BACTERIOPHAGE PHI-KP MEDIATED GENERALIZED TRANSDUCTION IN ERWINIA-CAROTOVORA SUBSPECIES CAROTOVORA

The bacteriophage phiKP is capable of generalized transduction in Erwinia carotovora subspecies carotovora (Ecc) strains SCRI193 and ATCC 39048. PhiKP is a virulent phage containing double stranded DNA of approximately 46 kb. The frequencies of transduction were established for a number of chromosomal markers and plasmid pHCP2, and conditions for transduction optimized after exposure of the phage lysate to UV irradiation

Incorporating mitigating circumstances into reputation assessment

Reputation enables customers to select between providers, and balance risk against other aspects of service provision. For new providers that have yet to establish a track record, negative ratings can significantly impact on their chances of being selected. Existing work has shown that malicious or inaccurate reviews, and subjective differences, can be accounted for. However, an honest balanced review of service provision may still be an unreliable predictor of future performance if the circumstances differ. Specifically, mitigating circumstances may have affected previous provision. For example, while a delivery service may generally be reliable, a particular delivery may be delayed by unexpected flooding. A common way to ameliorate such effects is by weighting the influence of past events on reputation by their recency. In this paper, we argue that it is more effective to query detailed records of service provision, using patterns that describe the circumstances to determine the significance of previous interactions

Context-driven Assessment of Provider Reputation in Composite Provision Scenarios

Service-oriented computing has become the de-facto way of developing distributed applications and, in such systems, an accurate assessment of reputation is essential for selecting between alternative providers. Existing methods typically assess reputation on a combination of direct experiences by the client being provided with a service and third party recommendations, but they exclude from consideration a wealth of information about the context of providers' previous actions. Such information is particularly important in composite service provision scenarios, where providers may delegate sub-tasks to others, and thus their success or failure needs to be interpreted in this context and reputation assessed according to responsibility. In response, to enable richer, more accurate reputation mechanisms, this paper models the delegation knowledge underlying a composite service provision, and incorporates such knowledge into the reputation assessment process, adjusting the contributions of past interactions with the composite service provider according to delegation context relevance. Experimental results demonstrate the effectiveness of the proposed approach

Decentralized Bayesian reinforcement learning for online agent collaboration

Solving complex but structured problems in a decentralized manner via multiagent collaboration has received much attention in recent. years. This is natural, as on one hand, multiagent systems usually possess a structure that determines the allowable interactions among the agents; and on the other hand, the single most pressing need in a cooperative multiagent system is to coordinate the local policies of autonomous agents with restricted capabilities to serve a system-wide goal. The presence of uncertainty makes this even more challenging, as the agents face the additional need to learn the unknown environment parameters while forming (and following) local policies in an online fashion. In this paper, we provide the first Bayesian reinforcement learning (BRL) approach for distributed coordination and learning in a cooperative multiagent system by devising two solutions to this type of problem. More specifically, we show how the Value of Perfect Information (VPI) can be used to perform efficient decentralised exploration in both model-based and model-free BRL, and in the latter case, provide a closed form solution for VPI, correcting a decade old result by Dearden, Friedman and Russell. To evaluate these solutions, we present experimental results comparing their relative merits, and demonstrate empirically that both solutions outperform an existing multiagent learning method, representative of the state-of-the-art. Copyright © 2012, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved

CONOISE-G: Agent-based virtual organisations for the Grid

The ability to create reliable and scalable virtual organisations (VOs) on demand in a dynamic, open and competitive environment is one of the challenges that underlie Grid computing. In response, in the CONOISE-G project, we are developing an infrastructure to support robust and resilient virtual organisation formation and operation. Specifically, CONOISE-G provides mechanisms to assure effective operation of agent-based VOs in the face of disruptive and potentially malicious entities in dynamic, open and competitive environments. In this paper, we describe the CONOISE-G system, outline its use in the context of VO formation and perturbation, and review current efforts to progress the work to deal with unreliable information sources

Monitoring, Policing and Trust for Grid-Based Virtual Organisations

A key challenge in Grid Computing is the ability to create reliable and scalable virtual organisations (VOs) which operate in an open, dynamic and competitive environment. In response, in the CONOISE-G project, we are developing an infrastructure to support robust and resilient virtual organisation formation and operation. Specifically, CONOISE-G provides mechanisms to assure effective operation of agent-based VOs in the face of disruptive and potentially malicious entities in dynamic, open and competitive environments. In this paper, we describe the architecture of the CONOISE-G system, and provide details of its implementation