Analysing partner selection through exchange values

Dynamic and resource-constrained environments raise interesting issues for partnership formation and multi-agent systems. In a scenario in which agents interact with each other to exchange services, if computational resources are limited, agents cannot always accept a request, and may take time to find available partners to delegate their needed services. Several approaches are available to solve this problem, which we explore through an experimental evaluation in this paper. In particular, we provide a computational implementation of Piaget's exchange-values theory, and compare its performance against alternatives

The Distribution of the Elements in the Galactic Disk III. A Reconsideration of Cepheids from l = 30 to 250 Degrees

This paper reports on the spectroscopic investigation of 238 Cepheids in the northern sky. Of these stars, about 150 are new to the study of the galactic abundance gradient. These new Cepheids bring the total number of Cepheids involved in abundance distribution studies to over 400. In this work we also consider systematics between various studies and also those which result from the choice of models. We find systematic variations exist at the 0.06 dex level both between studies and model atmospheres. In order to control the systematic effects our final gradients depend only on abundances derived herein. A simple linear fit to the Cepheid data from 398 stars yields a gradient d[Fe/H]/dRG = -0.062 \pm 0.002 dex/kpc which is in good agreement with previously determined values. We have also reexamined the region of the "metallicity island" of Luck et al. (2006). With the doubling of the sample in that region and our internally consistent abundances, we find there is scant evidence for a distinct island. We also find in our sample the first reported Cepheid (V1033 Cyg) with a pronounced Li feature. The Li abundance is consistent with the star being on its red-ward pass towards the first giant branch.Comment: 66 pages including tables, 12 figures, Accepted Astronomical Journa

An efficient and versatile approach to trust and reputation using hierarchical Bayesian modelling

In many dynamic open systems, autonomous agents must interact with one another to achieve their goals. Such agents may be self-interested and, when trusted to perform an action, may betray that trust by not performing the action as required. Due to the scale and dynamism of these systems, agents will often need to interact with other agents with which they have little or no past experience. Each agent must therefore be capable of assessing and identifying reliable interaction partners, even if it has no personal experience with them. To this end, we present HABIT, a Hierarchical And Bayesian Inferred Trust model for assessing how much an agent should trust its peers based on direct and third party information. This model is robust in environments in which third party information is malicious, noisy, or otherwise inaccurate. Although existing approaches claim to achieve this, most rely on heuristics with little theoretical foundation. In contrast, HABIT is based exclusively on principled statistical techniques: it can cope with multiple discrete or continuous aspects of trustee behaviour; it does not restrict agents to using a single shared representation of behaviour; it can improve assessment by using any observed correlation between the behaviour of similar trustees or information sources; and it provides a pragmatic solution to the whitewasher problem (in which unreliable agents assume a new identity to avoid bad reputation). In this paper, we describe the theoretical aspects of HABIT, and present experimental results that demonstrate its ability to predict agent behaviour in both a simulated environment, and one based on data from a real-world webserver domain. In particular, these experiments show that HABIT can predict trustee performance based on multiple representations of behaviour, and is up to twice as accurate as BLADE, an existing state-of-the-art trust model that is both statistically principled and has been previously shown to outperform a number of other probabilistic trust models

Charge Detection in a Closed-Loop Aharonov-Bohm Interferometer

We report on a study of complementarity in a two-terminal "closed-loop" Aharonov-Bohm interferometer. In this interferometer, the simple picture of two-path interference cannot be applied. We introduce a nearby quantum point contact to detect the electron in a quantum dot inserted in the interferometer. We found that charge detection reduces but does not completely suppress the interference even in the limit of perfect detection. We attribute this phenomenon to the unique nature of the closed-loop interferometer. That is, the closed-loop interferometer cannot be simply regarded as a two-path interferometer because of multiple reflections of electrons. As a result, there exist indistinguishable paths of the electron in the interferometer and the interference survives even in the limit of perfect charge detection. This implies that charge detection is not equivalent to path detection in a closed-loop interferometer. We also discuss the phase rigidity of the transmission probability for a two-terminal conductor in the presence of a detector.Comment: 4 pages with 4 figure

TRAVOS: Trust and Reputation in the Context of Inaccurate Information Sources

In many dynamic open systems, agents have to interact with one another to achieve their goals. Here, agents may be self-interested, and when trusted to perform an action for another, may betray that trust by not performing the action as required. In addition, due to the size of such systems, agents will often interact with other agents with which they have little or no past experience. There is therefore a need to develop a model of trust and reputation that will ensure good interactions among software agents in large scale open systems. Against this background, we have developed TRAVOS (Trust and Reputation model for Agent-based Virtual OrganisationS) which models an agent's trust in an interaction partner. Specifically, trust is calculated using probability theory taking account of past interactions between agents, and when there is a lack of personal experience between agents, the model draws upon reputation information gathered from third parties. In this latter case, we pay particular attention to handling the possibility that reputation information may be inaccurate