Distributed Artificial Intelligence, Agent Technology, and Collaborative Applications

Distributed Artificial Intelligence, Agent Technology, and Collaborative Applications

Vijayan Sugumaran (Oakland University, USA)
Indexed In: SCOPUS
Release Date: December, 2008|Copyright: © 2009 |Pages: 450
DOI: 10.4018/978-1-60566-144-5
ISBN13: 9781605661445|ISBN10: 1605661449|EISBN13: 9781605661452
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Description & Coverage
Description:

Cutting-edge developments in artificial intelligence are now driving applications that are only hinting at the level of value they will soon contribute to organizations, consumers, and societies across all domains.

Distributed Artificial Intelligence, Agent Technology, and Collaborative Applications offers an enriched set of research articles in artificial intelligence (AI), covering significant AI subjects such as information retrieval, conceptual modeling, supply chain demand forecasting, and machine learning algorithms. This comprehensive collection provides libraries with a one-stop resource to equip the academic, industrial, and managerial communities with an in-depth look into the most pertinent AI advances that will lead to the most valuable applications.

Coverage:

The many academic areas covered in this publication include, but are not limited to:

  • Active information systems
  • Artificial Intelligence
  • Automatic Web page organization
  • Causal strategy models
  • Conceptual Modeling
  • Demand supply network optimization
  • E-trade auction
  • Empirical inference of numerical information
  • Forecasting supply chain demand
  • Information modeling
  • Information Retrieval
  • Knowledge acquisition process
  • Knowledge-driven decision support
  • Logic specifications
  • Machine Learning Algorithms
  • Multi-agent architecture
  • Multi-Agent Systems
  • Petri Nets
  • Problem of universals
  • Rough set theory
  • Search engine performance comparisons
  • Trust formalization
  • Web Mining
Reviews and Testimonials

This book discusses a number of agent based applications developed for knowledge-driven decision support, online auctions, federated information systems, and mobile computing. Similarly, in the area of search and retrieval, the book provides current research in search engine performance, user centered approach for information and image retrieval, web mining and document clustering.

– Vijayan Sugumaran, Oakland University, USA

Research from around the world, collected here, examines a number of agent-based applications developed for knowledge-driven decision support, online auctions, federal information systems, and mobile computing.

– Book News Inc. (March 2009)
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Editor/Author Biographies
Vijayan Sugumaran is Distinguished Professor of Management Information Systems and Chair of the Department of Decision and Information Sciences at Oakland University, Rochester, Michigan, USA. He is also the Co-Director of the Center for Data Science and Big Data Analytics at Oakland University. He received his Ph.D. in Information Technology from George Mason University, Fairfax, Virginia, USA. His research interests are in the areas of Big Data Management and Analytics, Ontologies and Semantic Web, Intelligent Agent and Multi-Agent Systems. He has published over 260 peer-reviewed articles in Journals, Conferences, and Books. He has edited twenty books and serves on the Editorial Board of eight journals. He has published in top-tier journals such as Information Systems Research, ACM Transactions on Database Systems, Communications of the ACM, IEEE Transactions on Big Data, IEEE Transactions on Engineering Management, IEEE Transactions on Education, and IEEE Software. Dr. Sugumaran is the editor-in-chief of the International Journal of Intelligent Information Technologies (IJIIT). He is the Chair of the Intelligent Agent and Multi-Agent Systems mini-track for Americas Conference on Information Systems (AMCIS 1999–2021). Dr. Sugumaran has served as the Program Chair for the 14th Workshop on E-Business (WeB2015), the International Conference on Applications of Natural Language to Information Systems (NLDB 2008, NLDB 2013, NLDB 2016, and NLDB 2019), 29th Australasian Conference on Information Systems (ACIS 2018), 14th Annual Conference of Midwest Association for Information Systems (MWAIS 2019), 5th IEEE International Conference on Big Data Service and Applications (BDS 2019), and 2022 Midwest Decision Sciences Institute Annual Conference (MWDSI 2022). He also regularly serves as a program committee member for numerous national and international conferences.

Website: http://www.sba.oakland.edu/faculty/sugumara
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Editorial Review Board
  • Akhilesh Bajaj, University of Tulsa, USA
  • Ralph Bergmann, University of Trier, Germany
  • Kaushal Chari, University of South Florida, USA
  • Roger Chiang, University of Cincinnati, USA
  • Dursun Delen, Oklahoma State University, USA
  • Armin Heinzl, University of Mannheim, Germany
  • Dawn Jutla, Saint Mary's University, Canada
  • Rajiv Kishore, SUNY at Buffalo, USA
  • Christoph Schlueter Langdon, University of Southern California, USA
  • Zakaria Maamar, Zayed University, Dubai, United Arab Emirates
  • Greg Madey, University of Notre Dame, USA
  • Deependra Moitra, Infosys Technologies Limited, India
  • Karen Neville, University College Cork , Ireland
  • Wee Keong Ng, Nanyang Technological University, Singapore
  • Paolo Petta, Austrian Research Institute for AI, Austria
  • Ram Ramesh, SUNY at Buffalo, USA
  • Riyaz Sikora, University of Texas at Arlington, USA
  • Rahul Singh, The University of North Carolina at Greensboro, USA
  • David Taniar, Monash University, Australia
  • William Wagner, Villanova University, USA
  • Steven Walczak, University of Colorado at Denver, USA
  • Huaiqing Wang, City University of Hong Kong, Hong Kong
  • Kok Wai Wong, Murdoch University, Western Australia
  • Carson Woo, University of British Columbia, Canada
  • Victoria Yoon, University of Maryland, Baltimore County, USA
  • Daniel Zeng, University of Arizona, USA