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Lakhmi C.Jain
Ngoc Thanh Nguyen
(Eds.)
Knowledge Processing and
Decision Making in Agent-Based
Systems
123
Prof.LakhmiC.Jain
UniversityofSouthAustralia
AdelaideCity
MawsonLakesCampus
SouthAustraliaSA5095
Australia
Email:[email protected]
Prof.NgocThanhNguyen
InstituteofInformatics
WroclawUniversityofTechnology
Str.Janiszewskiego11/17
50-370Wroclaw
Poland
Email:[email protected]
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Preface
Agent technology has been successfully applied in the last decade to numerous busi-
ness applications ranging from entertainment and education to electronic commerce
and reliable intelligent manufacturing [1]. Today, agent technology has touched virtu-
ally every field in the world, some with a limited success. A number of researchers are
engaged in the design, development and innovative applications of intelligent agents
due to their characteristics such as autonomous behaviour and their ability to work
cooperatively in teams. Knowledge processing plays an important part in the imple-
mentation of intelligent machines which can mimic the human intelligence in a limited
way. The characteristics such as knowledge extraction from data, learning and teaming
are important to realize intelligent decision support systems. Decision support systems
can be benefitted by the better models of knowledge processing and intelligent agent
for delivering the best decisions to the user.
This book presents a sample of research results in the knowledge processing and
decision making in agent-based systems. The chapters range from theoretical founda-
tions to the practical applications.
We wish to express our sincere gratitude to the authors and reviewers for their
contributions. Thanks are due to the Springer-Verlag for their editorial assistance.
The editorial assistance provided by the SCI Data Processing Team of Scientific
Publishing Services Private Limited is acknowledged.
Lakhmi C. Jain
Ngoc Thanh Nguyen
Reference
[1] Nishida, T.: Foreword in a book on Intelligent Agents and Their Applications. In: Jain, L.C.,
et al. (eds.) Springer, Heidelberg (2002)
Contents
1 Innovations in Knowledge Processing and Decision Making
in Agent-Based Systems
Lakhmi C. Jain, Chee Peng Lim, Ngoc Thanh Nguyen ................. 1
2 Towards Real-World HTN Planning Agents
Hisashi Hayashi, Seiji Tokura, Fumio Ozaki .......................... 13
3 Mobile Agent-Based System for Distributed Software
Maintenance
Gordan Jezic, Mario Kusek, Igor Ljubi, Kresimir Jurasovic ............ 43
4 Software Agents in New Generation Networks: Towards
the Automation of Telecom Processes
Vedran Podobnik, Ana Petric, Krunoslav Trzec, Gordan Jezic .......... 71
5 Multi-agent Systems and Paraconsistent Knowledge
Jair Minoro Abe, Kazumi Nakamatsu................................ 101
6 An Agent-Based Negotiation Platform for Collaborative
Decision-Making in Construction Supply Chain
Xiaolong Xue, Zhaomin Ren ....................................... 123
7 An Event-Driven Algorithm for Agents on the Web
Anne H˚akansson.................................................. 147
8 A Generic Mobile Agent Framework towards Ambient
Intelligence
Yung-Chuan Lee, Elham S. Khorasani, Shahram Rahimi,
Sujatha Nulu ..................................................... 175
9 Developing Actionable Trading Strategies
Longbing Cao..................................................... 193
VIII Contents
10 Agent Uncertainty Model and Quantum Mechanics
Representation: Non-locality Modeling
Germano Resconi, Boris Kovalerchuk................................ 217
11 Agent Transportation Layer Adaptation System
Jeffrey Tweedale, Felix Bollenbeck, Lakhmi C. Jain, Pierre Urlings ...... 247
12 Software Agents to Enable Service Composition through
Negotiation
Claudia Di Napoli................................................. 275
13 Advanced Technology towards Developing Decentralized
Autonomous Flexible Manufacturing Systems
Hidehiko Yamamoto............................................... 297
Author Index................................................... 323
1
Innovations in Knowledge Processing and Decision
Making in Agent-Based Systems
Lakhmi C. Jain1, Chee Peng Lim2, and Ngoc Thanh Nguyen3
1 School of Electrical & Information Engineering
University of South Australia, Australia
2 School of Electrical & Electronic Engineering
University of Science Malaysia, Malaysia
3 Institute of Information Science and Engineering
Wroclaw University of Technology, Poland
Abstract. This chapter introduces knowledge processing and decision making using agent-
based technologies. The importance of creating effective and efficient computerized systems
for extracting information and processing knowledge as well as for supporting decision making
activities is highlighted. Then, an overview covering agent-based software tools and develop-
ment methodologies, and usability and challenges of agent-based systems in industrial applica-
tions is presented. The contribution of each chapter included in this book is also described.
1.1 Introduction
Most real life problems are complex and multi-facet, and involve many criteria in the
decision making process. As a result, researchers have investigated and proposed a
variety of methodologies and techniques to design and develop computerised systems
for decision support applications. In general, a Decision Support System (DSS) is a
computerized information system that supports decision-making activities in various
domains such as business, finance, management, manufacturing, and biomedicine. A
useful DSS is able to compile and extract meaningful information from raw data and
to suggest potential solutions for users to make informed decisions.
A useful conceptual framework for classifying and describing DSSs is proposed in
[1]. Five generic DSS types are identified and defined based upon the dominant tech-
nology component including communications-driven, data-driven, document-driven,
knowledge-driven, and model-driven DSSs. A DSS can be developed for specific or
general-purpose applications, and can be used by individuals or groups. The enabling
technology of the DSS can be a mainframe computer, a client/server LAN, a spread-
sheet, or a web-based architecture [1].
Many methodologies have been proposed to help build and understand DSSs. One
of the approaches for developing DSS is agent-based technologies. From the litera-
ture, a lot of agent-based DSSs can be found, and a lot of successful applications of
agent-based DSSs are reported. In this book, a small fraction of DSSs that utilize
agent-based technologies for knowledge processing and decision making are pre-
sented. The main aim is to share and disseminate information pertaining to recent
L.C. Jain, N.T. Nguyen (Eds.): Knowl. Proc. & Dec. Mak. in Agent-Based Sys., SCI 170, pp. 1–12.
springerlink.com © Springer-Verlag Berlin Heidelberg 2009
2 L.C. Jain, C.P. Lim, and N.T. Nguyen
advancements in theoretical and practical aspects of agent-based DSSs for tackling
real-life problems in various domains.
1.2 Knowledge Processing and Decision Making
Humans make decisions, either consciously or sub-consciously in daily activities.
The process of decision making is so essential that it has become an integral part of
life, and automated tools and systems for decision support are always in demand. The
demand is exacerbated by the rapid development and wide-spread usage of the inter-
net as a resource for information and knowledge sharing and reuse.
The world-wide-web contains many heterogeneous data sources ranging from text
documents to multimedia images; from audio files to video streams. However, it is
difficult for users to extract relevant materials from such a complex environment, due
to information overload [2, 3]. In general, data are raw, numeric records. Information
is concerned with data that have been processed and analysed, and knowledge covers
actionable information that is comprehensible for humans to reason and infer deci-
sions. Therefore, the heterogeneous data sources from the internet, or from other
sources, need to be processed and analysed sensibly, and automatically, so that mean-
ingful knowledge that are relevant and useful to decision makers can be retrieved.
For a computerized DSS to be useful for practical implementation, several crucial
properties that enable the DSS to combine different types of data and information
from various sources in a seamlessly manner and without much user intervention
should be established. These properties are related to knowledge processing and deci-
sion making activities such as knowledge representation, knowledge management and
reuse, reasoning and inference techniques, as well as risk analysis. In this field, one of
the emerging technologies to facilitate and manipulate knowledge processing and
decision making in DSS is agent-based systems, as described in [4, 5, 6].
1.3 Agent-Based Systems
The rapid advancement of agent-based technologies has opened up the way for the
development of a new and exciting paradigm for the establishment of intelligent soft-
ware systems operating in dynamic and complex environments. There are a lot of
areas in agent-based systems that have attracted attention of researchers. These in-
clude formal frameworks for collaboration and cooperation between agents, method-
ologies for development of multi-agent systems, as well as model and techniques for
managing inter-agent relationships (e.g., belief, trust, and reputation).
Agent-based technologies have emerged from the field of distributed artificial in-
telligence [7]. Before embarking on the overview of agent-based technologies, a
number of terminologies of agents that are useful for understanding their problem-
solving and decision making characteristics, as defined in [8], are provided. An (intel-
ligent) agent is an autonomous, problem-solving computational entity capable of
operating in dynamic and open environments. Agent properties refer to the fundamen-
tal characteristics of agents, which include autonomous decision making and
response, with the ability to communicate, negotiate, and cooperate with other
1 Innovations in Knowledge Processing and Decision Making 3
agents. Agent-based solutions to a decision-making problem explore agents as
autonomous decision-making units and their interactions to achieve global goals.
Other agent-related terminologies and definitions can be found in [8].
In [9], three main domains of research and development in agent-based technolo-
gies are discussed: (i) development of tools (environments, languages, specific com-
ponents); (ii) development of methodologies; and (iii) search for and development of
reusable solutions and components. In this aspect, some available software tools for
development and deployment of agent-based systems include MASDK [10], JACK
[11], JADE [12], and AgentBuilder [13]. There are a number of development meth-
odologies for agent-based system. They include Gaia [14], MaSE [15], Tropos [16],
Prometheus [17], and MESSAGE [18]. On the other hand, some useful web resources
pertaining to software tools as well as publications of agent-based systems are easily
accessible, refer [19-21].
While agent properties are useful for solving complex, real-world industrial prob-
lems, there are some challenges faced by the practical implementation of agent-based
systems. The challenges arising from application of agent-based systems include
ontology management (e.g. negotiation [22], perspectives [23]), reusable agent reposi-
tory, and spectral analysis automation [24]. Besides, although agent-based systems are
capable of addressing much richer and broader range of possible tasks, e.g. for quali-
fication and deployment of flight maintenance systems, the process of verification and
validation of agent-based software systems becomes considerably harder [25].
In industrial environments, agent-based solutions are investigated for tackling
problems in three main domains [8]. They are real-time manufacturing control prob-
lems, complex operation management problems, and virtual enterprise problems. As
pointed out in [8], five key industrial application areas that are suitable for deploying
agent-based solutions are real-time control of high-volume, high variety, discrete
manufacturing operations; monitoring and control of physically highly distributed
systems; production management of frequently disrupted operations; coordination of
organizations with conflicting goals; and frequently reconfigured, automated envi-
ronments. On one hand, the benefits of adopting agent-based solutions in industrial
environments include feasibility, robustness and flexibility, reconfigurablity, and
redeployability. On the other hand, the barriers include cost, guarantees for opera-
tional performance, and scalability [8].
1.4 Chapters Included in This Book
The book includes thirteen chapters. Chapter one introduces knowledge processing
and decision making in agent-based systems. It also outlines the contributions made in
the book. Chapter two presents a novel real-world HTN planning agent. It is
demonstrated by the authors that the proposed approach is efficient than other
replanning methods. Chapter three presents a mobile agent-based system for distrib-
uted software maintenance. The authors have considered scenarios such as mainte-
nance process, software modification in their case studies. Chapter four presents a
combination of artificial intelligence mechanisms and computational economics con-
cepts for tackling problems in telecommunication services. The mechanisms and
concepts are implemented in agent-based electronic markets, and novel discovery and
