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By Ming-Hsiang Tsou E-mail: [email protected] Phone: 619-5940205 Fax: 619-5944938 PowerPoint PPT Presentation


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

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By ming hsiang tsou e mail mtsou mail sdsu phone 619 5940205 fax 619 5944938

AAG 2002, Los Angeles

An Intelligent Agent-based Architecture for Internet Mapping and Distributing Geographic Information Services

By Ming-Hsiang Tsou

E-mail: [email protected]

Phone: 619-5940205

Fax: 619-5944938

The Department of Geography, San Diego State University


The promise of internet mapping

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


Problems in internet mapping

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.


By ming hsiang tsou e mail mtsou mail sdsu phone 619 5940205 fax 619 5944938

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?


By ming hsiang tsou e mail mtsou mail sdsu phone 619 5940205 fax 619 5944938

Web Map Users Don’t Have Sufficient Cartographic Knowledge

Web Map Users ≠GIS Users

  • Current Web Maps:

  • Good Interactivity

  • Good Flexibility

  • Poor Quality

  • Poor Design


Possible solution intelligent software agents

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)


By ming hsiang tsou e mail mtsou mail sdsu phone 619 5940205 fax 619 5944938

Agent-based Communication

GIS Components

(Programs)

Geodata

Objects

Metadata

Metadata

  • Software Agents

  • Info. finders/filters

  • Interpreters

  • Decision makers

Design

User-defined rules

Cartography Knowledge Bases

Agent collaboration


By ming hsiang tsou e mail mtsou mail sdsu phone 619 5940205 fax 619 5944938

Information Finder / Filter

Generalization

Color Scheme

Carto-rules

Method-1

Rule-2

Landuse-color

Method-2

Rule-3

DEM-color

~ ~ ~ ~ ~

~ ~ ~ ~ ~

~ ~ ~ ~ ~

Rule- 4

Method-400

Zoning-color

Color-scheme = DEM

Information Finder

------------------------

Information Filter

Iso-line rules

InterpolationAlgorithm

Site-A

KEYWORDS

Users

(User-defined rules)

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

Site-B

Site-C


By ming hsiang tsou e mail mtsou mail sdsu phone 619 5940205 fax 619 5944938

Information Interpreter

[Buffering] in UNIX

[Address matching] in Window 2000

Metadata

Metadata

Metadata becomes the source of knowledge bases

Information Interpreter

Metadata

Metadata

Metadata

Data-3 (Lat/Lon)

Data-1 (UTM)

Data-2 (SPCS)

  • 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.


The design of operational metadata

The Design of Operational Metadata

GeoData Object

Map display component

Metadata

GIS-operation requirements

(A, B)

(A, B, C, D, E, F)

System

metadata

Integrating

Metadata describe how the objects should be represented (color, symbols) and the domain of the object (vector, line, transportation).

Other

GIS components

Self-describing, Self-managing

map layers


By ming hsiang tsou e mail mtsou mail sdsu phone 619 5940205 fax 619 5944938

Decision Maker

Agent

(decision maker)

Actions

Events

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.


By ming hsiang tsou e mail mtsou mail sdsu phone 619 5940205 fax 619 5944938

Software Agents

Software Agents

User-defined rules

(Cartographic rules)

User Interface

Inference engine

Working memory

Rules

Facts

Intelligent software agent

Traditional

Expert Systems

GIS data and components framework

GIS Component

(Buffering)

GIS data object

(Road, Colorado)

Metadata

(Facts)

Metadata

(Facts)

Task #2 (Client Machine–B)

(Working memory)

Task #1 (Client Machine–A)

(Working memory)


By ming hsiang tsou e mail mtsou mail sdsu phone 619 5940205 fax 619 5944938

Hierarchy of GeoAgents

Machine Agent

Stationary GeoAgent

GeoAgent

Component Agent

Mobile GeoAgent

Geodata Agent

Carto Agent

UML notations

Generalization

UML: Unified Modeling Language


Walk through example

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.


Dynamic cartographic design

Dynamic Cartographic Design

  • Add a new landuse layer (metadata: color = blue)

  • Conflict with current sd_conven layer (same color: blue)

  • Overlap other information

Carto Agents re-arrange the layer sequences and reassign new color scheme for the landuse layer


Cartography ontology cartography knowledge base ckb

Above( PointLayer(x), PolyLayer(y) )

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

}


Combining metadata and rules

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)

}


Inference by multiple knowledge bases

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)


By ming hsiang tsou e mail mtsou mail sdsu phone 619 5940205 fax 619 5944938

M

M

M

M

M

M

Implementation: GIService Node

GIServices Workstation (a GIS node)

: metadata

Hardware Profiles:

CPU, OS, CRT, printer, scanner

GeoData Object Container

GIS Component Container

M

Agent Container

Component Agent

GeoData Agent

Machine Agent

Carto Agent


By ming hsiang tsou e mail mtsou mail sdsu phone 619 5940205 fax 619 5944938

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.


By ming hsiang tsou e mail mtsou mail sdsu phone 619 5940205 fax 619 5944938

Agent Communication Language (ACL) / Protocol (ACP)

  • KQML (Knowledge Query and Manipulation Language) (Finin and Weber, 1993)

  • ACL (Agent Communication Language) specificationFIPA (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)


  • By ming hsiang tsou e mail mtsou mail sdsu phone 619 5940205 fax 619 5944938

    SUMMARY

    Current Internet Mapping

    • 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 objects

    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.


    Future work

    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


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