1. An Intelligent Agent-based Architecture for Internet Mapping and Distributing Geographic Information Services By Ming-Hsiang Tsou E-mail: mtsou@mail.sdsu.edu Phone: 619-5940205 Fax: 619-5944938 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. Dynamic Construction (on the Internet) User Scenario: GIS Task  GIS node profile  Network performance GIS user GIS node GIS component (program) Geodata object Solution: Dynamic Architecture for GIServices

7. Dynamic Construction (on the Internet) User Scenario: Map Display [Colorado Roads] GIS user (Mike) GIS node GIS component Geodata object Build GIServices “ on-the-fly ” B C A

8. 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)

9. 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

10. 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

11. 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.

12. 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

13. 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.

14. 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

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

16. Geodata Objects Colorado Roads (Vector) Metadata Colorado DEM (Raster) Metadata Geodata Agents Component Agents Machine Agents Client machine Server machine GIS components Map Display Component Metadata Spatial Analysis Component Metadata AgentFunctionality

17. Agent Mobility Machine-A Stationary Agent-01 Machine-B Stationary Agent-05 Remote Procedure Call Machine-C Mobile Agent-03 Machine-D Mobile Agent-06 Mobile Agent-03 Copy (HTTP) a) Stationary Agent b) Mobile Agent

18. Advantages of a Mobile Agent Reduce network load Upload the agents to remote GIS databases Overcoming network latency Real-time response, agents on the remote site Protocol encapsulation Agent carries “codes” and “messages” Execute asynchronously and automatically More stable in fragile network connections Dynamically adoption Agent senses the execution environment and reacts autonomously to change

19. Problems of Mobile Agents Security (Mobile Agents as “Virus”) Implementation (Cross platforms/technologies) Size and Diversity (Small programs, more functions) Protocol Development (Agent communication) Levels of Control (Behavior, location)

20. Security Model for Agents Security Treats: Disclosure of information (interception) Denial of service (DOS) Corruption of information Attack Targets: Agents Agent Containers Countermeasures: Sandboxing (software-based fault isolation, Java) Digital Signature (signed code to confirm the authenticity of an object, its origin, and its integrity) Travel Histories (maintain an authenticatable record of the prior platforms visited by an agent. Others...

21. 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.

22. 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

23. 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) )

24. 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) }

25. 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)

26. 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

27. 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.

28. 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)

29. 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

30. 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

31. A GIS spatial analyst, Dick, wants to locate a new Wal-Mart store in Boulder. He needs to obtain related map information and perform a GIS overlay analysis for this task. Data conversion Shape fitting analysis Overlay analysis Dick : GIS analyst Ron: GIS software vendor Buffer analysis Matt: GIS programmer Scenario: Spatial Analysis Procedure-A

32. Dick’s GIS node The Planning Department CODOT The Tax Assessor Department The Policy Department Agent Land use Flood zone Roads Land value and parcels Crime Risk Index Procedure-A: (from Dick’s requests] Buffer 200m from [Road] to create [Buffer zone] Overlay [Land use] [Flood zone], [Buffer zone], and [Land parcels], [Crime Risk Index]. Agent The Roaming of Agent The Roaming of Agent (Carry a [Procedure-A]) Procedure-A: Agent Procedure-A: Agent Procedure-A: Agent Procedure-A:

33. 3. Agent travels to the agent container in CODOT. 4. Executes the first line of procedure-A. 5. Generates a new data called [Buffer zone] and puts the new data in the CODOT data container. Dick’s GIS node CODOT Procedure-A: (from Dick’s request) Buffer 200m from [Road] to create [Buffer zone] Overlay [Land use] [Flood zone], [Buffer zone], and [Land parcels], [Crime Risk Index]. Agent First Stage Data container 1. Agents search the location of [Roads], [Buffer operation], etc. 2. Find out the location of data and component. ([Roads] URL: www.CODOT.gov) ([Buffer]: URL: www.CODOT.gov) Agent container Component container Roads Buffer zone Agent Procedure-A Buffer

34. Dick’s GIS node CODOT Procedure-A: (from Dick’s request) Buffer 200m from [Road] to create [Buffer zone] Overlay [Land use] [Flood zone], [Buffer zone], and [Land parcels], [Crime Risk Index]. Agent Second Stage Data container Agent container Component container Roads Buffer zone Agent Procedure-A Buffer The Planning Department Agent container Buffer zone Agent Procedure-A Overlay Land use Flood zone Over-1

35. Procedure-A: (from Dick’s request) Buffer 200m from [Road] to create [Buffer zone] Overlay [Land use] [Flood zone], [Buffer zone], and [Land parcels], [Crime Risk Index]. Third Stage The Planning Department Agent container Buffer zone Agent Procedure-A Overlay Land use Flood zone Over-1 The Tax Assessor Department Agent container Agent Procedure-A Overlay Over-1 Land parcels Over-2

36. Final Stage The Police Department Agent container Agent Procedure-A Overlay Crime risk index Final The Tax Assessor Department Agent container Agent Procedure-A Overlay Over-1 Land parcels Over-2 Dick’s GIS node Data container Component container Agent container Agent Procedure-A (complete) Final

37. Deploy the Architecture GIS node: Matt-GIS. com Shape fitting analysis : GIS components GIS node: Dick.colorado.edu GIS node: Boulder-Planning.gov GIS node: Boulder-Police.gov Crime Rate records Overlay analysis component Buffering component Machine agent Component agent Geodata agent : Data object 3D shading and ray tracing Statistic analysis Shape fitting analysis Land use Roads Flood area Parcel rec. Land use Roads Auto. Data Conversion Crime rate

38. I n t e r n e t Local Network Intranet GIS Node GIS Node: Ming GIS Node: Mike GIS Node: Eva GIS Node: Tina GIS Node A GIS Task GIS Node: SDSU GIS Node: UCSB GIS Node: SUNY GIS Node: FGDC Collaboration among GIS nodes

39. Mike ’ s Input: GIS component: [Display] .Required Component Required data: [Colorado Roads] .Required Data (Machine agent-A) init { If.RequiredData found in [Data container] Then set.OperationData =.RequiredData ElseIf Search(.RequiredData) = Null Then print “ Data can not be found. ” ; exit Else set.OperationData = Search(.RequiredData).CopyToDataContainer End If If.RequiredComponent found in [Component container] Then set.OperationComponent =.RequiredComponent Elseif Search(.RequiredComponent) = Null Then print “ GIS component can not be found. ” ; exit Else set.OperationComponent = Search(.RequiredComponent).CopyToComponentContainer End If SendToComponentAgent(.OperationData,.OperationComponent) } Coding Example: Machine agents search for requested data object