The idea of a large minimum margin [17] explains the good generalization performance ofAdaBoost in the low noise regime. However, AdaBoost performs worse on noisy tasks [10, 11], such as the iris and the breast cancer benchmark data sets [1]. On the latter tasks, a large margin on all training points cannot be achieved without adverse effects on the generalization error. This experimental observation was supported by the study of [13] where the generalization error of ensemble methods wasbounded by the sum of the fraction of training points which have a margin smaller than some value p, say, plus a complexity term depending on the base hypotheses andp. While this bound can only capture part of what is going on in practice, it nevertheless already conveys the message that in some cases it pays to allow for some points which have a small margin, or are misclassified, if this leads to a larger overall margin on the remaining points. To cope with this problem, it was mandatory to construct regularized variants of AdaBoost, which traded off the number of margin errors and the size of the margin 562 G.Riitsch, B. Sch6lkopf, A. J. Smola, K.-R.

This paper examines the application of reinforcement learning to a wireless communicationproblem. The problem requires that channel utility be maximized while simultaneously minimizing battery usage. We present a solution to this multi-criteria problem that is able to significantly reducepower consumption. The solution uses a variable discount factor to capture the effects of battery usage. 1 Introduction Reinforcement learning (RL) has been applied to resource allocation problems in telecommunications, e.g.,channel allocation in wireless systems, network routing, and admission control in telecommunication networks [1,2, 8, 10]. These have demonstrated reinforcement learningcan find good policies that significantly increase the application reward within the dynamics of the telecommunication problems.

An important issue in neural computing concerns the description of learning dynamics with macroscopic dynamical variables. Recent progresson online learning only addresses the often unrealistic case of an infinite training set. We introduce a new framework to model batch learning of restricted sets of examples, widely applicable toany learning cost function, and fully taking into account the temporal correlations introduced by the recycling of the examples. For illustration we analyze the effects of weight decay and early stopping during the learning of teacher-generated examples.

Decision theory formally solves the problem of rational agents in uncertain worlds if the true environmental probability distribution is known. Solomonoff's theory of universal induction formally solves the problem of sequence prediction for unknown distribution. We unify both theories and give strong arguments that the resulting universal AIXI model behaves optimal in any computable environment. The major drawback of the AIXI model is that it is uncomputable. To overcome this problem, we construct a modified algorithm AIXI^tl, which is still superior to any other time t and space l bounded agent. The computation time of AIXI^tl is of the order t x 2^l.

The Innovative Applications of Artificial Intelligence Conference was held 18-22 July 1999 in Orlando, Florida. Ramasamy Uthurusamy was the Program Chair and Barbara Hayes-Roth was the Program Co-Chair. Although all the IAAI-99 papers and talks were certainly interesting and important, we present in this special issue of AI Magazine only a select subset because of page and other limitations. We include two invited talks and four applications as a snapshot of IAAI-99.

Finally, during knowledge refinement, the models are validated through simulation on paper or with prototyping, and the knowledge bases medicine, car troubleshooting, software are refined. The last of the book's authors domain-specific knowledge, and corrections or extensions to the products has been involved in this effort since standardizing the design and development of earlier ones. Thus, the book of expert systems then became The book is intended for practitioners is particularly interesting to those who the major research problems of the in knowledge management. The have been following their work. KADS methodology, as assets have become commonplace.

Based Search," by Peter Clark, John Thompson, Heather Holmback, and Lizbeth Duncan of the Boeing Co., demonstrated a concept-based search engine using an AI thesaurus with unambiguous control terms and relationships for ontology links for finding relevance when searching for human experts in the field.

I was asked to give a visionary talk about the future applications of Artificial Intelligence technology; but I should warn you that I'm actually not very good as a visionary. Most of my predictions about what will happen in the industry don't come true even though they ought to. So I'm not going to tell you what the future holds; what I will do is to point out some of the technological trends that are at work. The outline of the talk is as follows: I'll start off by looking at the previous IAAI conferences and reflect on what we've learned from them. Then I'll look at what's changing in the hardware base that sets the context for all the computer applications we do. I think that will lead to interesting new viewpoints. Next I'll sketch what applications might arise from this new viewpoint. Finally, I'll discuss how the development of practical applications ought to interact with the scientific enterprise of trying to understand intelligence, in particular, human intelligence.

The Fifth International Conference on Artificial Intelligence Planning and Scheduling (AIPS 2000) was held on 14-17 April 2000 at Breckenridge, Colorado; it was colocated with the Seventh International Conference on Principles of Knowledge Representation and Reasoning (KR2000). This conference brought together researchers working in all aspects of problems in planning, scheduling, planning and learning, and plan execution for dealing with complex problems.

Enabling organizations to capture, share, and apply the collective experience and know-how of their people is seen as fundamental to competing in the knowledge economy. As a result, there has been a wave of enthusiasm and activity centered on knowledge management. To make progress in this area, issues of technology, process, people, and content must be addressed. In this article, we develop a road map for knowledge management. It begins with an assessment of the current state of the practice, using examples drawn from our experience at Schlumberger. It then sketches the possible evolution of technology and practice over a 10-year period. Along the way, we highlight ways in which AI technology, present and future, can be applied in knowledge management systems.