Alasdair Allan
eSTAR Project
School of Physics
University of Exeter
Stocker Road
Exeter
EX4 4QL

Tel. +44-1392-264160
Email aa@astro.ex.ac.uk
Web http://www.astro.ex.ac.uk/people/aa/

VOTech Kick Off Meeting

Agent Architectures: Smarter software for astronomers

AlasdairAllan, eSTAR Project, University of Exeter, Exeter, U.K.

Abstract

In the real world, people rarely interact with other people, or data, in a hierarchical manner. If they do, they lose time and efficiency.

It's not necessary to have an global view of topology of the data, so long as you know about some of it, and know other people who know the rest. The emerging VO architecture can leverage the peer-to-peer nature of agent based solutions to make the software smarter, more efficient and more responsive to the user. The purpose of an agent is to know what a user wants, even if they don't know themselves. Establishing architectures and building agent based solutions for the VO will flatten the network topology, enabling links between related datasets to form more quickly, and make the resulting network more robust.

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An Agent-based Predictive Engine for Variable Star Observing

Eric Saunders, eSTAR Project, University of Exeter, Exeter, U.K.

We consider the problem of designing and implementing an autonomous intelligent agent that can make the most efficient use of the finite amount of telescope time available to an observer. Such an agent must be able to robustly quantify how well an observing schedule is proceeding and thus make changes, in real time, to optimise such a schedule. We present a system of metrics that will form the predictive algorithmic engine for the eSTAR intelligent agents. We discuss the broader applicability to the field of variable star observation.

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When are Agents good data miners?

Tim Naylor, eSTAR Project, University of Exeter, Exeter, UK

By exploiting the emerging VO architecture, agent technologies can offer interesting data mining capabilities. We describe the collaborative agent model eSTAR has used to interact with grid enabled telescopes, and how this model can be applied to more general data mining problems. We discuss use cases where this model has a clear advantage.

Download Power Point Presentation (564k)

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