A PROTOPTYPE TO RECOMMEND TRUSTWORTHY

KNOWLEDGE IN COMMUNITIES OF PRACTICE

Juan Pablo Soto, Aurora Vizcaíno, Javier Portillo-Rodríguez, Mario Piattini

University of Castilla-La Mancha, Alarcos Research Group – Institute of Information Technologies & Systems

Department of Information Technologies & Systems - Escuela Superior de Informática, Ciudad Real, Spain

Oscar M. Rodríguez-Elias

University of Sonora, Department of Mathematics, Hermosillo, Mexico

Keywords: Knowledge Management, Recommendation, multi-agent Systems.

Abstract: Knowledge Management is a key factor in companies which have, therefore, started using strategies and

systems to take advantage of its intellectual capital. However, employees frequently do not take advantage

of the means to manage knowledge that companies offer them. For instance, employees often complain that

knowledge management systems overload them with more work since they have to introduce information

into these systems, or that this kind of tools floods them with too much knowledge which is not always

relevant to them. In order to avoid these problems we have implemented a tool to recommend trustworthy

knowledge sources in communities of practice. This tool is based on a multi-agent architecture in

which agents attempt to help users to find the information which is most relevant to them. In order to do

this, the agents use a trust model to evaluate how trustworthy a knowledge source (which may even be

another agent) is.

1 INTRODUCTION

In recent years Knowledge Management (KM) has

become an important success factor for companies.

The purpose of knowledge management is to help

companies to create, share and use knowledge more

effectively (Davenport and Prusak, 1997).

Information technologies play a key role in

achieving these goals but are only a small

component of an overall system that must integrate

the supporting technology with people-based

business processes. KM is not a technology solution

but rather is primarily about people oriented process,

such as leadership, culture, expertise and learning,

with technology playing a supporting role. Based on

this idea we have studied how people obtain and

increase their knowledge in their daily work. From

this study we have realized that frequently,

employees exchange knowledge with people who

work on similar topics and consequently, either

formally or informally, communities are created

which can be called “communities of practice”, by

which we mean groups of people with a common

interest where each member contributes knowledge

about a common domain (Wenger, 1998).

Communities of practice (CoPs) enable their

members to benefit from each other’s knowledge.

This knowledge resides not only in people’s minds

but also in the interaction between people and

documents. CoPs share values, beliefs, and ways of

doing things. Many companies report that such

communities help reduce problems due to lack of

communication, and save time by “working smarter”

(Wenger et al, 2002). An interesting fact is that

members of a community are frequently more likely

to use knowledge built by their community team

members than those created by members outside

their group (Desouza et al, 2006). Because of this, as

is claimed in (Desouza et al, 2006), knowledge reuse

tends to be restricted within groups. Therefore,

people, in real life in general and in companies in

particular, prefer to exchange knowledge with

“trustworthy people” by which we mean people they

trust. For these reasons we consider important the

implementation of mechanism in charge of

321

Pablo Soto J., Vizcaíno A., Portillo-Rodríguez J., Piattini M. and M. Rodríguez-Elias O. (2008).

A PROTOPTYPE TO RECOMMEND TRUSTWORTHY KNOWLEDGE IN COMMUNITIES OF PRACTICE.

In Proceedings of the Third International Conference on Software and Data Technologies - PL/DPS/KE, pages 321-326

DOI: 10.5220/0001887103210326

 SciTePress

measuring and controlling the confidence level in a

community where the members sharing information.

Bearing in mind that people exchange

information with “trustworthy knowledge sources”

we have designed a prototype in which software

agents try to emulate humans evaluating knowledge

sources with the goal of fostering the use of

knowledge bases in companies where agents provide

“trustworthy knowledge” to the employees.

The remainder of this work is organized as

follows: Section 2 describes the design of a multi-

agent system to recommend CoPs’ members

trustworthy knowledge sources. Then in Section 3

the prototype of the system is presented, in this

section the trust model that we propose to be used in

CoPs is also explained. After that in Section 4 the

preliminary evaluation of the prototype is shown.

Section 5 outlined related works and finally in

Section 6 conclusions are future work are

summarized.

2 DESIGN OF THE

MULTI-AGENT SYSTEM

Due to the importance of knowledge management,

tools which support some of the tasks related to KM

have been developed. Different techniques are used

to implement these tools. One of them, which is

proving to be quite useful, is that of intelligent

agents (van-Elst et al., 2003). Software agent

technology can monitor and coordinate events,

meetings and disseminate information

(Balasubramanian et al., 2001). Furthermore, agents

are proactive in the sense that they can take the

initiative and achieve their own goals. The

autonomous behaviour of these agents is critical to

the goal of this research since agents can act on

behalf of their users by carrying out difficult and

often time-consuming tasks that employees have to

perform when using a KM system. Most agents

today employ some type of artificial intelligence

technique to assist the users with their computer-

related tasks, such as reading e-mails, maintaining a

calendar, and filtering information. The advantages

that agent technology has shown in the area of

information management have encouraged us to

consider agents as a suitable technique by which to

develop a multi-agent system with the goal of

supporting CoPs. To do this, we need to emulate

people’s behavior when they interact with the other

members of a community. For this reason, we have

grouped the agents into communities, thus

attempting to emulate CoPs. Figure 1 represents the

distribution of the multi-agent system where there

are two kinds of agent and where there are different

roles to play.

Figure 1: Multi-Agent System.

One type of agent is the User Agent which is in

charge of representing each person that may consult

or introduce knowledge in a community. The User

Agent can assume three types of behavior or roles

similar to the tasks that a person may carry out in

his/her community. Therefore, the User Agent plays

one role or another depending upon whether the

person that it represents carries out one of the

following actions:

 The person contributes new knowledge to the

communities in which s/he is registered. In this

case the User Agent plays the role of Provider.

 The person uses knowledge previously stored in

the community. Then, the User Agent will be

considered as a Consumer.

 The person helps other users to achieve their

goals, for instance by giving an evaluation of

certain knowledge. In this case the role is that of

a Partner. So, Figure 1 shows that in

Community 1 there are two User Agents

playing the role of Partner (Pa), one User Agent

playing the role of Consumer (Co) and another

being a Provider (Pr).

The second type of agent within a community is

called the Manager Agent (represented in black in

Figure 1) which is in charge of managing and

controlling its community.

Every user agent has been constructed by

following the multi-agent architecture explained in

(Soto et al, 2007), in which the authors present a

three level architecture to support CoPs.

The multi-agent system has been designed by

using the INGENIAS (Pavón and Gómez-Sanz,

2003) methodology because this is considered by

many authors to be one of the most up to date and

complete methodologies.

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3 THE PROTOTYPE

In order to test our multi-agent system, we have

developed a prototype system into which CoPs

members can introduce documents and where these

documents can also be consulted by other people.

The goal of software agents is that of helping

members to discover the information that may be

useful to the CoPs members, thus decreasing the

overload of information which, for instance,

employees often have and strengthening the use of

knowledge in enterprises. This prototype also helps

to discover experts in a community and permits the

detection of fraud when users insert non-valuable

knowledge into the community.

One feature of this system is that when a person

searches for knowledge in a community, and after

having used the knowledge obtained, that person

then has to evaluate the knowledge in order to

indicate whether:

- The knowledge was useful.

- How it was related to the topic of the search.

In this paper, due to space limitations, we shall

only explain how agents recommend documents

when a person is searching for information about a

topic.

In order to make it easier to search for documents

in a community, users can choose one topic from

those which are available in the community and the

user agent will attempt to find documents about this

topic.

The general idea is to consider those documents

which come from trustworthy knowledge sources

according to the user’s opinion or needs. User agents

use a trust model to discover which knowledge

sources are trustworthy. As this trust model will be

used in CoPs then the factors that arise in this kind

of community should be considered, such as:

The number of interactions that an agent will

have with other agents in the community will be low

in comparison with other scenarios such as auctions.

This is very important because we cannot use trust

models which need a lot of interactions to obtain a

reliable trust value; it is more important to obtain a

reliable initial trust value. We use four factors (see

Figure 2) to obtain a trust value:

 Position: employees often consider information

that comes from a boss as being more reliable

than that which comes from another employee

in the same (or a lower) position as him/her

(Wasserman and Glaskiewics, 1994). However,

this is not a universal truth and depends on the

situation. For instance in a collaborative

learning setting collaboration is more likely to

occur between people of a similar status than

between a boss and his/her employee or

between a teacher and pupils (Dillenbourg,

1999). Such different positions inevitably

influence the way in which knowledge is

acquired, diffused and eventually transformed

within the local area. Because of this, as will

later be explained, this factor will be calculated

in our research by taking into account a weight

that can strengthen this factor to a greater or to a

lesser degree.

 Expertise: This term can be briefly defined as

the skill or knowledge that a person who knows

a great deal about a specific thing has. This is an

important factor since people often trust experts

more than novice employees. In addition,

“individual” level knowledge is embedded in

the skills and competencies of the researchers,

experts, and professionals working in the

organization (Nonaka and Takeuchi, 1995). The

level of expertise that a person has in a

company or in a CoP could be calculated from

his/her CV or by considering the amount of time

that a person has been working on a topic. This

is data that most companies are presumed to

have.

 Previous Experience: This is a critical factor in

rating a trust value since, as was mentioned in

the definitions of trust and reputation, previous

experience is the key value through which to

obtain a precise trust value. However, when

previous experience is scarce or it does not exist

humans use other factors to decide whether or

not to trust in a person or a knowledge source.

One of these factors is intuition.



Intuition: This is a subjective factor which,

according to our study of the state of the art, has

not been considered in previous trust models.

However, this concept is very important

because when people do not have any previous

experience they often use their “intuition” to

decide whether or not they are going to trust

something. We have tried to model intuition

according to the similarity between personal

profiles: the greater the similarity between one

person and another, the greater the level of trust

in this person as a result of intuition.

A PROTOPTYPE TO RECOMMEND TRUSTWORTHY KNOWLEDGE IN COMMUNITIES OF PRACTICE

323

Figure 2: Trust Model.

We have classified these four factors into two

groups: objective factors (position and expertise)

and subjective factors (intuition and previous

experience). The former is given by the company or

community and the latter depends on the agent itself

and the agent’s experience in time. There are four

different ways of using these factors, which depend

upon the agent’s situation.

 If the agent has no previous experience, for

instance because it is a new user in the

community, then the agent uses position,

expertise and intuition to obtain an initial trust

value and this value is used to discover which

other agents it can trust.

 When the agent has previous experience

obtained through interactions with other agents

but this previous experience is low (low number

of interactions), the agent calculates the trust

value by considering the intuition value and the

experience value. For instance, if an agent A has

a high experience value for agent B but agent A

has a low intuition value for agent B (profiles

are not very similar), then agent A reduces the

value obtained through experience. In this case

the agent does not use position and expertise

factors (objective factors) because the agent has

its own experience and this experience is

adjusted with its intuition which is subjective

and more personalized.

 When the agent has enough previous experience

to consider that the trust value it has obtained is

reliable, then the agent only considers this

value.

The way to translate the trust model to trust values is

by using the following formula:

= w

+ (∑ QC

)/n

where T

is the value of trust of j in the eyes of i, E

is the value of expertise which is calculated

according to the degree of experience that the person

upon whose behalf the agent acts has in a domain.

is the value assigned to a person’s position.

denotes the intuition value that agent i has in

the agent j which is calculated by comparing each of

the users' profiles.

In addition, previous experience should also be

calculated. When an agent i consults information

from another agent j, the agent i should evaluate

how useful this information was. This value is called

(Quality of j’s Contribution in the opinion of i).

To attain the average value of an agent’s

contribution, we calculate the sum of all the values

assigned to these contributions and we divide it

between their total. In the expression n represents

the total number of evaluated contributions.

Finally, w

, w

and w

are weights with which the

trust value can be adjusted according to the degree

of knowledge that one agent has about another.

Therefore, if an agent i has had frequent interactions

with another agent j, then agent i will give a low

weight (or even zero) to w

since, in this case,

previous experience is more important than intuition.

The same may occur with w

, w

So the weights

may have the value of 0 or 1 depending on the

previous experience that an agent has.

In order to illustrate how the prototype works, let

us look at an example. If a user selects a topic and

wants to search for documents related to this topic

his/her user agent will contact other user agents

which have documents about the topic, and the user

agent will then calculate the trust value for each

agent, which means that these agents are considered

to be knowledge sources and the user agent needs to

calculate which “knowledge source” is more

trustworthy. Once these values have been calculated,

the user agent only shows his/her user the

documents which have come from the most

trustworthy agents. In Figure 3 we can see the

results of a search sorted by the trust values, that is,

the first documents on the list come from the most

trustworthy knowledge sources (in this case the most

trustworthy agents with the highest trust values).

There are other possibilities, depending on user

preferences. For instance, as we can see in Figure 3,

the results of the request (sorted by reputation) show

a large amount of results, and the first one on the list

has five stars in the reputation level and four shields

in the position level.

This method of rating trust helps to detect an

increasing problem in companies or communities in

which employees are rewarded if they contribute

with knowledge in the community. Thus, if a person

introduces non-valuable documents with the sole

aim of obtaining rewards, the situation can be

detected since these documents will have low values

and the person will also be considered to be less

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324

Figure 3: Showing and sorting results.

trustworthy. The agent will, therefore, not

recommend those documents. Moreover, this model

implies the reduction of users’ overload when they

use knowledge management systems, since with this

model the user agent only recommends the most

adequate and trustworthy knowledge.

4 EVALUATION OF THE

PROTOTYPE

Once the prototype has been finished we have

evaluated it. To do this, different approaches can be

followed, from a multi-agent point of view or from a

social one. First of all we have focused on the

former and we are testing the most suitable number

of agents advisable for a community. Therefore,

several simulations have been performed. As result

of them we found that:

The maximum number of agents supported by the

Community Manager Agent when it receives User

Agents’ evaluations is approximately 800. When we

tried to work with 1000 agents for instance, the

messages were not managed conveniently. However,

we could see that the Manager Agent could support

a high number of petitions, at least, using simpler

behavior.

All these results are being used to detect whether

the exchange of messages between the agents is

suitable, and to see if the information that we

propose to be taken into account to obtain a

trustworthy value of the reputation of each agent is

enough, or if more parameters should be considered.

5 RELATED WORKS

This research can be compared with other proposals

that use agents and trust in knowledge exchange.

With regard to Trust, in models such as eBay

(1995) and Amazon (1996), which were proposed to

resolve specific situations in online commerce, the

ratings are stored centrally and the reputation value

is computed as the sum of those ratings over six

months. Thus, reputation in these models is a single

global value. However, these models are too simple

(in terms of their trust values and the way in which

they are aggregated) to be applied in open multi-

agent systems. For instance, in (Zachaira et al, 1999)

the authors present the Sporas model, a reputation

mechanism for loosely connected online

communities where, among other features, new

users start with a minimum reputation value, the

reputation value of a user never falls below the

reputation of a new user and users with very high

reputation values experience much smaller rating

changes after each update. The problem with this

approach is that when somebody has a high

reputation value it is difficult to change this

reputation, or the system needs a high amount of

interactions. A further approach of the Sporas

authors is Histos which is a more personalized

system than Sporas and is orientated towards highly

connected online communities. In (Sabater and

Sierra, 2002) the authors present another reputation

model called REGRET in which the reputation

values depend on time: the most recent rates are

more important than previous rates. Carbó et al

(2003) presents the AFRAS model, which is based

on Sporas but uses fuzzy logic. The authors present

a complex computing reputation mechanism which

handles reputation as a fuzzy set while decision

making is inspired in a cognitive human-like

approach. In (Caballero et al, 2006) the authors

present a trust and reputation model that considers

trust and reputation as emergent properties of direct

interactions between agents, based on multiple

interactions between two parties. In this model, trust

is a belief an agent has about the performance of the

other party to solve a given task, according to own

knowledge.

The main differences between these reputation

models and our approach are that these models need

an initial number of interactions to obtain a good

reputation value and it is not possible to use them to

discover whether or not a new user can be trusted. A

further difference is that our approach is orientated

towards collaboration between users in CoPs. Other

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325

approaches are more orientated towards competition,

and most of them are tested in auctions.

6 CONCLUSIONS AND FUTURE

WORK

This paper describes a multi-agent prototype to

support CoPs in which knowledge source are rated

by using a trust model developed to be used solely in

CoPS. In this prototype CoPs members can

introduce documents and the software agents must

decide how trustworthy those documents are for the

user that they represent.

One important contribution of this paper is the

trust model, as it helps to detect experts in a

community, since those knowledge sources with

high trust values are supposed to be people who

contribute with valuable knowledge. The trust model

also helps to detect fraud when users contribute with

non-valuable knowledge. Another important feature

of our trust model, and that which makes it different

from previous models, is that even when a user is

new to the community and other agents do not have

any previous experience of working with him/her,

the trust model allows agents to obtain a preliminary

trust value by considering other factors such as the

new agent’s position, and level of expertise, along

with the intuition that each agent has about the new

member. In this way we attempt to model human

features, since when a person has to evaluate

something and s/he has no previous experience this

person uses other aspects such as his/her intuition in

order to decide whether or not to trust in it.

As future work, we are performing different tests

with the prototype and the trust model in order to see

how they might be improved according to different

domains.

ACKNOWLEDGEMENTS

This work is partially supported by the MELISA

project (PAC08-0142-3315), Junta de Comunidades

de Castilla-La Mancha, Consejería de Educación y

Ciencia, in Spain, partially supported by MECENAS

(PBI06-0024) and ESFINGE project (TIN2006-

15175-C05-05) Ministerio de Educación y Ciencia

(Dirección General de Investigación)/Fondos

Europeos de Desarrollo Regional (FEDER) in Spain

and CONACYT (México) under grant of the

scholarship 206147 provided to the first author.

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