intelligent agents in the australian bureau of meteorology sandy dance and mal gorman
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Intelligent Agents in the Australian Bureau of
MeteorologySandy Dance and Mal Gorman
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Introduction• About the Bureau of Meteorology
• Project to improve forecast process
• Alerts
• Agents in Bureau
• TAF alert pilot project
• Research issues
• The future
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New Bureau building in March 2004, 700 Collins St, Docklands.
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Forecast “Database”
• Machine-readable forecasts in database• Forecaster “personal digital assistant” (PDA)• Automatic alerting• Multi “view” product generation• Integration of existing systems
A project to enhance the forecasting process,
involving:
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radar
satellite
model
AWS
db1 db2 db3
products
Forecast DB – stage 1
interfaces
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Intelligent Alerts Goals
• Forecaster PDA
• Alerts from inconsistency between Forecast / Guidance / Observations
• Weather element alerts, eg temp
• Severe weather event alerts, eg hail
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Forecaster PDA
• Manage alerts
• Sanity check for forecasts (“deviates from climate”)
• Arrival alerts (ie, latest model, satellite images)
• ‘elephant stamps’ for successful unusual forecasts
• Automatic text generation for various forecast types
• Graphical editing of numerical forecast
• Control of alerting through media such as SMS, email, phone
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Consistency Alerts
Inter-comparison between:
• Forecasts and observations (verification),
• Observations and guidance,
• Guidance and forecasts.
(guidance = numerical atmospheric model)
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Severe weather alerts• Storm alerts from radar
• Microburst from radar
• Tornado from radar
• Hail from radar
• Lightning from radar and GPATS
• Fronts from satellite
….this is not exhaustive!
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Forecast DB - with agents
radar
satellite
model
AWS
db1 db2 db3
Microburstdetector
frontdetector
??detector
??detector ???
Storm track
?? alert
Cold front
forecast
special
warning
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…and again in more detail.
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An example of an agent –based detector: microburst detection
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Reflectivity output showing detected microbursts
(see www.bom.gov.au/weather/radar/ for more radar)
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Exploratory pilot projectTo trial an end-to-end system employing Jack agents to alert on
discrepancies between aviation forecasts and observations.
• Inputs: TAF (forecast) and AWS (observation) data from decoders
• Passed by TCP/IP and Jacob to Jack agent network
• An agent handles subscription to data of interest by other agents
• A monitoring agent issues alerts upon discrepancies between TAF and AWS data
• GUI subscribes to alerts and displays them under control of forecaster.
Conducted in collaboration with RMIT Agents Group and Agent Oriented Software Pty Ltd.
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TAF YMML 122218Z 0024
24006KT 9999 FEW025 BKN030
FM02 18015KT 9999 SCT040
FM17 25006KT 9999 BKN025
T 15 19 20 16 Q 1028 1026 1025 1026
A typical TAF
A typical AWS
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Alerting agent pilot
Data flow view of pilot agent network.
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Research issues raisedThe wish list from the Bureau, plus experience from the pilot
project, highlight our requirements for a large scale Bureau agent network. These include:
• Self-describing data• Service description• Service lookup• Failure handling• Dynamic quality-of-service managementThese are research issues that will be dealt with in a possible
ARC Linkage grant in association with RMIT and AOS.
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Self-describing data
• Allows agents to interpret data from elsewhere sensibly
• Allows reasoning about data
• Allows translation between related concepts.
Could use our in-house metadata-rich tree-table-xml.
Or more generally, an object model that can represent rich agent-oriented semantics and ontologies with data.
A research question!
We require a data representation that:
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Service Description
• Services will need to be advertised and searched.
• Must allow efficient reasoning about services,
• Must express the data provided, the transformations made, and the quality of the data and service.
Could use technologies like DAML+OIL*, or extensions or alternatives to these. Again an open research question.
* DARPA agent markup language, ontology inference language
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Service lookup
• Must allow new services to compete with old
• Handle data source failure or removal by seeking alternatives
• Handle vastly different temporal characteristics of data sources
Agents will need to seek data sources upon startup, as well as continuously during operation.
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The future• Extend the pilot to more stations, datatypes, forecast types,
alerting scenarios.
• Merge with forecaster GUI under development
• Incorporate severe weather detectors into the network.
• Pursue research issues to give us agents that can find and talk to each other – possible ARC Linkage grant!
• Gradually infiltrate agents throughout the Bureau.