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Explaining Task Processing in Cognitive Assistants that Learn
Deborah McGuinness1, Alyssa Glass1,2, Michael Wolverton2, Paulo
Pinheiro da Silva3*1Knowledge Systems, AI LaboratoryStanford
University{dlm | glass} @ksl.stanford.edu2SRI
[email protected] 3University of Texas El Paso**Work done
while on staff at Stanford [email protected]
*thanks to Li Ding, Cynthia Chang, Honglei Zeng, Vasco Furtado,
Jim Blythe, Karen Myers, Ken Conley, David Morley
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General MotivationInteroperability as systems use varied sources
and multiple information manipulation engines, they benefit more
from encodings that are shareable & interoperableProvenance if
users (humans and agents) are to use and integrate data from
unknown, unreliable, or evolving sources, they need provenance
metadata for evaluationExplanation/Justification if information has
been manipulated (i.e., by sound deduction or by heuristic
processes), information manipulation trace information should be
availableTrust if some sources are more trustworthy than others,
representations should be available to encode, propagate, combine,
and (appropriately) display trust valuesProvide interoperable
knowledge provenance infrastructure that supports explanations of
sources, assumptions, learned information, and answers as an
enabler for trust.
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Inference Web Infrastructure primary collaborators Ding, Chang,
Zeng, FikesFramework for explaining question answering tasks by
abstracting, storing, exchanging, combining, annotating, filtering,
segmenting, comparing, and rendering proofs and proof fragments
provided by question answerers.
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ICEE: Integrated Cognitive Explanation EnvironmentImprove Trust
in Cognitive Assistants that learn by providing transparency
concerning: * provenance * information manipulation * task
processing * learning
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Task Management FrameworkProcedure LearnersExecution
Monitor& PredictorProPLTask ManagerSPARKTime
ManagerPTIMEProcess ModelsTask
ExplainerICEEAdvicePreferencesTailor, LAPDOG, Execution
Monitor& PredictorProPLTask ManagerSPARKTime
ManagerPTIMEProcess ModelsTask
ExplainerICEEAdvicePreferencesPreferencesActivity
RecognizerLocationEstimatorPrimTL, PLOW
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ICEE ArchitectureCollaboration AgentJustification GeneratorTask
Manager (TM)TM WrapperExplanation DispatcherTM Explainer
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Task ExplanationAbility to ask why at any pointContextually
relevant responses (using current processing state and underlying
provenance)Context appropriate follow-up questions are
presented
Explanations generated completely automatically; No additional
work required by user to supply information
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Explainer StrategyPresent QueryAnswerAbstraction of
justification (using PML encodings)Provide access to meta
informationSuggest context-appropriate drill down options (also
provide feedback options)
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Sample Introspective Predicates:
ProvenanceAuthorModificationsAlgorithmAddition date/timeData
usedCollection time span for dataAuthor commentDelta from previous
versionLink to originalGlass, A., and McGuinness, D.L. 2006.
Introspective Predicates for Explaining Task Execution in CALO.
Technical Report, KSL-06-04, Knowledge Systems Lab., Stanford
Univ.
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Task Action SchemaWrapper extracts portions of task intention
structure through introspective predicatesStore extracted
information in action schemaDesigned to achieve three
criteria:Salience info relevant to information needsReusability
info usable by cognitive agent activities like procedure learning
or state estimationGenerality conceptual model appropriate for
action reasoning in bdi, blackboard systems, production systems,
etc.
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User Trust StudyInterviewed 10 Critical Learning Period (CLP)
participantsProgrammers, researchers, administratorsFocus of
study:TrustFailures, surprises, and other sources of
confusionDesired questions to ask CALOInitial results:Explanations
are required in order to trust agents that learnTo build trust,
users want transparency and provenanceIdentified question types
most important to CALO users --> motivation for future work
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Selected Future DirectionsBroaden explanation of learning (and
CALO integration) Explain learning by demonstration (integrating
initially with CALO component LAPDOG)Explain preference learning
(integrating initially with CALO component PTIME)Investigate
explanation of conflicts/failures. Explore this as feedback and a
driver to initiate learning procedure modifications or learning new
procedures.Expand dialogue-based interaction and presentation of
explanations (expanding our integration with Towel)Use trust study
results to prioritize provenance, strategy, and dialogue
work.Exploit our work on IW Trust - a method for representing,
propagating, and presenting trust within the CALO setting already
have results in intelligence analyst tools, integration with text
analytics, Wikipedia, likely to be used in IL, etc.
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Advantages to ICEE ApproachUnified framework for explaining task
execution and deductive reasoning, built on the Inference Web
infrastructure.Architecture for reuse among many task execution
systems.Introspective predicates and software wrapper that extract
explanation-relevant information from task reasoner.Reusable action
schema for representing task reasoning.
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ResourcesOverview of ICEE:Deborah McGuinness, Alyssa Glass,
Michael Wolverton and Paulo Pinheiro da Silva. Explaining Task
Processing in Cognitive Assistants That Learn. In the proc. of the
20th International FLAIRS Conference. Key, West, Florida, May 7-9,
2007.Introspective predicates:Glass, A., and McGuinness, D.L.
Introspective Predicates for Explaining Task Execution in CALO.
Technical Report, KSL-06-04, Knowledge Systems, AI Lab., Stanford
University, 2006.Video demonstration of
ICEE:http://iw.stanford.edu/2006/10/ICEE.640.movExplanation
interfaces:McGuinness, D.L., Ding, L., Glass, A., Chang, C., Zeng,
H., and Furtado, V. Explanation Interfaces for the Semantic Web:
Issues and Models. 3rd International Semantic Web User Interaction
Workshop (SWUI06). Co-located with the International Semantic Web
Conference, Athens, Georgia, 2006.Inference Web (including above
publications):http://iw.stanford.edu/
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Extra
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GS: GetSignatureBL: BuyLaptopGA:
GetApprovalSupportsTopLevelGoal(x) &
IntentionPreconditionMet(x) & TerminationConditionNotMet(x)
=> Executing(x)TopLevelGoal(y) & Supports(x,y) =>
SupportsTopLevelGoal(x)ParentOf (x,y) & Supports(y,z) =>
Supports (x,z)ParentOf (x,y) & Supports(y,z) => Supports
(x,z)Supports (x,x)
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Explaining Learning by DemonstrationGeneral MotivationLAPDOG
(Learning Assistant Procedures from Demonstration, Observation, and
Generalization) generalizes the users demonstration to learn a
procedureWhile LAPDOGs generalization process is designed to
produce reasonable procedures, it will occasionally get it
wrongSpecifically, it will occasionally over generalizeGeneralize
the wrong variables, or too many variablesProduce too general a
procedure because of a coarse-grained type hierarchyICEE needs to
explain the relevant aspects of the generalization process in a
user-friendly formatTo help the user identify and correct over
generalizationsTo help the user understand and trust the learned
proceduresSpecific elements of LAPDOG reasoning to
explainOntology-Based Parameter GeneralizationThe variables
(elements of the users demonstration) that LAPDOG chooses to
generalizeThe type hierarchy on which the generalization is
basedProcedure CompletionThe knowledge-producing actions that were
added to the demonstrationThe generalization done on those
actionsBackground knowledge that biases the learningE.g., rich
information about the email, calendar events, files, web pages, and
other objects upon which it executes it actionsPrimarily for future
versions of LAPDOG
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Explaining PreferencesGeneral MotivationPLIANT (Preference
Learning through Interactive Advisable Non-intrusive Training) uses
user-elicited preferences and past choices to learn user scheduling
preferences for PTIME, using a Support Vector Machine.Inconsistent
user preferences, over-constrained schedules, and necessity of
exploring the preference space result in user confusion about why a
schedule is being presented.Lack of user understanding of PLIANTs
updates creates confusion, mistrust, and the appearance that
preferences are being ignored.ICEE needs to provide justifications
of PLIANTs schedule suggestions, in a user-friendly format, without
requiring the user to understand SVM learning.Providing
Transparency into Preference LearningAugment PLIANT to gather
additional meta-information about the SVM itself:Support vectors
identified by SVMSupport vectors nearest to the query pointMargin
to the query pointAverage margin over all data pointsNon-support
vectors nearest to the query pointKernel transformation used, if
anyRepresent SVM learning and meta-information as justification in
PML, using added SVM rulesDesign abstraction strategies for
presenting justification to user as a similarity-based
explanation
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During the demo, notice:User can ask questions at any
timeReponses are context-sensitiveDependant on current task
processing state and on provenance of underlying
processExplanations generated completely automaticallyNo additional
work required by user to supply informationFollow-up questions
provide additional detail at users discretionAvoids needless
distraction
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Example Usage:Live Demo and/or Video Clip
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Future DirectionsBroaden explanation of learning and CALO
integration Explain learning by demonstration, integrating
initially with CALO component LAPDOGExplain preference learning,
integrating initially with CALO component PTIMEInvestigate
explanation of conflicts. Explore this as a driver to initiate
learning procedure modifications or learning new procedures.Expand
dialogue-based interaction and presentation of explanations,
expanding our integration with TowelWrite up and distribute trust
study (using our interviews with 10 year 3 CLP subjects). Use trust
study results to prioritize provenance, strategy, and dialogue
work.Potentially exploit our work on IW Trust - a method for
representing, propagating, and presenting trust within the CALO
setting already have results in intelligence analyst tools,
integration with text analytics, Wikipedia, likely to be used in
IL, etc.Continue discussions with:Tom Garvey about transition
opportunities to CPOFTom Dietterich about explanation-directed
learning and provenanceAdam Cheyer about explaining parts of the
OPIE environment
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How PML WorksJustification Trace IWBase
NodeSet foo:ns1(hasConclusion ) Query foo:query1
Question foo:question1 MappingNodeSet foo:ns2(hasConclusion )
SourceUsage
hasAnswerhasAntecendentfromQueryfromAnswerisQueryForInferenceEngine
InferenceRule
hasVariableMappinghasInferencEnginehasRuleInferenceStepLanguage
hasLanguageInferenceStepSource
isConsequentOfhasSourceUsagehasSource isConsequentOfusageTime
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Future DirectionsWe will leverage results from our trust study
to focus and prioritize our strategies explaining cognitive
assistants e.g., learning specific provenanceWe will expand our
explanations of learning to augment learning by instruction and
design and implement explanation of learning by demonstration
(initially focusing on LAPDOG).We will expand our initial design of
explaining preferences in PTIME Write up and distribute user trust
study to CALO participantsConsider using conflicts to drive
learning and explanations I have not finished because x has not
completed.Advanced dialogues exploiting TOWEL and other CALO
componentsPotentially exploit our work on IW Trust - a method for
representing, propagating, and presenting trust within the CALO
setting already have results in intelligence analyst tools,
integration with text analytics, Wikipedia, likely to be used in
IL, etc.
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Sample Task Hierarchy:Purchase equipmentPurchase
equipmentCollect requirementsGet quotesDo researchChoose set of
quotesPick single itemGet approvalPlace order
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Sample Task Hierarchy:Get travel authorizationGet travel
authorizationCollect requirementsGet approval, if necessaryNote:
this conditional step was added to the original procedure through
learning by instructionSubmit travel paperwork
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PML in Swoop
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Explaining Extracted EntitiesSource: fbi_01.txtSource Usage:
span from 01 to 78 This extractor decided that Person_fbi-01.txt_46
is a Person and not OccupationSame conclusion from multiple
extractors conflicting conclusion from one extractor
As web applications proliferate, more users (both people and
agents) find themselves faced with decisions about when and why to
trust application advice. In order to trust information obtained
from arbitrary applications, users need to understand how the
information was obtained and what it depended upon. Particularly in
web applications that may use question answering systems that may
be heuristic or incomplete or data that is either of unknown origin
or may be out of date, it becomes more important to have
information about how answers were obtained. Emerging web systems
will return answers augmented with Meta information about how
answers were obtained. In this talk, Deborah McGuinness will
describe an approach that can improve trust in answers generated
from web applications by making the answer process more
transparent. The added information is aimed to provide users
(humans or agents) with answers to questions of trust, reliability,
recency, and applicability. While this is an area of active
research, there are technologies and implementations that can be
used today to increase application trustability. The talk will
include descriptions of a few representative applications using
this approach.Dr. Deborah McGuinness a leading expert in
ontology-based tools and applications, knowledge representation and
reasoning languages. She is co-editor of the Ontology Web Language.
Deborah runs the Stanford Inference Web (IW) effort, which provides
a framework for explaining answers from heterogeneous web
applications.Inference Web is joint work with Pinheiro da Silva,
Fikes, Chang, Glass, Ding, Deshwal, Narayanan, Miller, Zeng,
Jenkins, Millar, Bhaowal, User can ask questions at any
timeReponses are context-sensitive; Dependant on current task
processing state and on provenance of underlying
processExplanations generated completely automatically; No
additional work required by user to supply informationFollow-up
questions provide additional detail at users discretion; Avoids
needless distraction
Salience. The wrapper should obtain information about an agents
processing that is likely to address some possible user information
needs.Reusability. The wrapper should obtain information that is
also useful in other cognitive agent activities that require
reasoning about actionfor example, state estimation and procedure
learning.Generality. The schema should represent action information
in as general a way as possible, covering the action reasoning of
blackboard systems, production systems, and other agent
architectures.CLP == Critical Learning Period. This was the 2-week
data-gathering exercise that was the basis for the "with learning"
portion (as opposed to the no-learning baseline) of the year-end
test. Learning Assistant Procedures from Demonstration,
Observation, and Generalization". PLIANT == Preference Learning
through Interactive Advisable Nonintrusive Training