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Reasoning about the Environment

Reasoning about the Environment. Chris Skelsey Keith Matthews. Macaulay Land Use Research Institute. 0. 1. km. Arable. Heather moor. Coniferous- plantation. Land Cover Data. Describes nature of land cover over some geographical area. Varied uses. Land Cover Data.

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Reasoning about the Environment

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  1. Reasoning about the Environment ChrisSkelsey Keith Matthews Macaulay Land Use Research Institute

  2. 0 1 km Arable Heather moor Coniferous- plantation Land Cover Data • Describes nature of land cover over some geographical area • Varied uses

  3. Land Cover Data • Derived from an interpretation of varied data and information sources • Aerial photographs • Soil maps • Knowledge of local management • Knowledge of seasonal cycles • Satellite imagery • Can automation play a more significant role?

  4. Established Software • GIS and remote sensing packages • Arc/Info • Smallworld • ER-Mapper • PCI • Erdas Imagine

  5. Problem Complexity • Procedural, quantitative functionality 1988 Areas of forest-felling 1995

  6. Artificial Intelligence (AI) • Production rules • Frame systems • Semantic networks • Neural networks • Fuzzy logic • Dempster-Shafer theories

  7. Limitations of AI Approaches • Data-specific and method-specific • Single software environment • Real-world domain complexities prevent these applications “scaling-up” • Most remain within the research community • Still need greater software flexibility

  8. A Prototype AI Toolkit • ETORA • Developed within G2 • Blackboard reasoning • Re-use of established software • ARC/INFO and PV-WAVE servers • Implementation of endorsement theory

  9. Disparate multi-source data Quantitative and qualitative knowledge Dynamic solution strategies Use of 3rd-party, established software Full reasoning explanations, associated with end-products A Prototype AI Toolkit

  10. 0 1 km A Map Revision Problem • Land Cover of Scotland (1988) dataset • Requires revision Arable Heather moor Coniferous- plantation

  11. “difficult access; >20m from forest boundary” “large enough to be completed felling” “may be a track; one exists within 20m” A Map Revision Problem • SYMOLAC: solves a simple problem, but demonstrates the flexibility of ETORA • Produces a useful product despite real-world complexities

  12. In Summary • Automation is becoming increasingly important • Recognised need for AI technology • AI approaches are often problem-specific, or adopt unsuitable software platforms

  13. Some Conclusions • Prototype ETORA toolkit offers flexibility to solution designer • Exists potential to automate a greater number of processes involved in land cover data production

  14. LADSS • Land Allocation Decision Support System • Evaluates economic impacts of land use strategies • Use of genetic algorithms • Bridge to the Smallworld GIS

  15. Why use G2? • Flexible knowledge-representation • Object-orientated concepts • Ability to visualise the reasoning processes • G2-Gateway

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