2/7/06

A challenge problem project on knowledge representation A challenge problem project on knowledge representation sponsored by DTOsponsored by DTO

Interoperable Knowledge Representation Interoperable Knowledge Representation for Intelligence Support (IKRIS)for Intelligence Support (IKRIS)

Technical Team LeadersTechnical Team Leaders

Prof. Richard FikesProf. Richard Fikes Dr. Christopher WeltyDr. Christopher WeltyKKnowledge nowledge SSystems,ystems,Knowledge Structures GroupKnowledge Structures Group

Artificial Intelligence Artificial Intelligence LLaboratory (KSL)aboratory (KSL) T. J. Watson Research CenterT. J. Watson Research CenterStanford UniversityStanford University IBM CorporationIBM Corporation

Government ChampionsGovernment Champions Steve CookSteve Cook (NSA) (NSA) Jean-Michel PomaredeJean-Michel Pomarede (CIA) (CIA)

John DonelanJohn Donelan (CIA) (CIA) John WalkerJohn Walker (NSA) (NSA)

Northeast Regional Research Center LeadersNortheast Regional Research Center Leaders Dr. Brant CheikesDr. Brant Cheikes (MITRE) (MITRE) Dr. Mark MayburyDr. Mark Maybury (MITRE) (MITRE)

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Knowledge Representation and ReasoningKnowledge Representation and Reasoning Knowledge RepresentationKnowledge Representation

Encoding descriptions – Encoding descriptions –

> That correspond in some coherent way to a world of interestThat correspond in some coherent way to a world of interest

> Are usable by a computer to make conclusions about that worldAre usable by a computer to make conclusions about that world

Primary areas of activity:Primary areas of activity:

> Developing declarative formalisms for expressing knowledgeDeveloping declarative formalisms for expressing knowledge

– Mostly “general-purpose” languages (e.g., First-order logic)Mostly “general-purpose” languages (e.g., First-order logic)

> Encoding knowledge (knowledge engineering)Encoding knowledge (knowledge engineering)

– Mostly identifying and describing conceptual vocabularies (ontologies)Mostly identifying and describing conceptual vocabularies (ontologies)

ReasoningReasoning Automating coherent creation of new knowledge from existing knowledgeAutomating coherent creation of new knowledge from existing knowledge Primary areas of activity:Primary areas of activity:

> Development and analysis of computational reasoning methodsDevelopment and analysis of computational reasoning methods

– Task-specific methods such as planning, scheduling, diagnosis, …Task-specific methods such as planning, scheduling, diagnosis, …

– Methods for managing reasoning such as hybrid reasoning, …Methods for managing reasoning such as hybrid reasoning, …

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Challenge Problems for the ICChallenge Problems for the IC

DTO (Disruptive Technology Office) funded challenge problem projectsDTO (Disruptive Technology Office) funded challenge problem projects

Focus is on problems that require collaboration to solveFocus is on problems that require collaboration to solve

DTO recognizes knowledge representation (KR) as a critical technologyDTO recognizes knowledge representation (KR) as a critical technology

IKRIS is addressing two KR challengesIKRIS is addressing two KR challenges

Enabling interoperability of KR technologies Enabling interoperability of KR technologies

> Developed by multiple contractorsDeveloped by multiple contractors

> Designed to perform different tasksDesigned to perform different tasks

Interoperable representations of scenarios and contextualized knowledgeInteroperable representations of scenarios and contextualized knowledge

> To support automated analytical reasoning about alternative hypothesesTo support automated analytical reasoning about alternative hypotheses

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Hypothesis Modeling and AnalysisHypothesis Modeling and Analysis

What’s the current What’s the current situation?situation?

What’s going What’s going to happen?to happen?

What What happened?happened?

…

Tools for modeling and analyzing alternative hypothetical scenariosTools for modeling and analyzing alternative hypothetical scenarios

Models enable automated reasoning to accelerate and deepen analysisModels enable automated reasoning to accelerate and deepen analysis Consistency and plausibility checking, deductive question-answering, Consistency and plausibility checking, deductive question-answering,

hypothesis generation, …hypothesis generation, …

Requires sophisticated knowledge representation technologyRequires sophisticated knowledge representation technology Actions, events, “abnormal” cases, alternatives, open-ended domains, …Actions, events, “abnormal” cases, alternatives, open-ended domains, …

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Interoperable KR TechnologyInteroperable KR Technology No one representation language is suitable for all purposesNo one representation language is suitable for all purposes

Technology development necessarily involves exploring alternativesTechnology development necessarily involves exploring alternatives Differing tasks require differing representation languages Differing tasks require differing representation languages

So, modules using differing KR languages need to be interoperableSo, modules using differing KR languages need to be interoperable Requires enabling modules to use each other’s knowledgeRequires enabling modules to use each other’s knowledge

The IKRIS approach to achieving interoperability – The IKRIS approach to achieving interoperability – Select and refine a standard knowledge Select and refine a standard knowledge interchangeinterchange language language

> Called IKRIS Knowledge Language (IKL)Called IKRIS Knowledge Language (IKL)

Develop translators to and from IKLDevelop translators to and from IKL

Each system module will then –Each system module will then – Use its own KR language internallyUse its own KR language internally Use IKL for inter-module communicationUse IKL for inter-module communication Translate knowledge to and from IKL as neededTranslate knowledge to and from IKL as needed

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IKRIS OrganizationIKRIS Organization Prime ContractorPrime Contractor – MITRE, – MITRE, Brant Cheikes and Mark MayburyBrant Cheikes and Mark Maybury

Technical Team LeadsTechnical Team Leads – Fikes – Fikes (Stanford KSL)(Stanford KSL) and Welty and Welty (IBM Watson)(IBM Watson)

Working GroupsWorking Groups InteroperabilityInteroperability – – Pat HayesPat Hayes, , University of West Florida

Chris Menzel, Michael Witbrock, John Sowa, Bill Andersen, Deb McGuinness, …

ScenariosScenarios – – Jerry HobbsJerry Hobbs, , Information Sciences Institute

Michael Gruninger, Drew McDermott, , David Martin, Selmer Bringsjord, …, …

ContextsContexts – – Selene MakariosSelene Makarios, , Stanford KSL

Danny Bobrow, Valeria de Paiva, Charles Klein, David Israel, …

EvaluationEvaluation – – Dave ThurmanDave Thurman, , Battelle Memorial Institute

Technology TransferTechnology Transfer – – Paula CowleyPaula Cowley, , Pacific Northwest National Laboratory

Translation technology and example translatorsTranslation technology and example translators – Stanford KSL – Stanford KSL

Government ChampionsGovernment Champions – – Steve Cook, John Donelan, Jean-Michel Pomarede, John WalkerSteve Cook, John Donelan, Jean-Michel Pomarede, John Walker

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IKRIS Project ScheduleIKRIS Project Schedule Preparation – January - April, 2005Preparation – January - April, 2005

Kickoff Meeting – April 2005Kickoff Meeting – April 2005

Established working groups and their chartersEstablished working groups and their charters

Developed work plan and began work in each groupDeveloped work plan and began work in each group

Working groups – April 2005 through April 2006Working groups – April 2005 through April 2006

Producing results and planning technology transferProducing results and planning technology transfer

Evaluation – January through September 2006Evaluation – January through September 2006

Iterative evaluation of workshop resultsIterative evaluation of workshop results

Second face-to-face workshop – April 2006Second face-to-face workshop – April 2006

Finalize and coordinate results of working groupsFinalize and coordinate results of working groups

Finalize plans for technology transition and for completing evaluationFinalize plans for technology transition and for completing evaluation

Technology transition – April through September 2006Technology transition – April through September 2006

Initiation of planned transition activitiesInitiation of planned transition activities

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FOL Knowledge Interchange LanguagesFOL Knowledge Interchange Languages KIF (Knowledge Interchange Format)KIF (Knowledge Interchange Format)

ASCII Lisp-style syntaxASCII Lisp-style syntax No formal model theoryNo formal model theory Pre-WWW/XML/UnicodePre-WWW/XML/Unicode Included a set theory, definition language, etc.Included a set theory, definition language, etc. Subset became de facto AI/KR standardSubset became de facto AI/KR standard Subset developed as a proposed ANSI standardSubset developed as a proposed ANSI standard

CL (Common Logic)CL (Common Logic) Based on KIFBased on KIF Formal model theory (based on Menzel/Hayes)Formal model theory (based on Menzel/Hayes) Abstract syntaxAbstract syntax ““Web savvy”Web savvy” In final stages of becoming an ISO standardIn final stages of becoming an ISO standard

IKL (IKRIS Knowledge Language)IKL (IKRIS Knowledge Language) Extension of CLExtension of CL Extensions include propositions, quotingExtensions include propositions, quoting

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CLIF Syntax for IKLCLIF Syntax for IKL

Designed for use on an open networkDesigned for use on an open network Names are made globally unique by – Names are made globally unique by –

> Including a URI as part of the nameIncluding a URI as part of the name

> Using the XML namespace conventions to abbreviate namesUsing the XML namespace conventions to abbreviate names

Universal quantifiers can be restricted by a unary predicateUniversal quantifiers can be restricted by a unary predicate

E.g., “All humans own a car.”E.g., “All humans own a car.”

(forall ((x isHuman)) (exists ((y Car)) (Owns x y))) (forall ((x isHuman)) (exists ((y Car)) (Owns x y)))

Existential quantifiers can be restricted by a numberExistential quantifiers can be restricted by a number

E.g., “All humans have as parts 10 toes.”E.g., “All humans have as parts 10 toes.”

(forall ((x isHuman)) (forall ((x isHuman)) (exists 10 (y) (and (Toe y) (PartOf y x))))(exists 10 (y) (and (Toe y) (PartOf y x))))

Cool!

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Examples of CL/IKL ExpressivityExamples of CL/IKL Expressivity Relations and functions are in the universe of discourseRelations and functions are in the universe of discourse

E.g.,E.g., (owl:inverseOf parent child)(owl:inverseOf parent child)

A relation or function can be represented by a termA relation or function can be represented by a term

E.g.,E.g., (forall (x y r) (iff (r x y) ((owl:inverseOf r) y (forall (x y r) (iff (r x y) ((owl:inverseOf r) y x)))x)))

Given the above axiom,Given the above axiom,

((owl:inverseOf parent) Arthur Ygrain) ((owl:inverseOf parent) Arthur Ygrain)

is equivalent to – is equivalent to –

(child Arthur Ygrain) (child Arthur Ygrain)

and entailsand entails

(parent Ygrain Arthur) (parent Ygrain Arthur)

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Examples of CL/IKL ExpressivityExamples of CL/IKL Expressivity A unary relation could be allowed to take multiple argumentsA unary relation could be allowed to take multiple arguments

So that, e.g., So that, e.g., (isHuman Fred Bill Mary)(isHuman Fred Bill Mary)

abbreviates abbreviates

(and (isHuman Fred) (isHuman Bill) (isHuman Mary))(and (isHuman Fred) (isHuman Bill) (isHuman Mary))

We might call such relations “Predicative”We might call such relations “Predicative”

E.g., assert E.g., assert (Predicative isHuman)(Predicative isHuman)

What it means to be Predicative could be axiomatized as follows – What it means to be Predicative could be axiomatized as follows – (forall ((forall (rr) (if (Predicative ) (if (Predicative rr) )

(forall (x y z) (iff ((forall (x y z) (iff (rr x y z) x y z)

(and ((and (rr x) ( x) (rr y) ( y) (rr z)))))) z))))))

Predicative itself could be Predicative – Predicative itself could be Predicative – (Predicative Predicative)(Predicative Predicative)

allowing such abbreviations asallowing such abbreviations as (Predicative isHuman isAnimal isFish)(Predicative isHuman isAnimal isFish)

WOW!

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Examples of CL/IKL ExpressivityExamples of CL/IKL Expressivity

Sequence namesSequence names Allows a sentence to stand for an infinite number of sentences, each Allows a sentence to stand for an infinite number of sentences, each

obtained by replacing each sequence name by a finite sequence of namesobtained by replacing each sequence name by a finite sequence of names

A sequence name is any constant beginning with “…”A sequence name is any constant beginning with “…”

E.g., the general axiom for Predicative is as follows:E.g., the general axiom for Predicative is as follows:

(forall (r) (if (Predicative r)(forall (r) (if (Predicative r)

   (forall (x y ...) (iff (r x y ...)   (forall (x y ...) (iff (r x y ...)

(and (r x) (r y ...))))))(and (r x) (r y ...))))))

Function “list” and relation “isList” are predefined as follows:Function “list” and relation “isList” are predefined as follows:

(forall (...) (isList (list ...)))(forall (...) (isList (list ...)))

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Goal: Support representation of contextualized and modal knowledgeGoal: Support representation of contextualized and modal knowledge Achieved by making propositions first-class entities in IKLAchieved by making propositions first-class entities in IKL

> Refer to them by name, quantify over them, have relations between them and Refer to them by name, quantify over them, have relations between them and other entities, define functions that apply to them, … other entities, define functions that apply to them, …

> Technically, a proposition is a 0-arity relationTechnically, a proposition is a 0-arity relation

The operator The operator thatthat is used to denote propositions is used to denote propositions thatthat takes a sentence as an argument takes a sentence as an argument

E.g., E.g., (that (Married Ygrain Uther))(that (Married Ygrain Uther))

A A thatthat expression denotes the proposition expressed by its argument expression denotes the proposition expressed by its argument

E.g., E.g., (that (Married Ygrain Uther))(that (Married Ygrain Uther))is a name, denoting the is a name, denoting the proposition that proposition that Ygrain and Uther are marriedYgrain and Uther are married

Issue: When are two propositions equivalent?Issue: When are two propositions equivalent?E.g., does E.g., does (and a b)(and a b) name the same proposition as name the same proposition as (and b a)(and b a)?? IKL provides a propositional equivalence relation, but does not build it inIKL provides a propositional equivalence relation, but does not build it in General propositional equivalence is undecidableGeneral propositional equivalence is undecidable

Extending CL to Include PropositionsExtending CL to Include Propositions

BAM!

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Relativizing Names in IKLRelativizing Names in IKL In some cases, the denotation of logical names needs to be In some cases, the denotation of logical names needs to be

relativizedrelativized(believes Mary(believes Mary

(that (forall (x) (if (Child x Joe) (Male x))))(that (forall (x) (if (Child x Joe) (Male x))))

… … but what if Mary thinks Frank is Joe?but what if Mary thinks Frank is Joe? Need to talk about “mary’s version of Joe”Need to talk about “mary’s version of Joe”

Special class of functions: quoted namesSpecial class of functions: quoted names ‘‘name’ is a function that returns the “right thing”name’ is a function that returns the “right thing”

> (‘Joe’) is just Joe(‘Joe’) is just Joe> (‘Joe’ Mary) would be Frank (what ‘Joe’ denotes to Mary)(‘Joe’ Mary) would be Frank (what ‘Joe’ denotes to Mary)> E.g.E.g.

(believes (Mary(believes (Mary(forall (x) (if (Child x (‘Joe’ Mary)) (Male x))))(forall (x) (if (Child x (‘Joe’ Mary)) (Male x))))

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IKRIS Language TranslatorsIKRIS Language Translators Developing 2-way IKL translators for several KR Developing 2-way IKL translators for several KR

languageslanguages OWL, RDF, KIF, CycL, Slate/MSLOWL, RDF, KIF, CycL, Slate/MSL

API for parsing/generating IKLAPI for parsing/generating IKL Design goal: “round trip” complianceDesign goal: “round trip” compliance

Significant new work in KRSignificant new work in KR Major challenge to round trip OWLMajor challenge to round trip OWL

> Simple “embedding” in IKLSimple “embedding” in IKL> Requires “axiom patterns” and meta-dataRequires “axiom patterns” and meta-data

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

(owl:subclassOf P Q)))(owl:subclassOf P Q)))

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Interoperable ScenariosInteroperable Scenarios

IKRIS is addressing two KR challengesIKRIS is addressing two KR challenges

Enabling interoperability of KR technologies Enabling interoperability of KR technologies

> Developed by multiple contractorsDeveloped by multiple contractors

> Designed to perform different tasksDesigned to perform different tasks

Interoperable representations of Interoperable representations of scenariosscenarios and contextualized knowledge and contextualized knowledge

> To support automated analytical reasoning about alternative hypothesesTo support automated analytical reasoning about alternative hypotheses

Developing an interoperable representation for processesDeveloping an interoperable representation for processes

Includes – Includes –

> Time points, time intervals, durations, clock time, and calendar dates Time points, time intervals, durations, clock time, and calendar dates

> Events and relationships that overlap in time and interact Events and relationships that overlap in time and interact

> Process constructs, preconditions, states, etc.Process constructs, preconditions, states, etc.

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SWSL/FLOWS

SPARK ResearchCyc

PSL

inter-theory

OWL-S

An Interlingua for ProcessesAn Interlingua for Processes

DONE!

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The Scenarios Inter-Theory (ISIT)The Scenarios Inter-Theory (ISIT) The Scenarios Working Group is producing an IKL inter-theoryThe Scenarios Working Group is producing an IKL inter-theory

vocabularyvocabulary

Bridging axioms Bridging axioms to other vocabulariesto other vocabularies

Trigger axioms Trigger axioms for making optional representational commitmentsfor making optional representational commitments

The inter-theory vocabulary includes – The inter-theory vocabulary includes –

The OWL time ontologyThe OWL time ontology

> Terminology for clock time, calendars, intervals, points, etc. Terminology for clock time, calendars, intervals, points, etc.

Terms such as the following to describe processes:Terms such as the following to describe processes:

> EventEvent

> EventTypeEventType

> StateState

> StateTypeStateType

> EventualityEventuality

> EventualityTypeEventualityType

> FluentForFluentFor

> SubeventSubevent

> PreconditionPrecondition

> PreconditionTokenPreconditionToken

> EffectEffect

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ISIT Bridging AxiomsISIT Bridging Axioms

Example Example bridging axioms bridging axioms to Cyc for Event and EventType:to Cyc for Event and EventType:

““For every EventType x, there is a Cyc subclass of cyc:Event that has the For every EventType x, there is a Cyc subclass of cyc:Event that has the same instances as x”same instances as x”

(forall ((x EventType)))(forall ((x EventType)))

(exists (y) (and (cyc:genls y cyc:Event)(exists (y) (and (cyc:genls y cyc:Event)

(forall (e) (iff (cyc:isa e y)(forall (e) (iff (cyc:isa e y)

(instanceOf e x)))))))(instanceOf e x)))))))

““For every subclass y of Cyc:Event, there is an EventType that has the For every subclass y of Cyc:Event, there is an EventType that has the same instances as y”same instances as y”

(forall (y) (if (cyc:genls y cyc:Event)(forall (y) (if (cyc:genls y cyc:Event)

(exists (x) (and (EventType x)(exists (x) (and (EventType x)

(forall (e) (forall (e)

(iff (cyc:isa e y)(iff (cyc:isa e y)

(instanceOf e x)))))))(instanceOf e x)))))))

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ISIT Trigger AxiomsISIT Trigger Axioms

Example trigger axioms for Cyc event/token distinctionExample trigger axioms for Cyc event/token distinction

In Cyc, EventTypes are classes and events are individualsIn Cyc, EventTypes are classes and events are individuals

> The inter-theory is neutral on the issueThe inter-theory is neutral on the issue

> A commitment can be made on this issue using a triggering axiomsA commitment can be made on this issue using a triggering axioms

““If the TypesAreClasses trigger is true, EventTypes and the subclasses of If the TypesAreClasses trigger is true, EventTypes and the subclasses of Cyc:Events are equivalent”Cyc:Events are equivalent”

(forall (x) (if (TypesAreClasses)(forall (x) (if (TypesAreClasses) (iff (cyc:genls x cyc:Event) (EventType x))))(iff (cyc:genls x cyc:Event) (EventType x))))

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ISIT ModulesISIT Modules Pre/Post conditionsPre/Post conditions

Classic AI-planning descriptionsClassic AI-planning descriptions Triggering axioms for situations vs. flowsTriggering axioms for situations vs. flows

CausalityCausality Can an event Can an event causecause an event? an event? Expected outcomes…Expected outcomes… Triggering axioms identify the distinctionTriggering axioms identify the distinction

Inputs/OutputsInputs/Outputs Processes (esp. information processing) can have inputs and Processes (esp. information processing) can have inputs and

outputs (different from pre/post conditions)outputs (different from pre/post conditions) Control FlowControl Flow

Are if/then/while important to model logically?Are if/then/while important to model logically? Still under discussionStill under discussion

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IS IT an Ontology?IS IT an Ontology?

ISIT ISIT includesincludes the five ontologies the five ontologiesNew vocabulary for generalizations of New vocabulary for generalizations of

common termscommon termsTrigger axioms exclude parts of the Inter Trigger axioms exclude parts of the Inter

Theory under certain conditionsTheory under certain conditions

In a strict sense, it is not an ontology, but an In a strict sense, it is not an ontology, but an amalgem of existing ontologies…amalgem of existing ontologies… Pan-ontology?Pan-ontology?

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Interoperable Contextualized KnowledgeInteroperable Contextualized Knowledge

IKRIS is addressing two KR challengesIKRIS is addressing two KR challenges

Enabling interoperability of KR technologies Enabling interoperability of KR technologies

> Developed by multiple contractorsDeveloped by multiple contractors

> Designed to perform different tasksDesigned to perform different tasks

Interoperable representations of scenarios and Interoperable representations of scenarios and contextualized contextualized

knowledgeknowledge

> To support automated analytical reasoning about alternative To support automated analytical reasoning about alternative

hypotheseshypotheses

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Contextualized Knowledge is PervasiveContextualized Knowledge is Pervasive The circumstances surrounding a specific activityThe circumstances surrounding a specific activity

E.g., E.g., In this conversationIn this conversation, ‘the suspect’ refers to Faris., ‘the suspect’ refers to Faris.

A published documentA published document

E.g., E.g., Based on the scheduleBased on the schedule, the Holland Queen will arrive in Boston sometime on April 29, , the Holland Queen will arrive in Boston sometime on April 29, and depart there sometime on May 1.and depart there sometime on May 1.

An intelligence reportAn intelligence report

E.g., Pakes is listed, E.g., Pakes is listed, according to a certain sourceaccording to a certain source, on the crew roster of the Holland , on the crew roster of the Holland Queen.Queen.

A databaseA database

E.g., Pakes is assumed, E.g., Pakes is assumed, based on certain recordsbased on certain records, to not be a citizen of USA., to not be a citizen of USA.

An assumptionAn assumption

E.g., Pakes’s presence on board the Holland Queen is E.g., Pakes’s presence on board the Holland Queen is assumed to be typicalassumed to be typical (i.e. he does (i.e. he does not behave abnormally).not behave abnormally).

A set of beliefsA set of beliefs

E.g., E.g., In the belief system of Abu Musab al ZarqawiIn the belief system of Abu Musab al Zarqawi, democracy is evil., democracy is evil.

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Interoperable Contextualized KnowledgeInteroperable Contextualized Knowledge

IKRIS is producing – IKRIS is producing – A context logic with a formal model theoryA context logic with a formal model theory

> Called IKRIS Context Logic (ICL)Called IKRIS Context Logic (ICL)

Recommended ways of using the logic for IC applications Recommended ways of using the logic for IC applications

E.g., to represent alternative hypothetical scenariosE.g., to represent alternative hypothetical scenarios

Methodology for translating into and out of IKLMethodology for translating into and out of IKL

Methodology for automated reasoningMethodology for automated reasoning

The model theory supports configurable entailmentsThe model theory supports configurable entailments

Three immediate customersThree immediate customers PARC, Cycorp, KANIPARC, Cycorp, KANI

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Context LogicContext Logic In McCarthy’s context logic – In McCarthy’s context logic –

Contexts are primitive entitiesContexts are primitive entities Propositions can be asserted with respect to a contextPropositions can be asserted with respect to a context

> (ist c (ist c )) means that proposition means that proposition is true is true in context cin context c

E.g.,E.g., (ist CM (forall (x) (implies (P x) (G x)))); (ist C0 (P Fred))

How can automated reasoning be done with ist sentences?How can automated reasoning be done with ist sentences?

E.g., assert E.g., assert (= CM C0)(= CM C0) and derive and derive (ist C0 (G Fred))

Contextualize constants rather than sentencesContextualize constants rather than sentences Constants in ist sentences are interpreted with respect to the contextConstants in ist sentences are interpreted with respect to the context

E.g., Fred in E.g., Fred in (ist C0 (P Fred)) is interpreted with respect to is interpreted with respect to C0

Replace each constant with a function of the context and the constantReplace each constant with a function of the context and the constant

E.g.,E.g., { (forall (x) (implies (P (iso CM x)) (G (iso CM x))));

(P (iso C0 Fred)) }

Use a first-order reasoner to make deductionsUse a first-order reasoner to make deductionsWhoa!

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KANI’s Hypothesis GraphKANI’s Hypothesis Graph

S1: There will be a coordinated event.S2: The event will occur on April 30.S3: Pakes is a participant.S4: Ramazi is a participant.S5: Goba is a participant. …

N1

S8: The event is a face-to-face meeting.N2

S9: The event is at Select Gourmet Foods.

N3 S10: The event is in Atlanta.N4

S11: Pakes is in Boston on April 30.N5New New

hypothesihypothesis added by s added by the analystthe analyst

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Conflict Detected by KANIConflict Detected by KANI

S1: There will be a coordinated event.S2: The event will occur on April 30.S2: The event will occur on April 30.S3: Pakes is a participant.S3: Pakes is a participant.S4: Ramazi is a participant.S5: Goba is a participant. …

N1

S8: The event is a face-to-face meeting.S8: The event is a face-to-face meeting.N2

S9: The event is at Select Gourmet Foods.

N3 S10: The event is in Atlanta.S10: The event is in Atlanta.N4

S11: Pakes is in Boston on April 30.S11: Pakes is in Boston on April 30.N5

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Helping Resolve InconsistenciesHelping Resolve InconsistenciesS1,S4,S5,…N1

~S2,S3S3N1.1 S2S2,~S3N1.2 S2,S3S2,S3N1.3

S8S8N2.1

S9N3.1

S10S10N4.1

S11S11N5.1

S8S8N2.2

S9N3.2

S10S10N4.2

S11S11N5.2

~S8N2.3 S8S8N2

S9N3.3

S10S10N4.3

S11S11N5.3

S9N3.3

~S10N4.4 S10S10N4

~S11N5.5

Event will not Event will not occur on April 30occur on April 30

Pakes is Pakes is not a not a

participantparticipant

Event is Event is not a not a

face-to-face-to-face face

meetingmeeting

Event is Event is not in not in

AtlantaAtlanta

Pakes is Pakes is not in not in

Boston on Boston on April 30April 30

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Evaluation and Tech TransferEvaluation and Tech Transfer EvaluationEvaluation

Goals: Goals: > Demonstrate the practical usability of results on IC-relevant problemsDemonstrate the practical usability of results on IC-relevant problems

> Provide functionality goals, scoping, and feedback for resultsProvide functionality goals, scoping, and feedback for results

Evaluation will be informal using sample IC tasksEvaluation will be informal using sample IC tasks

Tests will include – Tests will include –

> Round trip translations into and out of IKLRound trip translations into and out of IKL

> Inter-system knowledge exchange using IKL.Inter-system knowledge exchange using IKL.

Tech TransferTech Transfer

Goal: Transition results into DTO programs and the IC at largeGoal: Transition results into DTO programs and the IC at large

Producing “showcase” presentations of results for transition audiences Producing “showcase” presentations of results for transition audiences

Being advised and facilitated by our government champions and MITREBeing advised and facilitated by our government champions and MITRE

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Using CS4 to Demonstrate IKRIS TechnologyUsing CS4 to Demonstrate IKRIS Technology

Our demonstration shows interoperability and Our demonstration shows interoperability and collaboration among three selected NIMD technologies: collaboration among three selected NIMD technologies: KANI, SLATE, and NoöscapeKANI, SLATE, and Noöscape

Two motivations for interoperationTwo motivations for interoperation Different (overlapping) dataDifferent (overlapping) data

> The CS4 was carefully enhanced and partitioned so no system by itself The CS4 was carefully enhanced and partitioned so no system by itself had sufficient knowledge to “solve” CS4had sufficient knowledge to “solve” CS4

Different (overlapping) capabilitiesDifferent (overlapping) capabilities To be successful, each had to call upon the resources of To be successful, each had to call upon the resources of

the others.the others. Translators are being developed to support the knowledge Translators are being developed to support the knowledge

representation languages needed to support those representation languages needed to support those systems and to enable knowledge sharing.systems and to enable knowledge sharing.

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SummarySummary

IKRIS is enabling progress to be made on significant KR&R problemsIKRIS is enabling progress to be made on significant KR&R problems

We are addressing two KR challenges relevant to the ICWe are addressing two KR challenges relevant to the IC

Enabling interoperability of KR technologies Enabling interoperability of KR technologies

> Developed by multiple contractorsDeveloped by multiple contractors

> Designed to perform different tasksDesigned to perform different tasks

Interoperable representations of scenarios and contextualized knowledgeInteroperable representations of scenarios and contextualized knowledge

> To support automated analytical reasoning about alternative hypothesesTo support automated analytical reasoning about alternative hypotheses

Initial versions of the technical results have been completedInitial versions of the technical results have been completed

For more information, check out the IKRIS Web siteFor more information, check out the IKRIS Web site

http://nrrc.mitre.org/NRRC/ikris.htmhttp://nrrc.mitre.org/NRRC/ikris.htm