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A Brief Overview of Agent Communication in the Semantic Web Era -- and Beyond --. Tim Finin University of Maryland, Baltimore County April 2007. http://ebiquity.umbc.edu/resource/html/id/220/. Overview. Agent communication languages 1990-2000 DARPA knowledge sharing effort 1997-2002 FIPA

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A brief overview of agent communication in the semantic web era and beyond

A Brief Overview of Agent Communication in the Semantic Web Era-- and Beyond --

Tim Finin

University of Maryland, Baltimore County

April 2007

http://ebiquity.umbc.edu/resource/html/id/220/


Overview
Overview

  • Agent communication languages

  • 1990-2000 DARPA knowledge sharing effort

  • 1997-2002 FIPA

  • 2000- (Semantic) Web

  • 2010- and beyond

  • Are we making progress?


Agent communication
Agent Communication

  • Agent-to-agent communication is key to realizing the potential of the agent paradigm, just as the development of human language was key to the development of human intelligence and societies.

  • Agents use an Agent Communication Language(ACL) to communication information and knowledge.

  • Genesereth (CACM 1994) defined a software agent as any system which uses an ACL to exchange information.

    Genesereth, M. R., Ketchpel, S. P.: “Software Agents”, Communications of the Association for Computing Machinery, July 1994, pp 48-53.


Some acls
Some ACLs

  • Knowledge sharing approach

    • KQML, KIF, Ontologies

  • FIPA

  • Ad hock languages

    • e.g., SRI’s OAA

?

?? Shared

Experiences &

Strategies ??

e.g., team theories,joint intentions, …

Shared beliefs, plans, goals,intentions, policies …

Intentional

Sharing

e.g., KQML, FIPA,

KIF, Aglets, OWL

Shared facts, rules, constraints,

procedures and knowledge

Knowledge

Sharing

Object

Sharing

Shared objects, procedure calls

and data structures

e.g., CORBA,

RPC, RMI


Bdi agents theories and architectures
BDI Agents, Theories and Architectures

  • BDI architectures describe the internal state of an agent by the mental states of beliefs, goals and intentions

  • BDI theories provide a conceptual model of the knowledge, goals, and commitments of an agent

  • BDI agents have some (implicit or explicit) representations of the corresponding attitudes

  • BDI models are important in defining the semantics of ACLs


Bdi model and communication

B + D => I

B + D => I

I => A

I => A

BDI Model and Communication

  • Communication is a means to (1) reveal to others what our BDI state is and (2) attempt to effect the BDI state of others.

  • Note the recursion: an agent has beliefs about the world, beliefs about other agents, beliefs about the beliefs of other agents, beliefs about the beliefs another agent has about it, ...



Historical note knowledge sharing effort
Historical Note:Knowledge Sharing Effort

  • Initiated by DARPA circa 1990 with later support from NSF, AFOSR, etc.

    • Participation by ~100 researchers in academia and industry

  • Developing techniques, methodologies and software tools forknowledge sharing and knowledge reuse.

  • Sharing and reuse can occur at design, implementation or execution time.

  • Mostly wound down by ~2000 as funding dried up and industry failed to adopt the ideas


Knowledge sharing effort
Knowledge Sharing Effort

  • Knowledge sharing requires a communication

  • … which requires a common language

  • We can divide a language into syntax, semantics, and pragmatics

  • Some existing components that can be used independently or together:

    • KIF - knowledge Interchange Format(syntax)

    • Ontolingua - a language for defining sharable ontologies (semantics)

    • KQML - a high-level interaction language (pragmatics)

Propositional

Propositional

attitudes


Knowledge interchange format

KIF <-> Lang3 Translator

Sys 3

Know. Base

Know. Base

in KIF

KIF <-> Lang2 Translator

in

KIF

Lang3

Know. Base

KIF <-> Lang1 Translator

in

Library

Sys 2

Lang2

Know. Base

in

Sys 1

Lang1

Knowledge Interchange Format

  • KIF ~ First order logicwithset theory

  • An interlingua for encoded declarative knowledge

    • Takes translation among nsystems from O(n2) to O(n)

  • Common language for reusable knowledge

    • Implementation independent semantics

    • Highly expressive - can represent knowledge in typical application KBs.

    • Translatable - into and out of typical application languages

    • Human readable - good for publishing reference models and ontologies.

  • 2003: KIF superceded by Common Logic (http://cl.tamu.edu/)

    • http://en.wikipedia.org/wiki/Common_Logic

  • Semantic Web languages RDF and OWL are also alternatives


Kif syntax and semantics
KIF Syntax and Semantics

  • Extended version of first order predicate logic

  • Simple list-based linear ASCII syntax, e.g.,

    (forall ?x (=> (P ?x) (Q ?x)))

    (exisits ?person (mother mary ?person))

    (=> (apple ?x) (red ?x))

    (<<= (father ?x ?y) (and (child ?x ?y) (male ?x))

    (=> (believes john ?p) (believes mary ?p))

    (believes john '(material moon stilton))

  • Model-theoretic semantics

  • KIF includes an axiomatic specification of large function and relation vocabulary and a vocabulary for numbers, sets, and lists


Common semantics shared ontologies and ontolingua
Common Semantics Shared Ontologies and Ontolingua

  • Ontology: A common vocabulary and agreed upon meanings to describe a subject domain.

  • Ontolingua is a language for building, publishing, and sharing ontologies.

    • A web-based interface to a browser/editor server.

    • Ontologies can be automatically translated into other content languages, including KIF, LOOM, Prolog, etc.

    • The language includes primitives for combining ontologies.


Common pragmatics knowledge query and manipulation language
Common PragmaticsKnowledge Query and Manipulation Language

  • KQML is a high-level, message-oriented, communication language and protocol for information exchange independent of content syntax and ontology.

  • KQML is also independent of

    • transport mechanism, e.g., tcp/ip, email, corba, IIOP, http ...

    • High level protocols, e.g., Contract Net, Auctions, …

  • Each KQML message represents a single speech act(e.g., ask, tell, achieve, …) with an associated semantics and protocol.

  • KQML includes primitive message types of particular interest to building interesting agent architectures (e.g., for mediators, sharing intentions, etc.)


Common high level protocols
Common High-level Protocols

  • There is also a need for communication agents to agree on the agent-level protocols they will use.

  • The protocol is often conveyed via an extra parameter on a message

    • (ask :from Alice :to Bob … :protocol auction42 …)

  • Common protocols:

    • Contract net

    • Various auction protocols

    • Name registration

  • These protocols are often defined in terms of constraints on possible conversations and can be expressed as

    • Grammars (e.g., DFAs, ATNs, DCGs…)

    • Petri networks

    • UML-like interaction (activity) diagrams

    • Conversation plans

    • Rules or axioms


Common service infrastructure
Common Service Infrastructure

  • Many agent systems assume a common set of services such as:

    • Agent Name Sever

    • Broker or Facilitator

    • Communication visualizer

    • Certificate server

  • These are often tied rather closely to an ACL since a given service is implemented to speak a single ACL

  • Moreover, some of the services (e.g., name registration) may be logically ACL-dependent

    • e.g., Some ACLs don’t have a notion of an agent’s name and others have elaborate systems of naming


A kqml message

(tell :sender bhkAgent

:receiver fininBot

:in-reply-to id7.24.97.45391

:ontology ecbk12

:languageProlog

:content “price(ISBN3429459,24.95)”)

performative

parameter

value

A KQML Message

Represents a single speech actorperformative

ask, tell, reply, subscribe, achieve, monitor, ...

with an associated semantics and protocol

tell( i,j, Bi ) = fp[Bi Bi Bi( Bifj Bi Uifj Bi )]  re[Bj Bi] ...

and a list of attribute/value pairs

:content, :language, :from, :in-reply-to


Performatives 1997
Performatives (1997)

Insert

Uninsert

Delete-one

Delete-all

Undelete

Tell

Untell

Inform

Basic

DB

Broadcast

Forward

Inform

Ask-if

Ask-one

Ask-all

Network

Basic

Achieve

Unachieve

Goal

Query

Request

Stream

Stream

Eos

KQML

Performatives

Facilitation

Cursor

Standby

Ready

Next

Rest

Discard

Broker-one

Recommend-one

Recruit-one

Broker-all

Recommend-all

Recruit-all

Reply

Promise

Stream

Eos

Advertise

Unadvertise

Meta

Deny

Subscribe


Simple query performatives

ask-if(P)

ask-one(P)

Sorry

A

B

tell(P)

tell(P1)

A

B

tell((p1 p2 p3...))

Stream-all(P)

ask-all(P)

Simple Query Performatives

tell(P2)

tell(P3)

eos

  • The ask-one, ask-all, ask-if, and stream-all performatives provide a basic query mechanism.


Capability description

advertise(q3)

advertise(q2)

ask-all(advertise(P)

advertise(q1)

A

B

C

FAC

advertise(p1)

advertise(p2)

Capability Description

The advertise performative is used to describe the performatives an agent is prepared to accept.


Facilitation performatives

adv(ask(P))

recommend(ask(P))

advertise(ask(P))

broker(ask(P))

B

C

A

C

B

A

ask(P)

fwd(adv(ask(P)))

ask(P)

Broker

Recommend

tell(P)

tell(P)

tell(P)

advertise(ask(P))

recruit(ask(P))

B

C

A

ask(P)

tell(P)

Recruit

Facilitation Performatives

  • The three facilitation performatives come in a X-one and X-all versions:

    • Broker-one and broker-all

    • Recruit-one and recruit-all

    • recommend-one and recommend-all


Ontology languages vary in expressivity
Ontology languages vary in expressivity

Thesauri

“narrower

term”

relation

space of current interest

Inverse, Disjointness,part of…

Frames

(properties)

Formal

is-a

Catalog/ID

CYC

DB Schema

UMLS

RDF

RDFS

DAML

Wordnet

OO

OWL

IEEE SUO

Formal

instance

General

Logical

constraints

Informal

is-a

Value Restriction

Terms/

glossary

ExpressiveOntologies

SimpleTaxonomies

After Deborah L. McGuinness (Stanford)


Conceptual schemas

139 74.50140 77.60

… …

Conceptual Schemas

A conceptual schema specifies the intended meaning of concepts used in a data base

Data Base:

Data Base Schema:

Table: price *stockNo: integer; cost: float

Auto

Product

Ontology

price(x, y) =>

 (x’, y’) [auto_part(x’)

& part_no(x’) = x

& retail_price(x’, y’, Value-Inc)

& magnitude(y’, US_dollars) = y]

Product

Ontology

Conceptual Schema:

Units &

Measures

Ontology


1997 2003 standardization fipa
1997-2003StandardizationFIPA


What is fipa
What is FIPA

  • http://fipa.org/

  • The Foundation for Intelligent Physical Agents founded as a non-profit association ~1997.

  • FIPA’s purpose is to promote the success of emerging agent-based applications, services and equipment by establishing standards

    • MP3 was the model

  • In 2006 it became an IEEE standards committee


Fipa agent communication language
FIPA Agent Communication Language

  • Called FIPA ACL

  • Based on speech acts

  • Messages are actions (communicative actions or CAs)

  • Communicative acts are described in both a narrative form and a formal semantics based on modal logic

  • Syntax is similar to KQML

  • Specification provides a normative description of high-level interaction protocols (aka conversations)

  • Separate library of protocols and content languages (e.g., SL, KIF, RDF)

  • Several serializations


Agent standardization fipa cooperation between agents
Agent-Standardization - FIPA Cooperation between Agents

CAs for Information Exchange

  • proposition or reference as content

  • Basic CAs:

    • inform

    • query-ref

    • not-understood

  • Advanced CAs:

    • inform-if, inform-ref

    • confirm, disconfirm

    • subscribe


Agent standardization fipa cooperation between agents1
Agent-Standardization - FIPA Cooperation between Agents

CAs for task delegation

  • action-description as content

  • Basic CAs:

    • request

    • agree

    • refuse

    • failure

    • not-understood

  • Advanced CAs:

    • request-when, request-whenever

    • cancel


Agent standardization fipa cooperation between agents2
Agent-Standardization - FIPA Cooperation between Agents

CAs for Negotiation

  • action-description and proposition as content

  • Initiating CA

    • cfp

  • Negotiating CA

    • propose

  • Closing CAs

    • accept-proposal

    • reject-proposal


Agent standardization fipa cooperation between agents3
Agent-Standardization - FIPA Cooperation between Agents

Example

(request

:sender (:name [email protected]:3410)

:receiver (:name [email protected]://hilton.com:5001)

:ontology fipa-pta

:language SL

:protocol fipa-request

:content

( action [email protected]://hilton.com:5001

( book-hotel (:arrival 04/07/1999) (:departure 12/07/1999)

(:infos ( ))

)))

FIPA 99: other possibilities to define content!


Agent standardization fipa cooperation between agents4
Agent-Standardization - FIPA Cooperation between Agents

FIPA Cooperation

  • CAs have their own formal semantics

    • difficult to implement

    • need not be implemented - agent must behave according to semantics

  • Interaction protocols define structured conversations

    • based on CAs

    • basis for dialogues between agents

    • basic set of pre-defined IPs

    • own IPs can be defined


Agent standardization fipa cooperation between agents5
Agent-Standardization - FIPA Cooperation between Agents

FIPA-Query (simplified - for information exchange)

query

inform

not-understood


Agent standardization fipa cooperation between agents6
Agent-Standardization - FIPA Cooperation between Agents

FIPA-Request - for task delegation

request(action)

not-understood

refuse(reason)

agree

failure(reason)

inform(done())

inform-ref


AUML

  • Agent UML

  • http://www.auml.org/

  • ULM like framework for specifying agent communication and interaction protocols


Fipa agent platform

software

A

A

AMS

DF

ACC

IIOP

internal platform message transport

FIPA Agent Platform

Agents belong to one or more agent platforms which provide basic services.


Jade

Java Agent Development Framework is an OS software framework for multi-agent systems, implemented in Java.

  • Developed by Telcom Italia

  • Built on FIPA standards

  • Libraries (LEAP) for handheld and wireless devices

  • http://jade.tilab.com/

    System of choice for building FIPA based MAS

April 2007


2000 semantic web

2000-?Semantic Web


“XML is Lisp's bastard nephew, with uglier syntax and no semantics. Yet XML is poised to enable the creation of a Web of data that dwarfs anything since the Library at Alexandria.”

-- Philip Wadler, Et tu XML? The fall of the relational empire, VLDB, Rome, September 2001.


“The web has made people smarter. We need to understand how to use it to make machines smarter, too.”

-- Michael I. Jordan, paraphrased from a talk at AAAI, July 2002 by Michael Jordan (UC Berkeley)


how to use it to make machines smarter, too.”The Semantic Web will globalize KR, just as the WWW globalize hypertext”

-- Tim Berners-Lee


Origins
Origins how to use it to make machines smarter, too.”

Tim Berners-Lee’s original 1989 WWW proposal described a web of relationships among namedobjects unifying many info. management tasks.

Capsule history

  • Guha’s MCF (~94)

  • XML+MCF=>RDF (~96)

  • RDF+OO=>RDFS (~99)

  • RDFS+KR=>DAML+OIL (00)

  • W3C’s SW activity (01)

  • W3C’s OWL (03)

http://www.w3.org/History/1989/proposal.html


W3c s semantic web goals
W3C’s Semantic Web Goals how to use it to make machines smarter, too.”

Focus on machine consumption:

"The Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation."

-- Berners-Lee, Hendler and Lassila, The Semantic Web, Scientific American, 2001


Agents
Agents? how to use it to make machines smarter, too.”

  • DARPA gave the Semantic Web a big push starting in 2000

    • Going from simple RDF to OWL

  • DARPA Agent Markup Language

  • Goal was to give agents access to information and knowledge

  • And to populate the web with intelligent agents providing services


Tbl s semantic web vision
TBL’s semantic web vision how to use it to make machines smarter, too.”


Rdf is the first sw language
RDF is the first SW language how to use it to make machines smarter, too.”

Graph

XML Encoding

RDF

Data Model

<rdf:RDF ……..>

<….>

<….>

</rdf:RDF>

Good For

HumanViewing

Good for

MachineProcessing

Triples

stmt(docInst, rdf_type, Document)

stmt(personInst, rdf_type, Person)

stmt(inroomInst, rdf_type, InRoom)

stmt(personInst, holding, docInst)

stmt(inroomInst, person, personInst)

RDF is a simple language for building graph based representations

Good For

Reasoning


Simple rdf example
Simple RDF Example how to use it to make machines smarter, too.”

http://umbc.edu/~finin/talks/idm02/

dc:Title

“Intelligent Information Systemson the Web and in the Aether”

dc:Creator

bib:Aff

bib:email

http://umbc.edu/

bib:name

[email protected]

“Tim Finin”


Xml encoding for rdf
XML encoding for RDF how to use it to make machines smarter, too.”

<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"

xmlns:dc="http://purl.org/dc/elements/1.1/"

xmlns:bib="http://daml.umbc.edu/ontologies/bib/">

<description about="http://umbc.edu/~finin/talks/idm02/">

<dc:title>Intelligent Information Systems on the Web and in the Aether</dc:Title>

<dc:creator>

<description>

<bib:Name>Tim Finin</bib:Name>

<bib:Email>[email protected]</bib:Email>

<bib:Aff resource="http://umbc.edu/" />

</description>

</dc:Creator>

</description>

</rdf:RDF>


A usecase foaf
A usecase: FOAF how to use it to make machines smarter, too.”

  • FOAF (Friend of a Friend) is a simple ontology to describe people and their social networks.

    • See the foaf project page: http://www.foaf-project.org/

  • We recently crawled the web and discovered over 1,000,000 valid RDF FOAF files.

    • Most of these are from the http://liveJournal.com/ blogging system which encodes basic user info in foaf

    • See http://apple.cs.umbc.edu/semdis/wob/foaf/

  • <foaf:Person>

    • <foaf:name>Tim Finin</foaf:name>

    • <foaf:mbox_sha1sum>2410…37262c252e</foaf:mbox_sha1sum>

    • <foaf:homepage rdf:resource="http://umbc.edu/~finin/" />

    • <foaf:img rdf:resource="http://umbc.edu/~finin/images/passport.gif" />

  • </foaf:Person>


Foaf why rdf extensibility
FOAF: why RDF? Extensibility! how to use it to make machines smarter, too.”

  • FOAF vocabulary provides 50+ basic terms for making simple claims about people

  • FOAF files can use other RDF terms too: RSS, MusicBrainz, Dublin Core, Wordnet, Creative Commons, blood types, starsigns, …

  • RDF guarantees freedom of independent extension

    • OWL provides fancier data-merging facilities 

  • Result: Freedom to say what you like, using any RDF markup you want, and have RDF crawlers merge your FOAF documents with other’s and know when you’re talking about the same entities. 

After Dan Brickley, [email protected] 


Rdf schema rdfs
RDF Schema (RDFS) how to use it to make machines smarter, too.”

  • RDF Schema adds taxonomies forclasses & properties

    • subClass and subProperty

  • and some metadata.

    • domain and rangeconstraints on properties

  • Several widely usedKB tools can importand export in RDFS

  • Stanford Protégé KB editor

  • Java, open sourced

  • extensible, lots of plug-ins

  • provides reasoning & server capabilities


Rdfs supports simple inferences

New and how to use it to make machines smarter, too.”Improved!

100% Betterthan XML!!

RDFS supports simple inferences

  • An RDF ontology plus some RDF statements may imply additional RDF statements.

  • This is not true of XML.

  • Note that this is part of the data model and not of the accessing or processing code.

parent a property.

person a class.

woman subClass person.

mother a property.

eve a person;

a woman;

parent cain.

cain a person.

  • @prefix rdfs: <http://www.....>.

  • @prefix : <genesis.n3>.

    • parent rdfs:domain person;

    • rdfs:range person.

    • mother rdfs:subProperty parent;

    • rdfs:domain woman;

    • eve mother cain.


W3c s web ontology language owl

OWL how to use it to make machines smarter, too.”

W3C’s Web Ontology Language (OWL)

  • DAML+OIL begat OWL.

  • OWL released as W3C recommendation 2/10/04

  • See http://www.w3.org/2001/sw/WebOnt/ for OWL overview, guide, specification, test cases, etc.

  • Three layers of OWL are defined of decreasing levels of complexity and expressiveness

    • OWL Full is the whole thing

    • OWL DL (Description Logic) introducesrestrictions

    • OWL Lite is an entry level languageintended to be easy to understandand implement


Owl in one slide
OWL in One Slide how to use it to make machines smarter, too.”

OWL is built on top of XML and RDF

<rdf:RDF xmlns:rdf ="http://w3.org/22-rdf-syntax-ns#"

xmlns:rdfs=http://w3.org/rdf-schema#> xmlns:owl="http://www.w3.org/2002/07/owl#”>

<owl:Ontology rdf:about="">

<owl:imports rdf:resource="http://owl.org/owl+oil"/>

</owl:Ontology>

<owl:Class rdf:ID="Person">

<rdfs:subClassOf rdf:resource="#Animal"/>

<rdfs:subClassOf>

<owl:Restriction>

<owl:onProperty rdf:resource="#hasParent"/>

<owl:allValuesFrom rdf:resource="#Person"/>

</owl:Restriction>

</rdfs:subClassOf>

<rdfs:subClassOf>

<owl:Restriction owl:cardinality="1">

<owl:onProperty rdf:resource="#hasFather"/>

</owl:Restriction>

</rdfs:subClassOf>

</owl:Class>

<Person rdf:about=“http://umbc.edu/~finin/">

<rdfs:comment>Finin is a person.</rdfs:comment>

</Person>

It allows the definition, sharing, composition and use of ontologies

OWL is ~= a frame based knowledge representation language

It can be used to add metadata about anything which has a URI.

URIs are a W3C standard generalizing URLs

everything has a URI


Rdf is being used
RDF is being used! how to use it to make machines smarter, too.”

  • RDF has a solid specification

  • RDF is being used in a number of web standards

    • CC/PP (Composite Capabilities/Preference Profiles), P3P (Platform for Privacy Preferences Project), RSS (RDF Site Summary), RDF Calendar (~ iCalendar in RDF)

  • And in other systems

    • Netscape’s Mozilla web browser, open directory (http://dmoz.org/)

    • Adobe products via XMP (eXtensible Metadata Platform)

    • Web communities: LiveJournal, Ecademy, and Cocolog

    • In Microsoft’s VISTA: Connected Services Framework uses an RDF database and SPARQL

    • Oracle’s 10g and 11g products

    • Yahoo’s food portal

    • Joost TV over the web startup


Sparql example
SPARQL Example how to use it to make machines smarter, too.”

BASE <http://example.org/>

PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>

PREFIX foaf: <http://xmlns.com/foaf/0.1/>

PREFIX ex: <properties/1.0#>

SELECT DISTINCT $person ?name $age

FROM <http://rdf.example.org/people.rdf>

WHERE { $person a foaf:Person ;

foaf:name ?name.

OPTIONAL { $person ex:age $age } .

FILTER ! REGEX(?name, "Bob")

}

LIMIT 3 ORDER BY ASC[?name]


Some applications involving acls and the semantic web
Some applications involving how to use it to make machines smarter, too.”ACLs and the Semantic Web


Ittalks architecture

<daml> how to use it to make machines smarter, too.”

</daml>

<daml>

</daml>

<daml>

</daml>

<daml>

</daml>

ITTALKS Architecture

Web server + Java servlets

Web Services

People

MapBlast, CiteSeer,Google, …

HTTP

HTTP, WebScraping

Email, HTML, SMS, WAP

ApacheTomcat

Agents

FIPA ACL, KQML, DAML

SQL

RDBMS

DB

HTTP, KQML, DAML, Prolog

DAMLreasoningengine

Databases

DAML files


Report Direct Buy Transactions how to use it to make machines smarter, too.”

Report Contract

Report Auction Transactions

Market Oversight

Agent

Request

CFP

Report Travel Package

Bid

Bid

Bulletin Board

Agent

Auction Service

Agent

Customer

Agent

Proposal

Direct Buy

Travel Agents

Web Service

Agents

TAGA

Travel Agent Game in Agentcities

Motivation

  • Market dynamics

  • Auction theory (TAC)

  • Semantic web

  • Agent collaboration (FIPA & Agentcities)

Features

  • Open Market Framework

  • Auction Services

  • OWL message content

  • OWL Ontologies

  • Global Agent Community

Technologies

  • FIPA (JADE, April Agent Platform)

  • Semantic Web (RDF, OWL)

  • Web (SOAP,WSDL,DAML-S)

  • Internet (Java Web Start )

Ontologieshttp://taga.umbc.edu/ontologies/

  • travel.owl – travel concepts

  • fipaowl.owl – FIPA content lang.

  • auction.owl – auction services

  • tagaql.owl – query language

FIPA platform infrastructure services, including directory facilitators enhanced to use DAML-S for service discovery

http://taga.umbc.edu/

Acknowledgements: DARPA contract F30602-00-2-0591 and Fujitsu Laboratories of America.Students: Y. Zou, L. Ding, H. Chen, R. Pan. Faculty: T. Finin, Y. Peng, A. Joshi, R. Cost. 4/03


Http ebiquity umbc edu
http://ebiquity.umbc.edu/ how to use it to make machines smarter, too.”

  • Our research group’s web site generate both HTML and OWL.

  • HOW? This is relatively easy since the content is in a database.

  • PHP is sufficient for the job.

  • HTML pages have links to corresponding OWL

  • WHY? This exposes the information to programs and agents – no more web scraping.


Cmu mycampus project

Objective how to use it to make machines smarter, too.”: Enhance campus life through context-aware services accessible over the WLAN

Ontologies

Personal/contextual: location, calendar, organizational etc.

Privacy preferences: who has access to what, “obfuscation” rules

Web services: automated service identification and access (OWL-S)

CMU MyCampus Project

http://www.cs.cmu.edu/~sadeh/mycampus.htm#Video


Fujitsu task computing
Fujitsu Task Computing how to use it to make machines smarter, too.”

http://www.taskcomputing.org/

Play Jeff’s Video

Dial Contact from Outlook

Weather Info of FLA, CP

  • Objective: Make computing available throughout the physical environment while it is effectively invisible to the users

e-Services

OS/Application

Device

Dial

Dial

Video from DV

Video from DV

Open

Open

Save

Save

Print

Print

Add into Outlook

Add into Outlook

Aerial Photo of

Aerial Photo of

Weather Info of

Weather Info of

Play (Audio)

Play (Audio)

Play (Video)

Play (Video)

View

View

Jeff’s Video

Jeff’s Video

Contact from Outlook

Contact from Outlook

Web Pages

Devices

OS/Application


The context broker architecture
The Context Broker Architecture how to use it to make machines smarter, too.”

http://cobra.umbc.edu/

Access to more

information

Knowledge sharing

Policy


The easymeeting system
The EasyMeeting System how to use it to make machines smarter, too.”


An easymeeting scenario

The broker tells her how to use it to make machines smarter, too.”

location to her agent

The broker detects

people presence

People enter the conference room

They “beam” their

policy to the broker

»

A

B

B

»

»

Alice’s policy says,

“inform my personal

agent of my location”

The broker builds

the context model

B

A

.. isLocatedIn ..

Web

B

An EasyMeeting Scenario


An easymeeting scenario1

The broker informs how to use it to make machines smarter, too.”

the subscribed agents

B

When all expected

participants hv arrived

The Greeting Srv. greets

Alice & the others

Her agent informs

the broker about her

role and intentions

Alice’s policy says,

“can share with any

agents in the room”

The projector agent

sets up the slides

OFF

Hello! [xyz]

DIM

+

A

An EasyMeeting Scenario


The soupa ontology
The SOUPA Ontology how to use it to make machines smarter, too.”


2010 and beyond

2010- how to use it to make machines smarter, too.”and Beyond


It's hard to make predictions, especially about the future how to use it to make machines smarter, too.”


Things to watch
Things to watch how to use it to make machines smarter, too.”

  • Google (e.g., GoogleBase)

  • Wikipedia (e.g., Semantic MediaWiki)

  • Freebase (OWL meets Wikipedia?)

  • Joost – a high profile startup (internet meets TV) is using RDF and considers it to be "XML on steroids."

  • If RDF is the new KIF, then SPARQL might evolve into the new KQML


Conclusions
Conclusions how to use it to make machines smarter, too.”


Some key ideas
Some key ideas how to use it to make machines smarter, too.”

  • Software agents offer a new paradigm for very large scale distributed heterogeneous applications.

  • The paradigm focuses on the interactions of autonomous, cooperating processes which can adapt to humans and other agents.

  • Agent Communication Languages are a key enabling technology

    • Mobility is an orthogonal characteristic which many, but not all, consider central.

    • Intelligence is always a desirable characteristic but is not strictly required by the paradigm.

  • The paradigm is still forming and ACLs will continue to evolve.


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