Kevin B. Korb

Marcus Randall

Tim Hendtlass

Asweapproachthelimitsandcapabilitiesofmachines,we?ndthattheprinciple of diminishing returns is forcing scientists to turn their attention towardbiology to provide practical solutions to problems in our increasingly complex world.An emerging?eldofsciencethatstudies thesystemsrelatedtolife,its processesand evolution is known as Art?cial Life (ALife). It draws on the skills and talents of scientists from a variety of disciplines, including, but not limited to, biology, psychology,evolutionand computer science. These researchersareshowingthat, by using even simple simulations of the basic processes of nature, much about our complex natural world and our own humanity can be revealed. Gatherings in the expanding Alife community are becoming more common. One such series, the Australian Conference on Arti?cial Life (ACAL), began in Adelaide in 2001 and was known as the “Inaugral Workshop on Arti?cal Life.” From these small beginnings, it has become a biennial event that has previously been held in Canberra (2003), Sydney (2005)and the Gold Coast (2007). ACAL 2009 received over 60 quality submissions of which 27 were accepted for oral presentation at the conference. Each paper submission was assigned to three members of the Program Committee. The Program Committe, as well as the conference delegates, came from countries across Asia-Paci?c, North America and Europe. In addition to the regular papers, the conference was fortunate enough to be able to have two renowned invited speakers – Mark Bedau (Reed College, USA) and Andries Englebrecht (University of Pretoria, South Africa).

4th Australian Conference, ACAL 2009, Melbourne, Australia, December 1-4, 2009, Proceedings

Artificial Life: Borrowing from Biology

Xindong Wu

Ramamohanarao Kotagiri

This book constitutes the refereed proceedings of the Second Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD-98, held in Melbourne, Australia, in April 1998. The book presents 30 revised full papers selected from a total of 110 submissions; also included are 20 poster presentations. The papers contribute new results to all current aspects in knowledge discovery and data mining on the research level as well as on the level of systems development. Among the areas covered are machine learning, information systems, the Internet, statistics, knowledge acquisition, data visualization, software reengineering, and knowledge based systems.

Second Pacific-Asia Conference, PAKDD'98, Melbourne, Australia, April 15-17, 1998, Proceedings

Research and Development in Knowledge Discovery and Data Mining

Ann E. Nicholson

Updated and expanded, Bayesian Artificial Intelligence, Second Edition provides a practical and accessible introduction to the main concepts, foundation, and applications of Bayesian networks. It focuses on both the causal discovery of networks and Bayesian inference procedures. Adopting a causal interpretation of Bayesian networks, the authors discuss the use of Bayesian networks for causal modeling. They also draw on their own applied research to illustrate various applications of the technology. New to the Second Edition New chapter on Bayesian network classifiers New section on object-oriented Bayesian networks New section that addresses foundational problems with causal discovery and Markov blanket discovery New section that covers methods of evaluating causal discovery programs Discussions of many common modeling errors New applications and case studies More coverage on the uses of causal interventions to understand and reason with causal Bayesian networks Illustrated with real case studies, the second edition of this bestseller continues to cover the groundwork of Bayesian networks. It presents the elements of Bayesian network technology, automated causal discovery, and learning probabilities from data and shows how to employ these technologies to develop probabilistic expert systems. Web Resource The book’s website at www.csse.monash.edu.au/bai/book/book.html offers a variety of supplemental materials, including example Bayesian networks and data sets. Instructors can email the authors for sample solutions to many of the problems in the text.

Bayesian Artificial Intelligence, Second Edition

A Pragmatic Bayesian Platform for Automating Scientific Induction

As the power of Bayesian techniques has become more fully realized, the field of artificial intelligence has embraced Bayesian methodology and integrated it to the point where an introduction to Bayesian techniques is now a core course in many computer science programs. Unlike other books on the subject, Bayesian Artificial Intelligence keeps mathematical detail to a minimum and covers a broad range of topics. The authors integrate all of Bayesian net technology and learning Bayesian net technology and apply them both to knowledge engineering. They emphasize understanding and intuition but also provide the algorithms and technical background needed for applications. Software, exercises, and solutions are available on the authors’ website.

Martin Burch

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