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Mluri Reasoning about the Environment Chris Skelsey Keith Matthews Macaulay Land Use Research Institute.

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Presentation on theme: "Mluri Reasoning about the Environment Chris Skelsey Keith Matthews Macaulay Land Use Research Institute."— Presentation transcript:

1 mluri Reasoning about the Environment Chris Skelsey Keith Matthews Macaulay Land Use Research Institute

2 mluri Land Cover Data Describes nature of land cover over some geographical area Arable Heather moor Coniferous- plantation 01 km Varied uses

3 mluri 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 mluri Established Software GIS and remote sensing packages –Arc/Info –Smallworld –ER-Mapper –PCI –Erdas Imagine

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

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

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

10 mluri A Map Revision Problem Land Cover of Scotland (1988) dataset Requires revision 01 km Arable Heather moor Coniferous- plantation

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

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

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

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


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