Gloria Phillips-Wren, Nikhil Ichalkaranje and Lakhmi C. Jain (Eds.) Intelligent Decision Making: An AI-Based Approach

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1 Gloria Phillips-Wren, Nikhil Ichalkaranje and Lakhmi C. Jain (Eds.) Intelligent Decision Making: An AI-Based Approach

2 Studies in Computational Intelligence, Volume 97 Editor-in-chief Prof. Janusz Kacprzyk Systems Research Institute Polish Academy of Sciences ul. Newelska Warsaw Poland kacprzyk@ibspan.waw.pl Further volumes of this series can be found on our homepage: springer.com Vol. 74. Robert Schaefer Foundation of Global Genetic Optimization, 2007 ISBN Vol. 75. Crina Grosan, Ajith Abraham and Hisao Ishibuchi (Eds.) Hybrid Evolutionary Algorithms, 2007 ISBN Vol. 76. Subhas Chandra Mukhopadhyay and Gourab Sen Gupta (Eds.) Autonomous Robots and Agents, 2007 ISBN Vol. 77. Barbara Hammer and Pascal Hitzler (Eds.) Perspectives of Neural-Symbolic Integration, 2007 ISBN Vol. 78. Costin Badica and Marcin Paprzycki (Eds.) Intelligent and Distributed Computing, 2008 ISBN Vol. 79. Xing Cai and T.-C. Jim Yeh (Eds.) Quantitative Information Fusion for Hydrological Sciences, 2008 ISBN Vol. 80. Joachim Diederich Rule Extraction from Support Vector Machines, 2008 ISBN Vol. 81. K. Sridharan Robotic Exploration and Landmark Determination, 2008 ISBN Vol. 82. Ajith Abraham, Crina Grosan and Witold Pedrycz (Eds.) Engineering Evolutionary Intelligent Systems, 2008 ISBN Vol. 83. Bhanu Prasad and S.R.M. Prasanna (Eds.) Speech, Audio, Image and Biomedical Signal Processing using Neural Networks, 2008 ISBN Vol. 84. Marek R. Ogiela and Ryszard Tadeusiewicz Modern Computational Intelligence Methods for the Interpretation of Medical Images, 2008 ISBN Vol. 85. Arpad Kelemen, Ajith Abraham and Yulan Liang (Eds.) Computational Intelligence in Medical Informatics, 2008 ISBN Vol. 86. Zbigniew Les and Mogdalena Les Shape Understanding Systems, 2008 ISBN Vol. 87. Yuri Avramenko and Andrzej Kraslawski Case Based Design, 2008 ISBN Vol. 88. Tina Yu, David Davis, Cem Baydar and Rajkumar Roy (Eds.) Evolutionary Computation in Practice, 2008 ISBN Vol. 89. Ito Takayuki, Hattori Hiromitsu, Zhang Minjie and Matsuo Tokuro (Eds.) Rational, Robust, Secure, 2008 ISBN Vol. 90. Simone Marinai and Hiromichi Fujisawa (Eds.) Machine Learning in Document Analysis and Recognition, 2008 ISBN Vol. 91. Horst Bunke, Kandel Abraham and Last Mark (Eds.) Applied Pattern Recognition, 2008 ISBN Vol. 92. Ang Yang, Yin Shan and Lam Thu Bui (Eds.) Success in Evolutionary Computation, 2008 ISBN Vol. 93. Manolis Wallace, Marios Angelides and Phivos Mylonas (Eds.) Advances in Semantic Media Adaptation and Personalization, 2008 ISBN Vol. 94. Arpad Kelemen, Ajith Abraham and Yuehui Chen (Eds.) Computational Intelligence in Bioinformatics, 2008 ISBN Vol. 95. Radu Dogaru Systematic Design for Emergence in Cellular Nonlinear Networks, 2008 ISBN Vol. 96. Aboul Ella Hassanien, Ajith Abraham and Janusz Kacprzyky (Eds.) Computational Intelligence in Multimedia Processing: Recent Advances, 2008 ISBN Vol. 97. Gloria Phillips-Wren, Nikhil Ichalkaranje and Lakhmi C. Jain (Eds.) Intelligent Decision Making: An AI-Based Approach, 2008 ISBN

3 Gloria Phillips-Wren Nikhil Ichalkaranje Lakhmi C. Jain (Eds.) Intelligent Decision Making: An AI-Based Approach With 107 Figures and 44 Tables 123

4 Prof. Dr. Gloria Phillips-Wren Information Systems and Operations Management The Sellinger School of Business and Management Loyola College in Maryland 4501 N. Charles Street Baltimore, MD USA Dr. Nikhil Ichalkaranje School of Electrical and Information Engineering University of South Australia Adelaide South Australia SA 5095 Australia Prof. Dr. Lakhmi C. Jain School of Electrical and Information Engineering University of South Australia Adelaide South Australia SA 5095 Australia ISBN e-isbn Studies in Computational Intelligence ISSN X Library of Congress Control Number: c 2008 Springer-Verlag Berlin Heidelberg This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer-Verlag. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Cover Design: Deblik, Berlin, Germany Printed on acid-free paper springer.com

5 Dedicated to our mothers who were our first and best teachers.

6 Preface The fusion of artificial intelligence (AI) with decision support systems (DSSs) is opening exciting new areas of research and application. The resulting systems are smarter, more efficient, adaptable, and better able to aid human decision making. While AI aims to mimic human behaviour in limited ways, DSSs attempt to help humans make the best choice among a set of possible choices given explicit or implied criteria. Long a topic of science fiction, AI today is demonstrating that it can be integrated effectively into real systems and that it offers the only way possible to capture aspects of human intelligence such as learning. The combination of AI and DSSs provides formidable new computational assistants to humans that extend their capabilities in routine and complex stressful environments. Due to the increasing maturity of this interdisciplinary field as evidenced by the recent growth in the number of research publications and contributors entering the field, a book that explores the current state and future outlook of intelligent DSSs seems appropriate. The book is organized around three themes. The first two chapters provide a solid foundation by exploring studies and theories of human decision making. They trace some one hundred years of research including recent work by the well-known authors and provide a vision of the use of computerized decision aids. The second section deals with paradigms and methods associated with AI in DSS. The final section provides sample applications among the many that are appearing today and gives our perspective on future research directions needed to advance the field. This book would not have been possible without the efforts of many people. We thank the contributors for their inspiring research and the reviewers for their efforts to create a high-quality book. The publisher s support, patience and assistance are gratefully acknowledged. In particular, Srilatha Achuthan s unwavering efforts as project manager provided help when we needed it most.

7 VIII Preface We thank the research community for the advances that have made this book possible and our families for their continued support. USA Australia Australia Gloria Phillips-Wren Nikhil Ichalkaranje Lakhmi C. Jain

8 Foreword Intelligent decision systems (IDS) are a relatively new paradigm in the decision support systems (DSS) area. Consistent with the modern view on work activity as mostly knowledge work (Davenport, 2005) and recognising the critical role of knowledge for effective decision-making, intelligent decision support aims to provide the decision maker with quality assistance in gaining better knowledge and understanding of the decision situation. IDS are the means to achieve such assistance. This need for knowledge management and processing within decision support systems has resulted in a special class of systems that incorporates qualitative knowledge and reasoning, extending the functionality beyond those traditionally covered by DSS applications. These systems, variously termed Intelligent Decision Support Systems, Intelligent Decision Systems, Knowledge-Based Decision Support Systems, Active DSS and Joint Cognitive Systems, include qualitative knowledge to extend the typically quantitative data of earlier approaches to decision support (Burstein and Holsapple, 2008; Gupta et al. 2006). The label intelligent in IDS is derived from the attempts made in artificial intelligence (AI) to develop systems that computationally emulate some human cognitive capabilities such as reasoning, learning and memory. The need to incorporate domain knowledge and intelligent capabilities in decision support systems has been identified in various forms and models by many researchers, starting from Simon (1977), followed by Sprague (1993), and exemplified by Turban, Aronson and Liang (2005) and Holsapple and Whinston (1996) in their comprehensive analyses of tools and techniques for incorporating intelligence into DSS. Arnott and Pervan (2005), in their review of the DSS field, traced and described Intelligent Decision Support as a separate branch, which originated from research in AI and Expert Systems to complement the needs of modern Personalised Decision Support. The main role of IDS in an organisation is as an enabler for knowledge processing with communication capabilities to support knowledge sharing and exchange and to facilitate organisational learning (Carlsson and Kalling, 2006;

9 X Foreword Burstein and Linger, 2003). IDS aim to assist the decision maker in overcoming cognitive limitations to achieving the best decision outcomes. At the same time the system could identify some useful knowledge for future improvements in the decision-making process, thus facilitating continuous learning processes by an organisation. Conventional DSS was not intended to support such functionality, hence giving rise to IDS in a knowledge management context. Despite the significant potential of IDS and remarkable advances in AI technologies, the promise of IDS has not yet been realized. IDS are not widespread as such. One reason is that comprehensive research is still required on AI technologies to be used in IDS. Some technologies such as intelligent agents have advanced to the point that they are implemented in numerous practical applications, while other AI concepts such as neural networks are not yet as mature. In most cases, specialized IDS applications are reported in the literature, although generalized applications have not been developed. Research is needed on architectures and frameworks that could support production-level IDS both at the AI and at systems levels. Although IDS do not in general exist as stand-alone systems, any large-scale management information system would include some intelligent components. Modern approaches to assisting organizations such as customer relationship management (CRM), knowledge management systems (KMS), and business intelligence (BI) systems are heavily influenced by intelligent techniques and include a wide range of intelligent systems functionality. Many such systems require access to expert or problem-domain knowledge. Availability of sophisticated generic technological infrastructure makes it easier to specialise such systems to suit specific application domains. A number of books have been published in the area of IDS and related areas of Intelligent Decision Support Systems, and one needs to ask what another book can add to the community. Publication patterns over the last 10 years (shown in Fig. 1 based on data from Google Scholar) appear to show continued interest in IDS. This is a much needed book to update the interested reader in an exciting research field with many opportunities for advances in both theoretical and applied areas. The current volume is an effort to bridge the range of exploration in this field from fundamental understanding of human decision making at an abstract conceptual level, to methods of computational intelligence, and to applications of intelligent decision support techniques in specific contexts. The book presents fascinating background information on human decision making and makes a contribution to the IDS area by presenting the current state of knowledge and identifying key research gaps. I would like to congratulate the editors of this book and look forward to it being remembered as a pivotal beginning for collective focus and mutual inspiration. Victoria, Australia Frada V. Burstein

10 Foreword XI 25,000 20,000 15,000 10,000 intelligent decision support intelligent decision support systems intelligent decision systems 5, Fig. 1. Comparative data on publications in Intelligent Decision Support, Intelligent Decision Support Systems and Intelligent Decision Systems (based on the data from Google Scholar) References Arnott, D. and Pervan, G. (2005). A Critical Analysis of Decision Support Systems Research. Journal of Information Technology, 20, Burstein, F. and Holsapple, C.W. (eds.) (2008). Handbook on Decision Support Systems. Berlin/Heidelberg: Springer. Burstein, F. and Linger, H. (2003). Supporting Post-Fordist Work Practices: A Knowledge Management Framework for Supporting Knowledge Work. Information Technology and People, 16, 3, Carlsson, S.A. and Kalling, T. (2006). Decision Support through Knowledge Management: What Works and What Breaks. In Adam, F., Brézillon, P., Carlsson, S. and Humphreys, P. (eds.), Creativity and Innovation in Decision Making and Decision Support, London, UK: Decision Support Press, Davenport, T.H. (2005). Thinking for a Living: How to Get Better Performance and Results from Knowledge Workers. Boston, MA: Harvard Business School Press. Gupta J., Forgionne G., and Mora M. (eds.) (2006). Intelligent Decision-Making Support Systems (i-dmss): Foundations, Applications and Challenges. Engineering Decision Series of Springer. Simon, H. (1977). The New Science of Management Decisions. New Jersey, NJ: Prentice-Hall. Sprague, R.H. (1993). A Framework for the Development of Decision Support Systems. In Sprague, R.H. and Watson, H.J. (eds.), Decision Support Systems Putting Theory into Practice, New Jersey, NJ: Prentice-Hall International, Turban, E., Aronson, J. and Liang, T. (2005). Decision Support Systems and Intelligence Systems. 7th Ed., New Jersey: Pearson Prentice-Hall. Holsapple C. and Whinston, A. (1996). Decision Support Systems: A Knowledge Based Approach. St. Paul, MN: West Publishing.

11 Contents Dedication... V Preface...VII Foreword... IX Part I Background: Human Decision Making 1 Understanding Human Decision Making A Fundamental Step Towards Effective Intelligent Decision Support Jean-Charles Pomerol and Frederic Adam Cognitive Elements of Human Decision Making Jens Pohl Part II Methods: Computational Intelligence 3 Introduction to Computational Intelligence for Decision Making Witold Pedrycz, Nikhil Ichalkaranje, Gloria Phillips-Wren, and Lakhmi Jain Collaborative Decision Making Amongst Human and Artificial Beings Daniel I. Thomas and Ljubo B. Vlacic Decision Analysis with Fuzzy Targets Van-Nam Huynh, Yoshiteru Nakamori, and Tetsuya Murai...135

12 XIV Contents 6 An Approximation Kuhn Tucker Approach for Fuzzy Linear Bilevel Decision Making Guangquan Zhang, Jie Lu, and Tharam Dillon A Replanning Support for Critical Decision Making Situations: A Modelling Approach Guy Camilleri, Jean-Luc Soubie, and Pascale Zarate A Unifying Multimodel Taxonomy and Agent-Supported Multisimulation Strategy for Decision-Support Levent Yilmaz and Andreas Tolk Part III Applications: Intelligent Decision Support 9 A Consensus Support System for Group Decision Making Problems with Heterogeneous Information F. Mata, L. Martínez, and E. Herrera-Viedma Evaluating Medical Decision Making Heuristics and Other Business Heuristics with Neural Networks Steven Walczak Building Intelligent Sensor Networks with Multiagent Graphical Models Yang Xiang An Intelligent Expert Systems Approach to Layout Decision Analysis and Design under Uncertainty Abdul-Rahim Ahmad, Otman Basir, Khaled Hassanein, and Shahid Azam Using Self Organising Feature Maps to Unravel Process Complexity in a Hospital Emergency Department: A Decision Support Perspective A. Ceglowski and L. Churilov Future Directions: Building a Decision Making Framework Using Agent Teams Jeffrey Tweedale, Christos Sioutis, Gloria Phillips-Wren, Nikhil Ichalkaranje, Pierre Urlings, and Lakhmi C. Jain...387