Information Technology Reference
In-Depth Information
decision-making and problem solving (Shim et al., 2002). Organizations are increasingly complex
with increased emphasis on decentralized decision-making. Such changes create the need for DSS that
focus on supporting problem solving activities on distributed platforms by providing problem specific
knowledge, and supporting information, to a decision maker using Internet-based technologies. This
trend requires enterprise DSS for effective decision-making with processes and facilities to support the
use of knowledge management.
Recent advances in systems support for problem solving and decision-making witness the increased
use of artificial intelligence (AI) based techniques for knowledge representation (Whinston, 1997; Goul
2005). Knowledge representation takes multiple forms including the incorporation of business rules,
decision analytical models and models generated from the application of machine learning algorithms.
Intelligent decision support systems (IDSS) incorporate intelligence in the form of knowledge about
the problem domain, with knowledge representation to inform the decision process, facilitate problem
solving and reduce the cognitive load of the decision maker. Weber
et. al
. (2003) identified requirements
for organizational KMS where the central unit is a repository of knowledge artifacts collected from in-
ternal or external organizational sources. These KMS can vary based on the type of knowledge artifact
stored, the scope and nature of the topic described and the orientation (Weber et al., 2003). Ba
et. al.
(1997) enumerate the KM principles necessary to achieve intra-organizational knowledge bases as: (i)
the use of corporate data to derive and create higher-level information and knowledge, (ii) provision of
tools to transform scattered data into meaningful business information. Knowledge repositories play a
central and critical role in the storage, distribution and management of knowledge in an organization.
Interestingly, Bolloju
et. al.,
(2002) proposed an approach for integrating decision support and KM that
facilitates knowledge conversion through suitable automated techniques to:
•
Apply knowledge discovery techniques (KDT) for knowledge externalization
•
Employ repositories for storing externalized knowledge
•
Extend KDT for supporting various types of knowledge conversions
This chapter is motivated by these principles. We present an intelligent knowledge-based multi-agent
architecture for knowledge-based decision support using eXtensible Markup Language (XML) related
technologies for knowledge representation. This allows for knowledge exchange over distributed and
heterogeneous platforms. The proposed architecture integrates DSS and KMS using XML-based tech-
nologies as the medium for the representation and exchange of domain specific knowledge. Intelligent
agents to facilitate the creation, exchanges and use of the knowledge in decision support activities.
This is the primary contribution of this chapter to the existing body of knowledge in DSS, KMS and
multi-agent research.
This chapter builds on existing bodies of knowledge in intelligent agents, KM, DSS and XML
technology standards. Our research focuses on achieving a transparent translation between XML and
Decision Trees through software agents. This creates the foundation for knowledge representation
and exchange, through intelligent agents, to support decision-making activity for users of the system.
We use a knowledge repository to store knowledge, captured in XML documents, that can used and
shared by software agents within the multi-agent architecture. We call this architecture “
an Intelligent
Knowledge-based Multi-agent Decision Support Architecture
” (IKMDSA) IKMDSA integrates KDT
and knowledge repositories to store externalized knowledge. It uses an intelligent multi-agent system
with explanation facility to provide distributed decision support using Internet-based technologies.
Search WWH ::
Custom Search