Moravec's estimates of animal equivalence jostling of the atoms in a Moravec's strengths--his insightful are based solely on hardware rock can be seen as the operation of a complete, self-aware mind data analysis, extrapolation of technology complexity. It is often the case that (after Evert) (Everett, H., Many-to extreme conclusions, and hardware alone cannot deliver performance, Worlds of Interpretation/ Quantum provocative predictions--are all here but it also requires software Mechanics, Princeton University and will probably gain him some new sufficient to the task.

It is possible to argue, relatively convincingly, that any research topic only begins to become mature when it appears on a syllabus somewhere. Once the topic has become well enough understood that it can be explained easily to paying customers, and stable enough that anyone teaching it is not likely to have to update his/her teaching materials every few months as new developments are reported, it can be considered to have arrived. Another reasonable indicator of the maturity of a subject, a milestone along the road to academic respectability, is the publication of a really good book on the subject -- not another research monograph but a book that consolidates what is already known, surveys and relates existing ideas, and maybe even unifies some of them. Grigoris Antoniou's Nonmonotonic Reasoning is just such a milestone -- well written, informative, and a good source of information on an important and complex subject. Since the idea was first mooted

AI has embraced medical applications from its inception, and some of the earliest work in successful application of AI technology occurred in medical contexts. Medicine in the twenty-first century will be very different than medicine in the late twentieth century. Fortunately, the technical challenges to AI that emerge are similar, and the prospects for success are high.

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, held the 1998 Spring Symposium Series on 23 to 25 March at Stanford University. The topics of the eight symposia were (1) Applying Machine Learning to Discourse Processing, (2) Integrating Robotic Research: Taking the Next Leap, (3) Intelligent Environments, (4) Intelligent Text Summarization, (5) Interactive and Mixed-Initiative Decision-Theoretic Systems, (6) Multimodal Reasoning, (7) Prospects for a Common-Sense Theory of Causation, and (8) Satisficing Models.

Calendar of upcoming AI meetings, conferences, and other events.

It is a medium for disseminating information like to thank the Publications Committee for selecting me about AI areas and methods to readers across the entire as Editor, and especially to thank the Publications Committee field of AI, as well as to a broad multidisciplinary audience. Chair, Kenneth Ford, the previous Editor, Jude Shavlik, It is a journal of record for articles on important research and Publishing Consultant Mike Hamilton, for their warm and applications advances as well as for meeting reports, reviews, welcome, help, and very valuable guidance as I have embarked and discussions that illuminate the state of the art on the editorship. I am delighted to begin my term and emerging areas. Equally important, it is a forum for as Editor and look forward to working with the many who sharing visions for the field--perspectives on issues, priorities, contribute to AI Magazine's success. Indiana University helps to shape the future of artificial intelligence.

The Association for the Advancement of Artificial Intelligence (AAAI) held its 1998 Fall Symposium Series on 23 to 25 October at the Omni Rosen Hotel in Orlando, Florida. This article contains summaries of seven of the symposia that were conducted: (1) Cognitive Robotics; (2) Distributed, Continual Planning; (3) Emotional and Intelligent: The Tangled Knot of Cognition; (4) Integrated Planning for Autonomous Agent Architectures; (5) Planning with Partially Observable Markov Decision Processes; (6) Reasoning with Visual and Diagrammatic Representations; and (7) Robotics and Biology: Developing Connections.

An ensemble consists of a set of individually trained classifiers (such as neural networks or decision trees) whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble is often more accurate than any of the single classifiers in the ensemble. Bagging (Breiman, 1996c) and Boosting (Freund & Shapire, 1996; Shapire, 1990) are two relatively new but popular methods for producing ensembles. In this paper we evaluate these methods on 23 data sets using both neural networks and decision trees as our classification algorithm. Our results clearly indicate a number of conclusions. First, while Bagging is almost always more accurate than a single classifier, it is sometimes much less accurate than Boosting. On the other hand, Boosting can create ensembles that are less accurate than a single classifier -- especially when using neural networks. Analysis indicates that the performance of the Boosting methods is dependent on the characteristics of the data set being examined. In fact, further results show that Boosting ensembles may overfit noisy data sets, thus decreasing its performance. Finally, consistent with previous studies, our work suggests that most of the gain in an ensemble's performance comes in the first few classifiers combined; however, relatively large gains can be seen up to 25 classifiers when Boosting decision trees.

This paper presents a new approach to identifying and eliminating mislabeled training instances for supervised learning. The goal of this approach is to improve classification accuracies produced by learning algorithms by improving the quality of the training data. Our approach uses a set of learning algorithms to create classifiers that serve as noise filters for the training data. We evaluate single algorithm, majority vote and consensus filters on five datasets that are prone to labeling errors. Our experiments illustrate that filtering significantly improves classification accuracy for noise levels up to 30 percent. An analytical and empirical evaluation of the precision of our approach shows that consensus filters are conservative at throwing away good data at the expense of retaining bad data and that majority filters are better at detecting bad data at the expense of throwing away good data. This suggests that for situations in which there is a paucity of data, consensus filters are preferable, whereas majority vote filters are preferable for situations with an abundance of data.

The recent advances in computer speed and algorithms for probabilistic inference have led to a resurgence of work on planning under uncertainty. The aim is to design AI planners for environments where there might be incomplete or faulty information, where actions might not always have the same results, and where there might be trade-offs between the different possible outcomes of a plan. Addressing uncertainty in AI, planning algorithms will greatly increase the range of potential applications, but there is plenty of work to be done before we see practical decision-theoretic planning systems. This article outlines some of the challenges that need to be overcome and surveys some of the recent work in the area.