1 agents meet the semantic web in the aether tim finin university of maryland, baltimore county...

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Agents Meet the Agents Meet the Semantic WebSemantic Webin the Aetherin the Aether

Tim FininUniversity of Maryland, Baltimore

County

MATA’05, Montreal, 18 October 2004

http://ebiquity.umbc.edu/event/html/id/111/

Joint work with A. Joshi, Y. Yesha, Y. Peng & many students. http://creativecommons.org/licenses/by-nc-sa/2.0/

This work was partially supported by DARPA contract F30602-97-1-0215, NSF grants CCR007080 and IIS9875433 and grants from IBM, Fujitsu and HP.

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tell

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UMBCUMBCan Honors University in an Honors University in

MarylandMaryland 22

This talkThis talk

How we’ve found How we’ve found multiagent multiagent systemssystems and the and the semantic websemantic web useful in mobile and pervasive useful in mobile and pervasive computingcomputing

Illustrated using some recent Illustrated using some recent projects by colleagues and projects by colleagues and studentsstudents

Pointing out the lessons we’ve Pointing out the lessons we’ve learnedlearned


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Multiagent systems paradigmMultiagent systems paradigmIn this paradigm, agents areIn this paradigm, agents are(1) Distributed(1) Distributed(2) Autonomous(2) Autonomous Self interestedSelf interested Compete with other agentsCompete with other agents

(3) Social(3) Social Communicate to share informationCommunicate to share information Cooperate to share goals and tasksCooperate to share goals and tasks Have beliefs about other agents (e.g., Have beliefs about other agents (e.g.,

reputation)reputation)

(4) Intelligent(4) Intelligent Use knowledge, reason, self-aware, …Use knowledge, reason, self-aware, …


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Semantic Web TechnologySemantic Web Technology The W3C’s semantic web effort produced The W3C’s semantic web effort produced

standards for programs to share ontologies (i.e., standards for programs to share ontologies (i.e., vocabulary) and data & knowledge expressed in vocabulary) and data & knowledge expressed in them.them.

Built on Web and XML standards and have a Built on Web and XML standards and have a sound semantic modelsound semantic model

Key layers areKey layers are RDF:RDF: semantic networks semantic networks RDFS:RDFS: objects objects OWL:OWL: Web Ontology Language Web Ontology Language RuleMLRuleML: rules and logic: rules and logic

Semantic interoperability!Semantic interoperability!


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(1)(1) Trauma Pod Trauma Pod

2005: da Vinci Surgical Robot 2005: da Vinci Surgical Robot 2005: da Vinci Surgical Robot 2005: da Vinci Surgical Robot 2020: Automated Trauma Pod 2020: Automated Trauma Pod treats wounded soldiers on the treats wounded soldiers on the

battlefield. battlefield.

2020: Automated Trauma Pod 2020: Automated Trauma Pod treats wounded soldiers on the treats wounded soldiers on the

battlefield. battlefield.

•DARPA project to enable unmanned medical systems to DARPA project to enable unmanned medical systems to save lives on the battlefieldsave lives on the battlefield

•A portable Trauma Pod has no on-site human medical A portable Trauma Pod has no on-site human medical personnelpersonnel

•A human surgeon teleoperates surgical manipulators A human surgeon teleoperates surgical manipulators supported by automated robotic systemssupported by automated robotic systems

•DARPA project to enable unmanned medical systems to DARPA project to enable unmanned medical systems to save lives on the battlefieldsave lives on the battlefield

•A portable Trauma Pod has no on-site human medical A portable Trauma Pod has no on-site human medical personnelpersonnel

•A human surgeon teleoperates surgical manipulators A human surgeon teleoperates surgical manipulators supported by automated robotic systemssupported by automated robotic systems


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UMBC’s role in Trauma PodUMBC’s role in Trauma PodUse RFID tags to track loca-Use RFID tags to track loca-tion and use of tools and tion and use of tools and suppliessupplies

Monitor RFID events and mes-Monitor RFID events and mes-sage traffic; use workflow sage traffic; use workflow models and object roles to models and object roles to recognize what is happeningrecognize what is happening

Integrate with legacy supply Integrate with legacy supply chain systems and medical chain systems and medical encounter and patient encounter and patient recordsrecords

Begin to transition these Begin to transition these ideas to medical training and ideas to medical training and non-military use.non-military use.


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Trauma Pod ChallengesTrauma Pod ChallengesThis is new work just starting but we know …This is new work just starting but we know …

(1) The OR is a difficult environment for RFID(1) The OR is a difficult environment for RFID Lots of metal and fluidsLots of metal and fluids Noisy eventsNoisy events

(2) Making sense of large streams of noisy, (2) Making sense of large streams of noisy, low level, temporal data is hardlow level, temporal data is hard We’re exploring a two stage modelWe’re exploring a two stage model Lower level: parse primitive events to identify Lower level: parse primitive events to identify

higher level oneshigher level ones Upper level: use a hidden markov model to Upper level: use a hidden markov model to

recognize domain relevant eventsrecognize domain relevant events


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(2)(2) Trading Agents Trading Agents We’ve built an agent-based environment inspired We’ve built an agent-based environment inspired

by the Trading Agent Competition (TAC)by the Trading Agent Competition (TAC) A yearly forum for dynamic agent research since 1999A yearly forum for dynamic agent research since 1999 One task involves travel procurement -- agents buying One task involves travel procurement -- agents buying

and selling goods for clients and scored on the cost and selling goods for clients and scored on the cost and quality of trip packagesand quality of trip packages

TAC is organized around a central auction serverTAC is organized around a central auction server We opened up the system, allowing peer-to-peer We opened up the system, allowing peer-to-peer

communication among agents as well various communication among agents as well various kinds of mediator, auction, discovery, service kinds of mediator, auction, discovery, service provider agents …provider agents …

Goal: Evaluate the semantic web as a common Goal: Evaluate the semantic web as a common knowledge infrastructureknowledge infrastructure


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TAGA: Travel Agent Game in Agentcities

http://taga.umbc.edu/

TechnologiesTechnologiesFIPA FIPA (JADE, April Agent Platform)(JADE, April Agent Platform)

Semantic Web Semantic Web (RDF, OWL)(RDF, OWL)

Web Web (SOAP,WSDL,DAML-S)(SOAP,WSDL,DAML-S)

Internet Internet (Java Web Start )(Java Web Start )

FeaturesFeaturesOpen Market FrameworkOpen Market Framework

Auction ServicesAuction Services

OWL message contentOWL message content

OWL OntologiesOWL Ontologies

Global Agent CommunityGlobal Agent Community

MotivationMotivationMarket dynamicsMarket dynamicsAuction theory (TAC)Auction theory (TAC)Semantic webSemantic webAgent collaboration (FIPA Agent collaboration (FIPA & Agentcities)& Agentcities)

Travel Agents

Auction Service Agent

Customer Agent

Bulletin BoardAgent

Market Oversight Agent

Request

Direct Buy

Report Direct Buy Transactions

BidBid

CFP

Report Auction Transactions

Report Travel Package

Report Contract

Proposal

Web Service Agents

OntologiesOntologieshttp://taga.umbc.edu/ontologies/http://taga.umbc.edu/ontologies/

travel.owl travel.owl – travel concepts– travel concepts

fipaowl.owl fipaowl.owl – FIPA content lang.– FIPA content lang.

auction.owl auction.owl – auction services– auction services

tagaql.owl tagaql.owl – query language– query language

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

Owl for representation and reasoning

Owl for service

descriptions

Owl for negotiatio

n

Owl as a content languag

e

Owl for publishing

communicative acts

Owl for contract

enforcement

Owl for modeling trust

Owl for authorization policies

Owl for protocol

description

http://taga.umbc.edu/


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What we learnedWhat we learned OWL is a good KR language for a reasonably OWL is a good KR language for a reasonably

sophisticated MASsophisticated MAS Integrates well with FIPA standardsIntegrates well with FIPA standards

OWL made it easy to mix content from OWL made it easy to mix content from different ontologies unambiguouslydifferent ontologies unambiguously Supporting partial understanding & extensibilitySupporting partial understanding & extensibility

The use of OWL supported web integrationThe use of OWL supported web integration Using information published on web pages and Using information published on web pages and

integrating with web services via WSDL and SOAPintegrating with web services via WSDL and SOAP OWL has limitations: no rules, no default OWL has limitations: no rules, no default

reasoning, graph semantics, …reasoning, graph semantics, … Some of which are being addressedSome of which are being addressed



(3)(3) An early agent policy An early agent policy11 A robot may not injure a A robot may not injure a

human being, or, through human being, or, through inaction, allow a human inaction, allow a human being to come to harm.being to come to harm.

22 A robot must obey the A robot must obey the orders given it by human orders given it by human beings except where beings except where such orders would such orders would conflict with the First conflict with the First Law.Law.

33 A robot must protect its A robot must protect its own existence as long as own existence as long as such protection does not such protection does not conflict with the First or conflict with the First or Second Law.Second Law.- Handbook of Robotics, 56th Edition, 2058 A.D.



(3)(3) It’s policies all the way It’s policies all the way downdown

In Asimov’s world, the robots couldn’t always follow their policies unlike DB access control & OS file

permissionsAutonomous agents need “norms of behavior” to be followed by good citizens

Enforcement is an issue, especially for policies involving obligations

So, it’s natural to worry about … How to resolve conflicts in policies Dealing with failure to follow policies –

sanctions, reputation, etc. Whether policy engineering will be any

easier than software engineering

1 A robot may not injure a human being, or, through inaction, allow a human being to come to harm.

2 A robot must obey the orders given it by hu-man beings except where such orderswould conflict with the First Law.

3 A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.

- Handbook of - Handbook of Robotics, 56th Edition, Robotics, 56th Edition, 2058 A.D.2058 A.D.



Rei Policy LanguageRei Policy Language Rei is a declarative policy language for describing Rei is a declarative policy language for describing

policies over actionspolicies over actions Reasons over domain dependent informationReasons over domain dependent information

Currently represented in OWL + logical variablesCurrently represented in OWL + logical variables Based on deontic conceptsBased on deontic concepts

Permission, Prohibition, Obligation, DispensationPermission, Prohibition, Obligation, Dispensation Models speech actsModels speech acts

Delegation, Revocation, Request, CancelDelegation, Revocation, Request, Cancel Meta policiesMeta policies

Priority, modality preferencePriority, modality preference Policy engineering toolsPolicy engineering tools

Reasoner, IDE for Rei policies in EclipseReasoner, IDE for Rei policies in Eclipse



Applications – past, present & Applications – past, present & futurefuture

Coordinating access in Coordinating access in supply chain supply chain management systemmanagement system

Authorization policies in a Authorization policies in a pervasive pervasive computing environmentcomputing environment

Policies for team formation, Policies for team formation, collaboration, information flow in collaboration, information flow in multi-agent systemsmulti-agent systems

Security in Security in semantic web servicessemantic web services Privacy and trust on the Privacy and trust on the InternetInternet Privacy in Privacy in pervasive computing pervasive computing

environmentsenvironments

1999

2002

2003…

2004…



Ex: Security and Trust forEx: Security and Trust forSemantic Web ServicesSemantic Web Services

Semantic web services are web services Semantic web services are web services described using OWL-Sdescribed using OWL-S

Policy-based security infrastructurePolicy-based security infrastructure Advantages of using policies:Advantages of using policies:

Expressive -- can be over descriptionsExpressive -- can be over descriptionsof requester, service & contextof requester, service & context

Authorization: Rules for access controlAuthorization: Rules for access control Privacy: Rules for protecting informationPrivacy: Rules for protecting information Confidentiality: Cryptographic character-Confidentiality: Cryptographic character-

istics of information exchangedistics of information exchanged

Policies +Semantic

Web Services



ExampleExample

Mary is looking for a reservation Mary is looking for a reservation serviceservice foaffoaf description description ConfidentialityConfidentiality policy policy

BravoAir is a reservation serviceBravoAir is a reservation service OWL-SOWL-S description description AuthorizationAuthorization policy policy

Only users belonging to the same project as Only users belonging to the same project as John can access the serviceJohn can access the service

MaryMary

<foaf:Person rdf:ID="mary"><foaf:Person rdf:ID="mary"><foaf:name>Mary Smith</foaf:name><foaf:name>Mary Smith</foaf:name>

<foaf:title>Ms</foaf:title><foaf:title>Ms</foaf:title><foaf:firstName>Mary</foaf:firstName><foaf:firstName>Mary</foaf:firstName><foaf:surname>Smith</foaf:surname><foaf:surname>Smith</foaf:surname><foaf:homepage <foaf:homepage rdf:resource="http://www.somewebsite.com/marysmith.html"/>rdf:resource="http://www.somewebsite.com/marysmith.html"/><foaf:<foaf:currentProjectcurrentProject rdf:resource=" rdf:resource=" http://www.somewebsite.com/SWS-Project.rdf "/>http://www.somewebsite.com/SWS-Project.rdf "/>

<sws:<sws:policyEnforcedpolicyEnforced rdf:resource="&mary;ConfidentalityPolicy"/>rdf:resource="&mary;ConfidentalityPolicy"/>

</foaf:Person></foaf:Person>

</rdf:RDF></rdf:RDF>

Bravo PolicyBravo Policy<entity:Variable rdf:about="&bravo-policy;var1"/>

<entity:Variable rdf:about="&bravo-policy;var2"/>

<constraint:SimpleConstraint

rdf:about="&bravo-policy;GetJohnProject"

constraint:subject="&john;John"

constraint:predicate="&foaf;currentProject"

constraint:object="&bravo-policy;var2"/>

<constraint:SimpleConstraint

rdf:about="&bravo-policy;SameProjectAsJohn"

constraint:subject="&bravo-policy;var1"

constraint:predicate="&foaf;currentProject"

constraint:object="&bravo-policy;var2"/>



<constraint:And rdf:about="&bravo-policy;AndCondition1"

constraint:first="&bravo-policy;GetJohnProject"

constraint:second="&bravo-policy;SameProjectAsJohn"/>

<deontic:Right rdf:about="&bravo-policy;AccessRight"><deontic:actor rdf:resource="&bravo-policy;var1"/><deontic:action rdf:resource="&bravo-service;BravoAir_ReservationAgent"/><deontic:constraint rdf:resource="&bravo-policy;AndCondition1"/>

</deontic:Right>

………

<rdf:Description rdf:about="&bravo-service;BravoAir_ReservationAgent"><sws:policyEnforced rdf:resource="&bravo-policy;AuthPolicy"/>

</rdf:Description>



How it worksHow it works

Bravo Service OWL-S Desc

URL to foaf desc + query request <sws:policyEnforced rdf:resource

= "&bravo-policy;AuthPolicy"/>

BravoAirWeb service

Mary

Matchmaker+ Reasoner



How it worksHow it worksMary’s query = Bravo Service ? YESExtract Bravo’s policyDoes Mary meets Bravo’s policy ?Authorization enforcement complete

<deontic:Right rdf:about="&bravo-policy;AccessRight"><deontic:actor rdf:resource="&bravo-policy;var1"/><deontic:action rdf:resource="&bravo-

service;BravoAir_ReservationAgent"/><deontic:constraint rdf:resource="&bravo-

policy;AndCondition1"/></deontic:Right>

<policy:Granting rdf:about="&bravo-policy;AuthGranting"><policy:to rdf:resource="&bravo-policy;var1"/><policy:deontic rdf:resource="&bravo-policy;AccessRight"/>

</policy:Granting>

<sws:AuthorizationPolicy rdf:about="&bravo-policy;AuthPolicy"><policy:grants rdf:resource="&bravo-policy;AuthGranting"/>

</sws:AuthorizationPolicy>

<rdf:Description rdf:about="&bravo-service;BravoAir_ReservationAgent"><sws:policyEnforced rdf:resource="&bravo-policy;AuthPolicy"/>

</rdf:Description>

Mary BravoAirWeb service

<constraint:SimpleConstraint rdf:about="&bravo-policy;SameProjectAsJohn" constraint:subject="&bravo-policy;var1" constraint:predicate="&foaf;currentProject" constraint:object="&bravo-policy;var2"/>

Is the constraint true when var2 = http://www.somewebsite.com/SWS-Project.rdfvar1 = http://www.cs.umbc.edu/~lkagal1/rei/examples/sws-sec/MaryProfile.rdf

<foaf:currentProject rdf:resource = "http://www.somewebsite.com/SWS-Project.rdf"/>

<constraint:SimpleConstraint rdf:about = "&bravo-policy;GetJohnProject” constraint:subject="&john;John" constraint:predicate="&foaf;currentProject" constraint:object="&bravo-policy;var2"/>

var2 = http://www.somewebsite.com/SWS-Project.rdf



What we learnedWhat we learned Declarative policies can be used to model Declarative policies can be used to model

security, trust and privacy constraintssecurity, trust and privacy constraints Reasonably expressive policy languages can Reasonably expressive policy languages can

be encoded on OWL & translated to be encoded on OWL & translated to executable form.executable form.

This enables policies over attributes and This enables policies over attributes and context information available on the context information available on the semantic websemantic web

Policies are applicable at every level of the Policies are applicable at every level of the stack, from MAC to APPstack, from MAC to APP



(4)(4) Pervasive Pervasive ComputingComputing

“The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it ” – Mark Weiser

Think: writing, central heating, electric lighting, water services, …

Not: taking your laptop to the beach, or immersing yourself into a virtual reality



Motivation: moving from this



Motivation: to hereMotivation: to here



Motivation:Motivation:but not tobut not toherehere



Representing and Reasoning about Context

CoBrACoBrA: a broker centric agent : a broker centric agent architecture for supporting pervasive architecture for supporting pervasive context-aware systemscontext-aware systems Uses SW ontologies to model context Uses SW ontologies to model context

and reason about devices, space, time, and reason about devices, space, time, people, preferences, meetings, etc.people, preferences, meetings, etc.

Uses logical inference to interpret Uses logical inference to interpret context and to detect and resolve context and to detect and resolve inconsistent knowledgeinconsistent knowledge

Allows users to define policies Allows users to define policies controlling how information about them controlling how information about them is used and sharedis used and shared



A Bird’s Eye View of CoBrAA Bird’s Eye View of CoBrA



A Typical CoBrA Use CaseA Typical CoBrA Use Case

Alice enters a conference room

The broker negotiatesprivacy policy with Alice

The broker detects Alice’s presence

B

Policy says, “can share with any agents in the room”

AB

The broker buildsthe context model

Web

The broker knows Alice’s role and

intention

+

Alice in Wonderland*Alice in Wonderland*

* Our intelligent meeting room



A Typical CoBrA Use CaseA Typical CoBrA Use Case

The projector agent wants to help Alice

The broker informsthe subscribed agents

B A

The projector agentasks slide show info.

B

The broker acquires the slide show info.

BWeb

The broker informs the

projector agent

B

The projector agent sets up the slides

Alice in WonderlandAlice in Wonderland



SOUPA Ontology provides common SOUPA Ontology provides common vocabularyvocabulary



A Simple Spatial Model of UMBC



Where’s Harry?



Detecting InconsistenciesDetecting Inconsistencies



Privacy Protection in CoBrAPrivacy Protection in CoBrA

Users define policies to permit or Users define policies to permit or prohibit the sharing of their informationprohibit the sharing of their information Policies are provided by personal Policies are provided by personal

agents or published on web pagesagents or published on web pages and use the SOUPA ontologies as well and use the SOUPA ontologies as well

as other SW assertions (e.g., FOAF, as other SW assertions (e.g., FOAF, schedules)schedules)

The context broker follows user defined The context broker follows user defined policies when sharing information, policies when sharing information, unless contravened by higher policiesunless contravened by higher policies



The SOUPA Policy Ontology



Policy Reasoning Use CasePolicy Reasoning Use Case

The speaker doesn’t want others to know The speaker doesn’t want others to know the specific room that he’s in, but is willing the specific room that he’s in, but is willing for others to know he’s on campusfor others to know he’s on campus

He defines the following privacy policyHe defines the following privacy policy Share my location with a granularity >= “State”Share my location with a granularity >= “State”

The broker The broker isLocated(US) => Yes!isLocated(US) => Yes! isLocated(Maryland) => Yes!isLocated(Maryland) => Yes! isLocated(UMBC) => Uncertain..isLocated(UMBC) => Uncertain.. isLocated(ITE-RM210) => Uncertain..isLocated(ITE-RM210) => Uncertain..



What we learnedWhat we learned

FIPA and OWL were good for integrating FIPA and OWL were good for integrating disparate componentsdisparate components

Even when some of these were running on Even when some of these were running on cell phones!cell phones!

OWL made it easy to mix content from OWL made it easy to mix content from different ontologies unambiguouslydifferent ontologies unambiguously

The use of OWL made it easy to take The use of OWL made it easy to take advantage of information published in XML advantage of information published in XML on the webon the web e.g., foaf information, privacy policye.g., foaf information, privacy policy



(4)(4) TIVO for Mobile TIVO for Mobile ComputingComputing

A mobile computing vision and a problemA mobile computing vision and a problemDevices “broadcast” information and service Devices “broadcast” information and service descrip-tions via short-range RF (802.11, descrip-tions via short-range RF (802.11, Bluetooth, UWB, …) Bluetooth, UWB, …)

Moving people and devices can access this data, Moving people and devices can access this data, but only while in rangebut only while in range The data may be out of range when it’s neededThe data may be out of range when it’s needed

Devices must anticipate information need to Devices must anticipate information need to cache data when it’s availablecache data when it’s available Using user models, preferences, schedule, Using user models, preferences, schedule,

context, trust, …context, trust, … Compute a dynamic utility function to create a Compute a dynamic utility function to create a

“semantic” cache replacement algorithm“semantic” cache replacement algorithm



MoGATU’s distributed belief MoGATU’s distributed belief modelmodel

MoGATU is a data management module for MANETsMoGATU is a data management module for MANETs Devices send queries to peersDevices send queries to peers

Ask its vicinity for reputation of untrusted peers that responded Ask its vicinity for reputation of untrusted peers that responded -- trust a device if trusted before or if enough trusted peers trust -- trust a device if trusted before or if enough trusted peers trust itit

Use answers from (recommended to be) trusted Use answers from (recommended to be) trusted peers to determine answerpeers to determine answer

Update reputation/trust level for all responding Update reputation/trust level for all responding devicesdevices Trust level increases for devices giving what becomes final Trust level increases for devices giving what becomes final

answeranswer Trust level decreases for devices giving “wrong” answerTrust level decreases for devices giving “wrong” answer

Each devices builds Each devices builds a ring of trusta ring of trust……



A: Where is Bob?

C: I know where Bob is.

D: I know where Bob is.

B: I know where Bob is.



A: D, where is Bob?A: C, where is Bob?A: B, where is Bob?



C: A, Bob is at work.

D:A, Bob is home.

B: A, Bob is home.



A:B: Bob at home,C: Bob at work,D: Bob at home

A: I have enoughtrust in D. Whatabout B and C?



A: Do you trust C?

C: I always do.

D: I don’t.

B: I am not sure.

E: I don’t.

F: I do.

A:I don’t care what C says.

I don’t know enough about B, but I trust D, E, and F. Together,

they don’t trust C, so I won’t.



A: Do you trust B?

C: I never do.

D: I am not sure.

B: I do.

E: I do.

F: I am not sure.

A:I don’t care what B says.

I don’t trust C, but I trust D, E, and F. Together,

they trust B a little, so I will.



A: I trust B and D,both say Bob is

home…

A:Increase trust in D.

A:Decrease trust in C.

A:Increase trust in B.

A:Bob is home!




OWL was a good language for capturing OWL was a good language for capturing user profiles and the simple BDI models we user profiles and the simple BDI models we neededneeded

Any of several simple trust models increase Any of several simple trust models increase the accuracy of informationthe accuracy of information Designing a good trust model depends on Designing a good trust model depends on

the MANET assumptionsthe MANET assumptions As well as the level of cooperation and As well as the level of cooperation and

honestyhonesty Trading reputation information boosts the Trading reputation information boosts the

performance of the algorithmsperformance of the algorithms



(5)(5) Security in P2P Systems Security in P2P Systems Peer-to-peer systems are manifest at multiple Peer-to-peer systems are manifest at multiple

levels, such as ad hoc networking, file-sharing levels, such as ad hoc networking, file-sharing applications, and multiagent systems, applications, and multiagent systems,

Recognizing “bad actors” in P2P systems is Recognizing “bad actors” in P2P systems is hardhardActors might be out of range, having trouble, low on Actors might be out of range, having trouble, low on power, incompetent, uncooperative, or maliciouspower, incompetent, uncooperative, or malicious

P2P systems can be subverted by the P2P systems can be subverted by the introduction of malicious nodesintroduction of malicious nodese.g.: blackhole routers that do not forward packetse.g.: blackhole routers that do not forward packets

MANETS offer additional challengesMANETS offer additional challengesCross layer effects can be subtleCross layer effects can be subtle



Fostering cooperative behavior in Fostering cooperative behavior in MANETsMANETs

Agent recognizes good and badAgent recognizes good and badbehavior in their neighborsbehavior in their neighbors Kudos and accusations are signedKudos and accusations are signed

and sharedand shared Reputations emerge from theReputations emerge from the

corroborated and unchallenged observations corroborated and unchallenged observations and opinions at multiple layers (PHY, MAC, and opinions at multiple layers (PHY, MAC, NW, … App)NW, … App) Uncorroborated or false reports are noted too!Uncorroborated or false reports are noted too!

Agents use local policies, their own Agents use local policies, their own observations, and global reputation to make observations, and global reputation to make decisionsdecisions On communication, services, tasks, grouping, etc.On communication, services, tasks, grouping, etc.



Threats occur at every Threats occur at every levellevel

MAC/PHY level attacks MAC/PHY level attacks RTS, CTS attacks – gain unfair share of bandwidth, RTS, CTS attacks – gain unfair share of bandwidth,

disruptiondisruption Routing attacks Routing attacks

gray holes, black holes, worm holes …gray holes, black holes, worm holes … Attacks on data trafficAttacks on data traffic

Dropping, mangling or injecting data packetsDropping, mangling or injecting data packets Application vulnerabilities include resources Application vulnerabilities include resources

that lie, are poor reasoners, have dated that lie, are poor reasoners, have dated information, …information, … Identities, reputations, trust evolutionIdentities, reputations, trust evolution



Sophisticated attacks cross Sophisticated attacks cross layerslayers

Classical attacks are easy to detect using Classical attacks are easy to detect using thresholdsthresholds Packet dropping, mangling, misrouting etc.Packet dropping, mangling, misrouting etc. To evade detection attacker must stay under the To evade detection attacker must stay under the

detection threshold (insignificant disruption)detection threshold (insignificant disruption) More sophisticated attacks are possibleMore sophisticated attacks are possible

Launching attacks at multiple levels, e.g. Launching attacks at multiple levels, e.g. combining RTS attacks and packet dropscombining RTS attacks and packet drops

Any single attack signature might not suffice for Any single attack signature might not suffice for detectiondetection

Observations on a single layer in isolation will be Observations on a single layer in isolation will be inconclusiveinconclusive



Cross-layer AnalysisCross-layer Analysis

ApplicationApplication

TransportTransport

LinkLink

MAC/PHYMAC/PHY

CommendationsCommendationsAccusationsAccusations

(to other devices)(to other devices)

CommendationsCommendationsAccusationsAccusations

(to other devices)(to other devices)

Routing attacks,Routing attacks,disruptionsdisruptions

Routing attacks,Routing attacks,disruptionsdisruptions

Unfair contention,Unfair contention,JammingJamming

Unfair contention,Unfair contention,JammingJamming

IntrusionDetectionIntrusionDetection

ResponseResponse

Packet dropping,Packet dropping,Mangling, injectionMangling, injectionPacket dropping,Packet dropping,

Mangling, injectionMangling, injection

Trust evolution, reputation management,Trust evolution, reputation management,recourserecourse

Trust evolution, reputation management,Trust evolution, reputation management,recourserecourse




A neighborhood watch algorithm can be used A neighborhood watch algorithm can be used to detect bad actors in mobile ad hoc to detect bad actors in mobile ad hoc networksnetworks

Using an active algorithm decreases false Using an active algorithm decreases false positive at a modest decrease in throughputpositive at a modest decrease in throughput

Social reputation collected via voting is effectiveSocial reputation collected via voting is effective Other techniques can be added, such as Other techniques can be added, such as

random anonymous auditsrandom anonymous auditsWhich techniques are applicable depends on the Which techniques are applicable depends on the communication assumptions (e.g., can communication assumptions (e.g., can messages be “overheard”?)messages be “overheard”?)



Conclusions & final Conclusions & final thoughtsthoughts

(1) How do SW languages fit into current agent (1) How do SW languages fit into current agent technology?technology?

(2) SW and might be a chance for us to get some (2) SW and might be a chance for us to get some AI out of the labAI out of the lab

(3) Requirements for pervasive computing (3) Requirements for pervasive computing applications motivate agents and the SWapplications motivate agents and the SW

(3) How do we get there(3) How do we get there



How does SW fit into agent How does SW fit into agent frameworks?frameworks?

Most multiagent systems work assumes some Most multiagent systems work assumes some kind of infrastructure to support discovery, kind of infrastructure to support discovery, communication, cooperation, etc.communication, cooperation, etc. Cougaar, KQML+KIF, CoABS Grid, FIPA, etc.Cougaar, KQML+KIF, CoABS Grid, FIPA, etc.

We found it easy to fit RDF & OWL into FIPA’s We found it easy to fit RDF & OWL into FIPA’s framework and should fit well into others*framework and should fit well into others*

Big wins: (1) open, distributed, published Big wins: (1) open, distributed, published ontologies, (2) easy mixing of vocabulary, (3) ontologies, (2) easy mixing of vocabulary, (3) adoption path from RDF to OWL-lite to OWL to adoption path from RDF to OWL-lite to OWL to …, (4) web standards compatible…, (4) web standards compatible

* Your mileage may * Your mileage may varyvary



Rethinking agent communicationRethinking agent communicationThe MAS paradigm inherited communication The MAS paradigm inherited communication from client-server systems – messages from client-server systems – messages mediated by middleware. As with people, mediated by middleware. As with people, there are other waysthere are other ways Agents “publish” beliefs, requests, and other Agents “publish” beliefs, requests, and other

“speech acts” on web pages.“speech acts” on web pages. Brokers “search” for and “index” published Brokers “search” for and “index” published

contentcontent Agents “discover” what peers have published Agents “discover” what peers have published

on the web and browse for more detailson the web and browse for more details And comment on what they read…And comment on what they read… And others comment on what they said…And others comment on what they said…



The symbol grounding The symbol grounding problemproblem

Some doubt human-like AI is unlessSome doubt human-like AI is unlessmachines share our perception ofmachines share our perception ofthe worldthe world

One solution to the “symbolOne solution to the “symbolgrounding problem” is to givegrounding problem” is to giverobots human inspired sensesrobots human inspired senses

This has proven hard!This has proven hard! Maybe the Semantic Web is a way out of Maybe the Semantic Web is a way out of

this problem?this problem?

MIT’s Cog



Solving the symbol grounding Solving the symbol grounding problemproblem

The Web may be a common world that The Web may be a common world that both humans and machines can both humans and machines can understand.understand.

Confession: the web is more familiar Confession: the web is more familiar and real to me than much of the real and real to me than much of the real world.world.

Physical objects can be taggedPhysical objects can be taggedwith low cost (e.g., $0.05)with low cost (e.g., $0.05)transponders or RFID tagstransponders or RFID tagsencoding their URIsencoding their URIs



How do we get there from How do we get there from here?here?

This semantic web emphasizes This semantic web emphasizes ontologiesontologies: their development, use, : their development, use, mediation, evolution, ...mediation, evolution, ...

It will take some time to really deliver on It will take some time to really deliver on the agent paradigm, either on the Internet the agent paradigm, either on the Internet or in a pervasive computing environment.or in a pervasive computing environment.

The development of complex systems is The development of complex systems is basically an evolutionary process.basically an evolutionary process.

Random search carried out by tens of Random search carried out by tens of thousands of researchers, developers and thousands of researchers, developers and grad students.grad students.



ClimbingClimbingMountMountImprobableImprobable

“The sheer height of the peak doesn't matter, so long as you don't try to scale it in a single bound. Locate the mildly sloping path and, if you have unlimited time, the ascent is only as formidable as the next step.”

-- Richard Dawkins, Climbing MountImprobable, Penguin Books, 1996.



T.T.T: things take timeT.T.T: things take time Prior to the 1890’s, papers Prior to the 1890’s, papers

were held together with were held together with straight pens.straight pens.

The development of The development of “spring steel” allowed the “spring steel” allowed the invention of the paper clip invention of the paper clip in 1899.in 1899.

It took about It took about 25 years (!)25 years (!) for the evolution of the for the evolution of the modern “gem paperclip”, modern “gem paperclip”, considered to be optimal considered to be optimal for general use.for general use.



http://ebiquity.umbc.edu/http://ebiquity.umbc.edu/

1 agents meet the semantic web in the aether tim finin university of maryland, baltimore county...

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