American Association for Artificial Intelligence – serie

Android epistemology is the exploration of the space of possible machines and their capacities for knowledge, beliefs, attitudes, desires, and for action in accord with their mental states.Android epistemology is the exploration of the space of possible machines and their capacities for knowledge, beliefs, attitudes, desires, and for action in accord with their mental states.

Proceedings from the latest meeting of the leading AI conference; includes theoretical, experimental, and empirical work.The National Conference on Artificial Intelligence remains the bellwether for research in artificial intelligence. Leading AI researchers and practitioners as well as scientists and engineers in related fields present theoretical, experimental, and empirical results, covering a broad range of topics that include principles of cognition, perception, and action; the design, application, and evaluation of AI algorithms and systems; architectures and frameworks for classes of AI systems; and analyses of tasks and domains in which intelligent systems perform. The Innovative Applications of Artificial Intelligence conference highlights successful applications of AI technology; explores issues, methods, and lessons learned in the development and deployment of AI applications; and promotes an interchange of ideas between basic and applied AI. This volume presents the proceedings of the latest conferences, held in July, 2004.

How to think about minds that aren't like ours: using artificial intelligence and computation theory to study the powers and limits of systems that learn. For millennia, "from Aristotle to almost yesterday," the great problems of philosophy have all been about people: questions of epistemology and philosophy of mind have concerned human capacities and limitations. Still, say the editors of Thinking about Android Epistemology, there should be theories about other sorts of minds, other ways that physical systems can be organized to produce knowledge and competence. The emergence of artificial intelligence in mid-twentieth century provided a way to study the powers and limits of systems that learn, to theorize and to make theories sufficiently concrete so that their properties and consequences can be demonstrated. In this updated version of the 1995 MIT Press book Android Epistemology, computer scientists and philosophers—among them Herbert Simon, Daniel Dennett, and Paul Churchland—offer a gentle, unsystematic introduction to alternative systems of cognition. They look at android epistemology from both theoretical and practical points of view, offering not only speculative proposals but applications—ideas for using computational systems to expand human capacities. The accessible and entertaining essays include a comparison of 2001's HAL and today's computers, a conversation among aliens who have a low opinion of human cognition, an argument for the creativity of robots, and a short story illustrating the power of algorithms for learning causal relations.ContributorsNeil Agnew, Margaret Boden, Paul Churchland, Daniel Dennett, Ken M. Ford, Clark Glymour, Pat Hayes, Henry Kyburg, Doug Lenat, Marvin Minsky, Joseph Nadeau, Anatol Rappoport, Herbert Simon, Lynn Andrea Stein, Susan Sterrett

This book presents introductions to DKD and PKD, extensive reviews of the field, and state-of-the-art techniques.Foreword by Vipin Kumar Knowledge discovery and data mining (KDD) deals with the problem of extracting interesting associations, classifiers, clusters, and other patterns from data. The emergence of network-based distributed computing environments has introduced an important new dimension to this problem—distributed sources of data. Traditional centralized KDD typically requires central aggregation of distributed data, which may not always be feasible because of limited network bandwidth, security concerns, scalability problems, and other practical issues. Distributed knowledge discovery (DKD) works with the merger of communication and computation by analyzing data in a distributed fashion. This technology is particularly useful for large heterogeneous distributed environments such as the Internet, intranets, mobile computing environments, and sensor-networks.When the data sets are large, scaling up the speed of the KDD process is crucial. Parallel knowledge discovery (PKD) techniques addresses this problem by using high-performance multiprocessor machines. This book presents introductions to DKD and PKD, extensive reviews of the field, and state-of-the-art techniques. ContributorsRakesh Agrawal, Khaled AlSabti, Stuart Bailey, Philip Chan, David Cheung, Vincent Cho, Joydeep Ghosh, Robert Grossman, Yi-ke Guo, John Hale, John Hall, Daryl Hershberger, Ching-Tien Ho, Erik Johnson, Chris Jones, Chandrika Kamath, Hillol Kargupta, Charles Lo, Balinder Malhi, Ron Musick, Vincent Ng, Byung-Hoon Park, Srinivasan Parthasarathy, Andreas Prodromidis, Foster Provost, Jian Pun, Ashok Ramu, Sanjay Ranka, Mahesh Sreenivas, Salvatore Stolfo, Ramesh Subramonian, Janjao Sutiwaraphun, Kagan Tummer, Andrei Turinsky, Beat Wüthrich, Mohammed Zaki, Joshua Zhang

A state-of-the-art survey of recent advances in data mining or knowledge discovery.Data mining, or knowledge discovery, has become an indispensable technology for businesses and researchers in many fields. Drawing on work in such areas as statistics, machine learning, pattern recognition, databases, and high performance computing, data mining extracts useful information from the large data sets now available to industry and science. This collection surveys the most recent advances in the field and charts directions for future research. The first part looks at pervasive, distributed, and stream data mining, discussing topics that include distributed data mining algorithms for new application areas, several aspects of next-generation data mining systems and applications, and detection of recurrent patterns in digital media. The second part considers data mining, counter-terrorism, and privacy concerns, examining such topics as biosurveillance, marshalling evidence through data mining, and link discovery. The third part looks at scientific data mining; topics include mining temporally-varying phenomena, data sets using graphs, and spatial data mining. The last part considers web, semantics, and data mining, examining advances in text mining algorithms and software, semantic webs, and other subjects.

An introduction to the use of computer simulation in studying organizational behavior.An organization is more than the sum of its parts, and the individual components that function as a complex social system can be understood only by analyzing their collective behavior. This book shows how state-of-the-art simulation methods, including genetic algorithms, neural networks, and cellular automata, can be brought to bear on central problems of organizational theory related to the emergence, permanence, and dissolution of hierarchical macrostructures. The emphasis is on the application of a new generation of equation- and agent-based computational models that can help students of organizations to reformulate their basic research questions starting from assumptions about how to link—rather than separate—different levels of organizational analysis.