposal.

In order to encourage the reuse of documents in

CoPs, in this work we propose a multi-agent

recommender system with which to suggest

trustworthy documents. Some of the advantages of

our system are:

 The use of agents to represent members of the

community helps members to avoid the problem

of information overload since the system gives

agents the ability to reason about the

trustworthiness of the other agents or about the

recommendation of the most suitable documents

to the members of the community. Users are

not, therefore, flooded with all the documents

that exist with regard to a particular subject, but

their agents filter them and recommend only

those which are most trustworthy (when they

have rates) or those which are provided by more

trustworthy sources or sources which have

preferences and features that are similar to those

of the user in question.

 The system can detect those users with the

greatest level of participation and those whose

documents have obtained higher rates. This

information can be used for two purposes:

expert detection and/or recognition of

fraudulent members who contribute with

worthless documents. Both functionalities imply

various advantages for any kind of organization,

i.e., the former permits the identification of

employee expertise and measures the quality of

their contributions, and the latter permits the

detection of fraud when users contribute with

non-valuable information.

 The system facilitates the exchange and reuse of

information, since the most suitable documents

are recommended. The tool can also be

understood as a knowledge flow enabler

(Rodríguez-Elias et al, 2007), which encourages

knowledge reuse in companies.

Furthermore, the proposed algorithm is quite

flexible since in many situations weights are used to

modify the formulas. This algorithm could,

therefore, be used by the designers of other

recommender systems who could decide what values

they should give to these weights in order to adapt

the formula to their needs.

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