Peer-to-peer file-sharing networks are currently receiving much attention as a means of sharing and distributing information. However, as recent experience with P2P networks such as Gnutella shows, the anonymous, open nature of these networks offers an almost ideal environment for the spread

The water must be changed The fish must be fed 2. Preservation of an Ideal Environment The water temperature kept constant Bowl cleaned—fungi & molds cleaned away Best feed used 3. Isolated—Artificially Limited Environment Isolated from other fish Artificial/limited living space

Human institutions—ways of organizing activities—affect the resilience of the environ-ment. Locally evolved institutional arrangements governed by stable communities and buffered from outside forces have sustained resources successfully for centuries, al-though they often fail when rapid change

Bayou is a replicated, weakly consistent storage system designed for a mobile computing environment that includes portable machines with less than ideal network connectivity. To maximize availability, users can read and write any accessible replica. Bayou's design has focused on supporting

Modem large retrieval environments tend to overwhelm their users by their large output. Since all documents are not of equal relevance to their users, highly relevant documents should be identified and ranked first for presentation to the users. In order to develop IR techniques to this direction

The possibility of performing data assimilation using the flow-dependent statistics calculated from an ensemble of short-range forecasts (a technique referred to as ensemble Kalman filtering) is examined in an idealized environment. Using a three-level, quasigeostrophic, T21 model and simulated

Typical reconfigurable machines exhibit shortcomings that make them less than ideal for general-purpose computing. The Garp Architecture combines reconfigurable hardware with a standard MIPS processor on the same die to retain the better features of both. Novel aspects of the architecture

with photographs of a physical model. The procedure is applicable to environments composed of ideal diffuse reflectors and can account for direct illumination from a variety of light sources. The resultant surface intensities are independent of observer position, and thus environments can be preprocessed

By now it is widely accepted that learning a task from scratch, i.e., without any prior knowledge, is a daunting undertaking. Humans, however, rarely attempt to learn from scratch. They extract initial biases as well as strategies how to approach a learning problem from instructions and/or demonstrations of other humans. For learning control, this paper investigates how learning from demonstration can be applied in the context of reinforcement learning. We consider priming the Q-function, the value function, the policy, and the model of the task dynamics as possible areas where demonstrations can speed up learning. In general nonlinear learning problems, only model-based reinforcement learning shows significant speed-up after a demonstration, while in the special case of linear quadratic regulator (LQR) problems, all methods profit from the demonstration. In an implementation of pole balancing on a complex anthropomorphic robot arm, we demonstrate that, when facing the complexities of real signal processing, model-based reinforcement learning offers the most robustness for LQR problems. Using the suggested methods, the robot learns pole balancing in just a single trial after a 30 second long demonstration of the human instructor. 1.

In this paper, we discuss how Go, a strategy game widely played in Asia and other parts of the world, provides a rich, challenging environment for capstone and undergraduate research projects. We first describe the game itself and the characteristics that make it appropriate for more advanced