Prediction of intent in robotics and multi-agent systems.

Moving beyond the stimulus contained in observable agent behaviour, i.e. understanding the underlying intent of the observed agent is of immense interest in a variety of domains that involve collaborative and competitive scenarios, for example assistive robotics, computer games, robot-human interaction, decision support and intelligent tutoring. This review paper examines approaches for performing action recognition and prediction of intent from a multi-disciplinary perspective, in both single robot and multi-agent scenarios, and analyses the underlying challenges, focusing mainly on generative approaches

Interconnection alternatives for hierarchical monitoring communication in parallel SoCs

Interconnection architectures for hierarchical monitoring communication in parallel System-on-Chip (SoC) platforms are explored. Hierarchical agent monitoring design paradigm is an efficient and scalable approach for the design of parallel embedded systems. Between distributed agents on different levels, monitoring communication is required to exchange information, which forms a prioritized traffic class over data traffic. The paper explains the common monitoring operations in SoCs, and categorizes them into different types of functionality and various granularities. Requirements for on-chip interconnections to support the monitoring communication are outlined. Baseline architecture with best-effort service, time division multiple access (TDMA) and two types of physically separate interconnections are discussed and compared, both theoretically and quantitatively on a Network-on-Chip (NoC)-based platform. The simulation uses power estimation of 65 nm technology and NoC microbenchmarks as traffic traces. The evaluation points out the benefits and issues of each interconnection alternative. In particular, hierarchical monitoring networks are the most suitable alternative, which decouple the monitoring communication from data traffic, provide the highest energy efficiency with simple switching, and enable flexible reconfiguration to tradeoff power and performance. (C) 2009 Elsevier B.V. All rights reserved

An approach to control collaborative processes in PLM systems

Companies that collaborate within the product development processes need to implement an effective management of their collaborative activities. Despite the implementation of a PLM system, the collaborative activities are not efficient as it might be expected. This paper presents an analysis of the problems related to the collaborative work using a PLM system. From this analysis, we propose an approach for improving collaborative processes within a PLM system, based on monitoring indicators. This approach leads to identify and therefore to mitigate the brakes of the collaborative work

Hierarchical Agent-based Adaptation for Self-Aware Embedded Computing Systems

Siirretty Doriast

DEPAS: A Decentralized Probabilistic Algorithm for Auto-Scaling

The dynamic provisioning of virtualized resources offered by cloud computing infrastructures allows applications deployed in a cloud environment to automatically increase and decrease the amount of used resources. This capability is called auto-scaling and its main purpose is to automatically adjust the scale of the system that is running the application to satisfy the varying workload with minimum resource utilization. The need for auto-scaling is particularly important during workload peaks, in which applications may need to scale up to extremely large-scale systems. Both the research community and the main cloud providers have already developed auto-scaling solutions. However, most research solutions are centralized and not suitable for managing large-scale systems, moreover cloud providers' solutions are bound to the limitations of a specific provider in terms of resource prices, availability, reliability, and connectivity. In this paper we propose DEPAS, a decentralized probabilistic auto-scaling algorithm integrated into a P2P architecture that is cloud provider independent, thus allowing the auto-scaling of services over multiple cloud infrastructures at the same time. Our simulations, which are based on real service traces, show that our approach is capable of: (i) keeping the overall utilization of all the instantiated cloud resources in a target range, (ii) maintaining service response times close to the ones obtained using optimal centralized auto-scaling approaches.Comment: Submitted to Springer Computin