SEKE 2012 Proceedings - Knowledge Systems Institute

Using Social Networks for Learning New Concepts
in Multi-Agent Systems
Shimaa M. Shimaa El-Sherif M. 1 El-Sherif , 1 , Behrouz Far Far 2 2
Department of of Electrical and and Computer Computer Engineering Engineering
University University of Calgary of Calgary
Calgary, Calgary, Canada Canada
e-mail: e-mail: 1 smmelshe@ucalgary.ca
1 smmelshe@ucalgary.ca
2 far@ucalgary.ca
2
far@ucalgary.ca
Armin Eberlein
Department of Computer Science & Engineering
American University of Sharjah
Sharjah, UAE
e-mail: eberlein@ucalgary.ca
Abstract—Traditionally, communication between agents in multiagent
systems is possible by committing to a common ontology.
Unfortunately, this commitment is unrealistic and difficult to
achieve in all cases. It is preferred in communication between
agents to enable each agent to use its own conceptualization of its
knowledge domain (i.e. each agent needs to use its own ontology).
But that makes communication between agents more difficult
and complex. In order to overcome this obstacle, agents need to
negotiate the meaning of concepts and use their learning
capability. Agents can learn new concepts they do not know but
need in order to communicate with other agents in the system.
This paper addresses the formation of new concepts in a multiagent
system where individual autonomous agents try to learn
new concepts by consulting other agents. In this paper, individual
agents create their distinct conceptualization and rather than
commit to a common ontology, they use different ontologies. This
paper uses positive and negative examples to help agents learn
new concepts. It also investigates the selection of those examples
and their numbers from teacher agents based on the strength of
the ties between the learner agent and each teacher agent. A
contribution of this paper is that the concept learning is realized
by a multi-agent system in the form of a social network. We
investigate using the concept of social networks in defining
relationships between agents and show that it will improve the
overall learning accuracy.
Keywords- distributed knowledge management; concept
learning; multi-agent system; social network; ontology
I. INTRODUCTION
Interest in Distributed Knowledge Management (DKM)
systems has been growing due to their ability to solve real
world complex problems that cannot be solved by centralized
Knowledge Management systems. The challenges that DKM
faces are:
Representation of knowledge
Distribution of knowledge
Sharing of distributed knowledge
Ontologies can help represent knowledge and are therefore
a good solution for the first challenge faced by DKM
(representation of knowledge). Regarding the second challenge
(distribution of knowledge), multi-agent systems (MAS) can
handle distributed and heterogeneous environments. MAS is an
environment in which different agents can interact with each
other to solve complex problems. The third challenge is the
most difficult issue for DKM: how to share and communicate
knowledge and how to overcome the semantic heterogeneity.
This is the main target of this paper.
In this paper we propose a concept learning system based
on blending the heterogeneity of MAS and sharing capability
of social networks. In our system, several MASs interact with
each other to learn a new concept. Each MAS controls a
repository of knowledgebase (i.e. concepts and their relations)
that consists of an ontology with concept definitions and
instances illustrating each concept. These MASs can interact
with each other through a social network with varying strengths
of ties between each two agents. The strengths of ties can be
updated according to the frequency and type of interaction
between agents. In the case study system, there is one learner
agent that tries to learn a new concept from multiple teacher
agents using this setup. Each teacher agent has its own
ontology representation with its distinct understanding of the
new concept. Teacher agents try to teach the learner agent this
new concept the way they understand it by sending it positive
and negative examples.
II. BACKGROUND AND LITERATURE REVIEW
In this section we will cover the main areas that are
essential to understanding our proposed system. We will briefly
describe multi-agent systems (MAS), ontologies and social
networks. Then, we outline the current state of the art in
ontological concept learning.
A. Multi-Agent System (MAS)
MAS can be de fined as: “a l oosely coupled network of
problem solvers (agents) that interact to solve problems which
are beyond the individual capabilities or knowledge of each
problem solver” [1]. MAS is therefore a co llection of
heterogeneous agents, each of which with its own problem
solving capability, able to locate, communicate and coordinate
with each other.
In our system, agents perform major functionalities on
behalf of each repository. The main roles of agents in our
system are: handling query statements; managing concepts in
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