System analysis of a Peer-to-Peer Video-on-Demand architecture: Kangaroo

Architectural design and deployment of Peer-to-Peer Video-on-Demand (P2PVoD) systems which support VCR functionalities is attracting the interest of an increasing number of research groups within the scientific community; especially due to the intrinsic characteristics of such systems and the benefits that peers could provide at reducing the server load. This work focuses on the performance analysis of a P2P-VoD system considering user behaviors obtained from real traces together with other synthetic user patterns. The experiments performed show that it is feasible to achieve a performance close to the best possible. Future work will consider monitoring the physical characteristics of the network in order to improve the design of different aspects of a VoD system.

Efficient learning of reactive robot behaviors with a Neural-Q_learning approach

The purpose of this paper is to propose a Neural-Q_learning approach designed for online learning of simple and reactive robot behaviors. In this approach, the Q_function is generalized by a multi-layer neural network allowing the use of continuous states and actions. The algorithm uses a database of the most recent learning samples to accelerate and guarantee the convergence. Each Neural-Q_learning function represents an independent, reactive and adaptive behavior which maps sensorial states to robot control actions. A group of these behaviors constitutes a reactive control scheme designed to fulfill simple missions. The paper centers on the description of the Neural-Q_learning based behaviors showing their performance with an underwater robot in a target following task. Real experiments demonstrate the convergence and stability of the learning system, pointing out its suitability for online robot learning. Advantages and limitations are discussed