Improving knowledge management by building intelligent agents using social network analysis systems

1. 2009 Fourth International Multi-Conference on Computing in the Global Information Technology
Improving Knowledge Handling by Building Intelligent
Systems Using Social Agent Modelling
Amjad Fayoumi, Hossam Faris, Francesca Grippa
eBusiness Management School – Scuola Superiore ISUFI, University of Salento,
via per Monteroni, sn 73100 Lecce, Italy
amjad.fayoumi@imebm.ebms.it, hossam.faris@ebms.unile.it, francesca.grippa@ebms.unile.it
help in providing tools and information to characterize the
Abstract—Any purposeful organization can be understood as a agents’ schema. Agent based modelling is the way to model
value network. The main goal of this network is to deliver the multi-agent systems, and agent based modelling systems. Its
highest value from the interdependencies between nodes.
main roots are in modelling human social and organizational
Improvement in this domain requires to increase efficiency,
response time, knowledge availability and knowledge storing. behaviour and individual decision making. With this, comes
One of the most interesting research topics in the field of multi- the need to represent social interaction, collaboration, group
agent systems is the definition of models with the aim of behaviour, and the emergence of higher order social structure.
representing social structures such as organizations and In applications of ABMS (agent-based modelling systems) to
coalitions, to control the emergent behaviour of open systems. social processes, agents represent people or groups of people,
This paper presents an approach to capture knowledge from and agent relationships represent processes of social
social environment by building new features in the social interaction. Agents can share knowledge using any agreed
network analysis systems and use this knowledge as a source for language; ontology will help to define common understanding
modelling multi-agent systems. This paper presents a different
approach to capture knowledge from social environment and
for terms and knowledge transferred and it will be used to
handle social aspects in intelligent analysis systems by developing define the ontological relations between the nodes to provide
and simulating agent’s behaviour. Those proposed methods will meaning to links between nodes. The ontology, as represented
help to represent knowledge in a new way as well as simulate and in Figure 1, will be able to connect Human Level, Agents
automate knowledge flow. Level and the level of the Social Network Analysis System
(SNAS), that is a set of tools to visualize and measure the
Keywords— Agent modelling, Knowledge Management, Value
relationships between actors. We will explain it in details in
network, Value Network Analysis, Social Network Analysis, the coming sections.
Multi Agent Systems
II. Structure of the Paper
I. Introduction To describe our research we will first introduce the main
Knowledge Management is considered an important domain theoretical background (section III), Then, we will present the
to improve firm’s performance and its processes; it is different blocks of our model: the importance of tracking the
important to support dynamic business models with the social environment where agents interact (section IV); the role
underlying knowledge, as the knowledge is usually distributed of social network analysis systems (section V) to conclude
and dispersed between people and in their minds, influenced with the presentation of the Agents Modelling platform
by their personalities and by the environment. The progress in (Section VI).
technology and methodology aims to find a method to handle
the whole knowledge (explicit and tacit). Many studies in
fields like Social Network Analysis (SNA) try to understand
how to optimize value network defined as a sets of roles,
interactions and relationships generating economic, social or
environmental value. SNA is a tool that enables companies to
map the information exchanges among employees and
determine how to support information brokers, gatekeepers
and boundary spanners, and to integrate isolated groups.
From a research point of view, it is important to find a map
between representing Multi agents and value networks, since
Multi-agent systems can be used to solve problems which are
difficult or impossible for an individual agent or monolithic
system to solve; and that’s exactly what is happening in value
network components and activities. Multi-agents system will
be used in our proposed model to model social structure.
Value network analysis (VNA) is a methodology for
understanding, using, visualizing, and optimizing internal and
external business value networks and complex economic
ecosystems. VNA and SNA (Social Network Analysis) will Figure 1. Research View
978-0-7695-3751-1/09 $25.00 © 2009 IEEE
$26.00 86
DOI 10.1109/ICCGI.2009.21

2. III. Theoretical Background To our knowledge, few literature exists for the overall use of
agent systems with SNA systems for modeling social structure
In social sciences there was a close connection between game and there are few papers which extend social network analysis
theory and the digital computer from early on in the post application to cover emotions, relations and intangible aspects.
World War II era. And continuing with developments in early This review will address the importance of capturing
behavioural economics researches, early models were so knowledge to simulate human behaviour in a complex
heavily constrained by limited computing technology that they economic environment; the increasing need for evaluating the
focused on two or at most a few individuals. effectiveness of value network components, and highlight the
MAS systems design can be inspired by human social existing approaches, Technology and Social Criteria will be
phenomena. Furthermore, by computationally modelling used for this research.
social phenomena we can provide a better understanding of
them. “Social” does not mean only organization, roles, The theoretical framework used in this project to describe the
communication and interaction protocols, norms (and other nature and evolution of communities is known as ‘complexity
forms of coordination and control); it should be taken also in science’. According to this approach organizational
terms of spontaneous orders and self-organising structures communities are viewed as “complex adaptive systems”
[18]. The modern conception of agents is often credited to (CAS): they co-evolve with the environment because of the
Schelling [21] and his model of urban segregation situated self-organizing behaviour of the agents determining fitness
several purposive-behaving individuals with explicit landscape of market opportunities and competitive dynamics.
behavioral rules on a spatial landscape and studied the typical A system is complex when equations that describe its progress
configurations of the model. An important contribution of this over time cannot be solved analytically. Understanding
work was his demonstration that a population of individuals, complex systems is a challenge faced by different scientific
none of whom prefers segregated outcomes to integrated ones, disciplines, from neuroscience and ecology to linguistics and
can nonetheless end up in segregationist configurations by geography.
virtue of system effects. Essentially, if agents of the same type
wish to have some fraction of their neighbours of their same
type, this leads to clustering of like-agents at the aggregate IV. Social Aspects
level, thus producing segregation despite the innocuous
preferences of the individuals. Social aspect is a first block in our proposed model. Social
environment will provide us with an infrastructure for agents
Today, studying agents is a rapidly growing research area in to interact productively. It contains the principles and
the social sciences as well as within computer science. A final processes that govern and support the interrelations resulting
dimension of the agent pedigree derives from research in from an agent’s association with other entities in the MAS
artificial life (ALife), often associated with the Santa Fe environment. And it provides those functions and structures
Institute [6]. This line of research, pursued both by computer necessary to member of a group or society. Studying sociality
scientists and biologists, seeks to create “life forms” within from different aspects individually and in groups is necessary
software through genetic, evolutionary and other means. to define methods for the analysis of behaviours, reactions,
Agent-based models in ecology derive much of their impetus social network impact, decision making and how it is
from this tradition. influenced by the sub-conscious and/or by the environment.
Agent-based models were primarily used for social systems Research across different fields (anthropology, sociology,
by Craig Reynolds, who tried to model the reality of living computer science) has contributed to understand how the
biological agents, known as artificial life, a term coined by entire set of networks in which actors are embedded interacts
Langton [22]. Reynolds introduced the notion of individual- and affects social and economic outcomes. Nevertheless, it is
based models, in which one investigates the global considered incomplete, in particular when related to
consequences of local interactions of members of a population knowledge network representation [2]. A lot of measures of
(e.g., plants and animals in ecosystems, vehicles in traffic, social network evolution has been developed and tested
people in crowds). In these models individual agents (possibly mainly on small networks (usually between 100 and 5000
heterogeneous) interact in a given environment according to nodes). There is still the need to further understand which of
procedural rules tuned by specific parameters. these measures continue to provide information, when the
In 1996 the first large scale agent model, the Sugarscape, network is composed of many nodes and ties [3].
has been introduced to simulate and explore the role of social
phenomena such as seasonal migrations, pollution, sexual Three important domains will help in understanding and
reproduction, combat, trade and transmission of disease and defining methods of analysing human and social aspects. First,
culture. the Neuro-Linguistic Programming (NLP) approach will
The Artificial Life community has been the first in insure to specify very detailed facts regarding people in social
developing agent-based models [15]; but since then agent- level mainly related to their natural and personal profile [16].
based simulations have become an important tool in other Second, the Sensemaking approach will help to study
scientific fields and in particular in the study of social systems. reactions, make sense of situations in complex ecosystems,
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3. stimulate behaviours and forecast decisions (possible 4- Using specific and sensitive technology systems such
interaction). The third approach is given by the set of Norms as wearable sensors, which have been used to
which defines organizational structure, behavioural rules, monitor and measure evolution of voices, level of
roles and groups. tension and movement [9] .
Those three domains explored at social level will provide us We can sort important criteria to help build teams in firms or
with the required concepts to study social network patterns, project teams by understanding the keys of action waves
social behaviour, emotional, sociological and psychological which will drive explanation and reasoning to how they feel,
factors on the top of the knowledge level to find the important think and act. Quilliam [17] detailed a method to describe
criteria in order to measure and evaluate the targeted criteria. people’s behavior, motivation, which is important to define
Then, we will work to evaluate the level of intelligence and people’s personalities. Table 1 represents people’s motivation
extend the current SNA system of software tools to cover criteria, we can imagine the importance of such an analysis in
more intelligence, emotional aspects and capture the trust building groups and networks in the most successful way to
between those nodes; all these factors are already contained in avoid knowledge gaps, information bottlenecks, low
the real social level (real nodes) and are captured by the tools motivation and to increase communication effectiveness in
already defined for measurement. This will help to transfer the work and society. It might also be used to specify consumers’
criteria (trust, emotion, relation) to analyse and design Multi motivation to provide them optimum services that can
Agent Systems by describing the structures and mechanisms stimulate their interest.
which may help decision making and/or simulation and
automation of the current or desired behaviours.
The main limitation of SNA is to be mainly a quantitative The Criteria The Opposite Criteria
social science method, ignoring sometimes the importance of
qualitative issues to explain phenomena. Its unit of analysis is Looking to General View Looking to details
not the single actor with its attributes, but the relations
between actors, defined identifying the pair of actors and the Prefer stability Prefer change
properties of the relation among them. By focusing mainly on Can Start things Can finish things
the relations, SNA might underestimate many organizational
elements which could influence the ability of an organization Initiative prospect Waiting prospect
to reach its goals. It does not measure how different actors’
Optimism thinking Pessimism thinking
attributes influence the network configuration. Furthermore,
perceptive measures are often ignored by SNA. What seems to Positive Stimulating Negative incentive
be missing in current SNA research is an approach to study
how the individual actors’ characteristics change the network Diastole Introversion
configuration and performance. The empirical work on
network information advantage is still “content agnostic” [14]. Extrovert person isolation person
As stated by Goodwin and Emirbayer [20], SNA globally Internal stimulating External stimulating
considered is a framework to investigate the information
structure of groups, the structural aspect of relationships,
Table 1. Example of criteria that may be measured to help build
disregarding the content of relationships, and the nodes’ effective networks
properties. Paying attention only to the structural facets of
community interactions is like considering all the ties as In many organizations nowadays, the automation of business
indistinguishable and homogeneous. In this perspective, actors processes is not enough to avoid inefficiencies and ensure
performing different activities, or involved in different performance. Successful organizations are recognizing the
projects, are detected simply as interacting members, with no need to integrate distributed work activities based on social
distinction among sub-categories that might change over time. and knowledge networks. Network management requires
searching for the right people and the most appropriate
Human behaviour and evolution can be measured by using
knowledge, dynamically monitor and evaluate the ability of
some of the following research methodologies: groups of people and their collective knowledge to achieve the
1- Administering questionnaires, which is the most predefined business goals [5].
widely used technique by social scientists. Jack Welch (1991) noticed that strict control and command
2- Observing people and monitoring specific behaviors management leaders might lead to lose their effectiveness,
to discover the changes in character/personality traits. simply because it shut down the emergent collective
intelligence and social networking of the employees [19]. In
3- Tracking communication among agents by accessing this perspective, we will try to demonstrate the need of
e-mail traffic. modeling social networks ensuring a representation of the
various ontological aspects. We will define nodes and their
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4. related knowledge that could be used to evaluate position,
privileges of accessing specific knowledge and power of the
node by writing algorithms that satisfy the knowledge criteria, 2- Ontology of communication terms and language to help
where each node will be weighted depending on business need share common understanding about the environment
and requirements. and messages between agents. An example is the
SBVR vocabulary and terms ontology definition: when
V. Social Network Analysis system agent A requests a service from agent B (e.g., request
for information), agent B will evaluate the requester if
In the recent years a considerable number of social network it trusts Agent? Which is the level of privileges? Then
analysis software has been developed in order to identify, it will evaluate the request, if it has the required
represent, analyze, visualize and simulate nodes (e.g., agents, information to respond to it.
organizations, or knowledge) and edges (relationships) from
various types of input data. The output data can be saved in 3- Ontology learning will help in creating evolution in
external files. communication, moving in parallel with agents
evolution after applying genetic algorithms on multi-
Network analysis tools allow researchers to investigate and agent system for large-scale semantic use.
understand representations of networks of different size - from
small populations (e.g., families, project teams) to very large In our implementation of the model we chose the software
(e.g., the Internet, disease transmission). The various tools Condor [4]. Condor is a dynamic social network analysis tool
provide a mathematical, statistical and visual analysis of the that employs text mining, auto-categorization and social
relationships in this kind of networks. Visual representations network mapping technologies in a unique visual way to
of social networks are important to understand network data discover hidden relationships by mining unstructured data of
and convey the result of the analysis as they play an important social networks such as the web site link structures, e-mail
role in generating new insights in social network analysis. networks, phone archives, RSS feeds, online forums. Condor
Visualization is often used as an additional or standalone data provides a graphic picture in real time of the relationships of
analysis method. With respect to visualization, network people, ideas, and organizations. Moreover it allows the user
analysis tools are used to change the layout, colours, size and to create visual maps, movies and adjacency matrices.
other properties of the network representation.
Condor takes as input Outlook Mailboxes, Eudora Mailboxes,
We propose in our model to extend social network analysis web mailing lists and online forums, web links, and flat files.
systems to cover both quantitative and qualitative methods to It parses those documents and incrementally stores them in a
capture knowledge. Then we will focus on evaluating nodes’ database. Condor allows to calculate indicators of
weight based on ontology development, as following three collaboration of actors and groups within a communication
level of ontology contributing in the proposed system. network. For example, the contribution index that is defined
as the number of messages sent minus the messages received
Ontology of social structure will help in defining mechanisms
normalized by the total number of message sent and received.
of social interactions as the following:
One of our research goals is to extend this and other social
1- Ontology of social nodes defining the position, roles
network indicators by adding “weighted factors” represented
and natural relations, as shown in Figure 2.
by the role of people in the organization or other meta
information of social nodes in the “ontological SNA”: the
personality traits or the influence of norms on the agents’
behaviour. We are planning to use a validation system (an
algorithm) to compare the nodes based on ontological data.
For example, an actor will have a higher weight if he/she has a
higher role, tenure or expertise in the firm. Furthermore, a
higher weight might be assigned to actors who have more
relationships with important clients or suppliers. These
weights should be tuned periodically according to the
availability of data within the firm and according to the type
of data and weight. We assume the update of firms database
should occur every three and six months, thus we plan to tune
the weights on such interval.
VI. Agents Modelling
There are many current agent-based modeling software
Figure 2. Ontological SNA frameworks to implement working models, Ascape
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5. (www.brookings.edu/dynamics/models/ascape), SWARM agent platform will create the agents with reference to the
(www.swarm.org), and RePast (repast.sourceforge.net) are nodes in SNA system Database.
among the best known of those suitable for constructing
research-quality models. Each of them makes use of Java and VII. Conclusions
is in the public domain, i.e., is freely downloadable for non-
This paper presents a different approach to develop and
commercial use. In our research case we decided to use
simulate agents, as well as to handle social aspects in
Repast Simphony platform based on Eclipse, because it is
intelligent analysis systems. Those proposed methods will
targeting social science modelling with wide use from
researchers, and also because of its friendly user interface, help in representing knowledge and simulating and
also agility and compatibility that are very important automating knowledge flow. The results are still in their early
stages, as it is only a prototype to prove the method used to
supported aspects. We will be able to adapt additional
extend and use data of social analysis system in multi agents
functionality by using UML2 tools to adapt UML profile for
systems for more realistic effective simulation and
agents or using any other required plug-ins that might be
useful. As we mentioned before, the agents we aim to develop management. We are in a phase of theory validation, but the
are social agents that reflect people in the environment (see issue requires a lot of time and effort to implement the
complete proposed method, and also put in production a truly
Figure 3).
distributed development environment with the cooperation of
several firms to increase adoption values. Figure 5 shows our
initiative to develop social agents on Repast framework.
Figure 3. Agent in social implementation
Till today there is no recognizable best way to build the agents,
every agent design has to include mechanisms for receiving
input from the environment, for storing a history of previous
inputs and actions, for devising what to do next, for carrying
out actions and for distributing outputs. In our research we
will build environment and groups that reflect the
environment and groups in social level (real life). The rules
will be based on mixing qualitative and quantitative capturing
Figure 5. Modeling agent behavior in Repast framework
knowledge methods in complex algorithms to treat
environment as the real node do. Figure 4 details the model’s
implementation.
VIII. Future Work
Why is the research model important to business? The answer
is because we live in an increasingly complex world. First, the
systems that we need to analyse and model are becoming
more complex in terms of their interdependencies.
Also nowadays, some systems have always been too complex
for us to be adequately modelled. Modelling economic
markets has traditionally relied on the notions of perfect
markets, homogeneous agents, and long-run equilibrium
because these assumptions made the problems analytically
and computationally tractable.
This research will take a more realistic view of these
economic systems through agent based modelling.
Figure 4. Integration implementation view
Furthermore, data are becoming organized into databases at
finer levels of granularity. Micro-data can now support micro-
We start from importing data from social network analysis
simulations. And finally, but most importantly, computational
system database to agent platform, handling the data by an
power is advancing rapidly. We also aim to compute large-
algorithm which will represent the agent behavior. Also in the
scale micro-simulation models.
same way the data are sorted in database for each node, the
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