1. An Intelligent Agent-based Architecture for Internet Mapping and Distributing Geographic Information Services By Ming-Hsiang Tsou E-mail: mtsou@mail.sdsu.edumtsou@mail.sdsu.edu Phone: 619-5940205 Fax: 619-5944938 AAG 2002, Los Angeles The Department of Geography, San Diego State University

2. The Promise of Internet Mapping Flexible Information Access/Distribution (Spatial Information at your fingertips + Real time data) Information Sharing and Integration (Access multiple Internet map servers at the same time – local data, federal agencies, USGS, EPA, Census etc.) Distributed Mapping/GIS Tools: Web Services (LEGO-like GIS components/mapping tools) 3D, Visualization, Networking Analysis, Hydrological models The Network is Your Mapping Factory

3. Problems in Internet Mapping Temporary, technology-centered solutions: The lack of an intelligent architecture which can operate in complicated mapping situations and new/unknown environments. Focus on Databases, not on Map Presentation: We need to create a new mechanism for connecting cartographic knowledge with Internet mapping applications.

4. Information Overload Access hundreds of map layers from different servers How to combine Map-Server-A layers With Map-Server-B layers? Change Symbols? Apply Color Scheme? Create scale threshold? Use Different Projections?

5. Web Map Users Don’t Have Sufficient Cartographic Knowledge Current Web Maps: Good Interactivity Good Flexibility Poor Quality Poor Design Web Map Users ≠ GIS Users

6. Possible Solution: Intelligent Software Agents Apply cartographic principles to web mapping Software Agents (Cartographers pre-defined)+ User defined rules Establish cartographic rules dynamically (Different tasks have different rules and knowledge base) Create distributed cartography knowledge base (CKB) (Access/Distribute different rules and symbols, color schemes, layout.. via the software agent network)

7. Software Agents Info. finders/filters Interpreters Decision makers User-defined rules Cartography Knowledge Bases Agent collaboration Agent-based Communication GIS Components (Programs) Geodata Objects Metadata Design

8. Information Finder / Filter Information Finder ------------------------ Information Filter Carto-rules Rule-2 Rule-3 ~ ~ ~ ~ ~ Rule- 4 Color-scheme = DEM Iso-line rules Interpolation Algorithm KEYWORDS (User-defined rules) Users Color Scheme Landuse-color DEM-color ~ ~ ~ ~ ~ Zoning-color Site-B Site-A Generalization Method-1 Method-2 ~ ~ ~ ~ ~ Method-400 Site-C Examples: Search “Color Scheme” for Digital Elevation Model. Search Methods: 1. Message Broadcasting 2. Agent Roaming 3. Create a “Metadata Repository” to improve the search efficiency

9. Information Interpreter Data-1 (UTM) Metadata [Buffering] in UNIX Information Interpreter Metadata [Address matching] in Window 2000 Metadata Data-2 (SPCS) Metadata Data-3 (Lat/Lon) Metadata Metadata becomes the source of knowledge bases Automatically convert from “UTM” coordinate systems to “SPCS California VI” by accessing the metadata of GIS data objects. Transform map units from feet to meters. Transform data from ESRI Shapefiles to AutoDesk SDFs.

10. The Design of Operational Metadata Map display component GIS-operation requirements (A, B) System metadata OtherGIS components GeoData Object Metadata (A, B, C, D, E, F) Metadata describe how the objects should be represented (color, symbols) and the domain of the object (vector, line, transportation). Integrating Self-describing, Self-managing map layers

11. Agent (decision maker) Decision Maker Events Actions Agent (Information Finder) Agent (Information Interpreter) Agent Collaboration Event: If a new [polygon] data layer is added into point data layers. Agent Collaboration: Info. Finder --> search for cartographic rules Interpreter --> convert cartographic rules into executable procedures. Action: Move [point] data layers above the polygon layer.

12. Software Agents User-defined rules (Cartographic rules) Task #1 (Client Machine–A) (Working memory) Task #2 (Client Machine–B) (Working memory) GIS data and components framework GIS Component (Buffering) Metadata (Facts) GIS data object (Road, Colorado) Metadata (Facts) User Interface Inference engine RulesFacts Working memory Traditional Expert Systems Intelligent software agent

13. Generalization Hierarchy of GeoAgents GeoAgent Machine Agent Component Agent Geodata Agent UML notations Stationary GeoAgent Mobile GeoAgent UML: Unified Modeling Language Carto Agent

14. Walk-through Example Sd_pointofinterest Metadata: Carto-Type: Point Symbols: star Color: red Size: 7 point Scale threshold: 1:20,000- 1:10,000 Carto Agents retrieve the operational metadata from data objects and apply it on the map design.

15. Dynamic Cartographic Design Carto Agents re-arrange the layer sequences and reassign new color scheme for the landuse layer Add a new landuse layer (metadata: color = blue) Conflict with current sd_conven layer (same color: blue) Overlap other information

16. Cartography Ontology (Cartography Knowledge Base – CKB ) Statements: All point layers should be above all polygon layers. First-Order Logic: Computer Program: number  layout sequences (1:top, 2:second..) polylayer(x).number = i pointlayer(y).number = j If (i < j) then { polylayer(x).number = j pointlayer(y).number = i } Above( PointLayer(x), PolyLayer(y) )

17. Combining Metadata and Rules Cartographic Rule: If the color of the new polygon layer is the same as one of the existing layers, carto-agents will change the color of new layer to a unique color. Computer Program: Color[AllPolyLayers] = [blue, red, green] Color(NewLayer) = NewLayer.metadata.color While ( Color[AllLayers] contain Color(NewLayer) ) { Color(NewLayer) = Color(Randam) }

18. Inference by Multiple Knowledge Bases Multiple Cartographic Knowledge Bases (CKB): Rule#1: “Landuse” data objects are qualitative. (from San Diego State University) http://map.sdsu.edu/001.ckb Rule#2: Color-hue is best visual variable for displaying qualitative area data. (from UC-Santa Barbara) http://geog.ucsb.edu/hydro.ckb) Inference: Rule#1 AND Rule#1  Landuse should use “Color-hue” for area symbol display. Computer Program (Software Agents): Landuse.Symbols = ColorScheme(Hue).Attribute(LU)

19. GIServices Workstation (a GIS node) Agent Container GIS Component Container GeoData Object Container Machine Agent Hardware Profiles: CPU, OS, CRT, printer, scanner Component Agent GeoData Agent M M M M M : metadata M M Implementation: GIService Node Carto Agent

20. Software Agent Platform Java (Sun Microsystems): Java Virtual Machine (VM), Java applets / servlets. CORBA (OMG): Common Object Request Broker Architecture, UNIX-based, IIOP (Internet Inter-ORB Protocol),.NET (Microsoft): Windows 2000/NT, ActiveX container, COM-based model XML (W3C): (Extensible Markup Language) lightweight agent systems, scripting language, open-ended, metadata- enhanced.

21. Agent Communication Language (ACL) / Protocol (ACP) KQML (Knowledge Query and Manipulation Language) (Finin and Weber, 1993) ACL (Agent Communication Language) specification FIPA (Foundation for Intelligent Physical Agents) 1997 IIOP (Internet Inter-ORB Protocol), and CORBA’s Mobile Agent Facility Specification 1.0 OMG, (1999) XML-based scripting language (Lange, Hill, & Oshima, 2000)

22. Current Internet Mapping Intelligent Agent Solution Improve the quality of web maps Create dynamic cartographic design Search for appropriate map styles / color schemes. Establish distributed cartographic knowledge bases. Poor quality of maps - No cartographic principles Problems with multiple data/layer presentation Difficult to apply color schemes / map styles Unknown situation for mapping new data objectsSUMMARY

23. FUTURE WORK Implementation of Cartography Ontology Convert from “logics and rules” to “computer languages” New Cartographic Principles for New Tasks 3D rules, Layer Transparency, Animation rules, etc. Other A.I. possibility for software agents? Fuzzy logic for scale threshold?, Probabilistic theory for Data uncertainty representation? Neural networks for _______???, PowerPoint Slide is available: http://map.sdsu.edu