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International Technology AllianceIn Network & Information Sciences
International Technology AllianceIn Network & Information Sciences
Dr David MottIBM UK
Dr David MottIBM UK
Discussion with Prof Harold Boley3rd Dec 2009
Discussion with Prof Harold Boley3rd Dec 2009
[2]
Suggested Agenda
• The ITA programme
• The “RIF enterprise”
• Your work
• How can we help?
[3]
International Technology Alliance in Network and Information Sciences
[4]
ITA Programme
• Focus– Enabling coalition operations over collaborative network centric systems
• Technical Areas– TA1 Network Theory
– TA2 Security across a system of systems
– TA3 Sensor information processing and delivery
– TA4 Distributed Network Enabled Cognition
• Aspects– From 2006 to 2011
– 1st research defence collaboration between US (ARL) and UK (MOD)
– Must be fundamental research published in open forum
– Joint academia and industry, US and UK
– http://usukita.org/
U.S.Gov.
Industry
Academia
U.K.Gov.
INDUSTRY9. BBNT Solutions LLC
10.The Boeing Corporation
11.Honeywell Aerospace Electronic Systems
12. IBM Research
13.Klein Associates
ACADEMIA1. Carnegie Mellon University
2. City University of New York
3. Columbia University
4. Pennsylvania State University
5. Rensselaer Polytechnic Institute
6. University of California Los Angeles
7. University of Maryland
8. University of Massachusetts
INDUSTRY 8. IBM UK
9. LogicalCMG
10.Roke Manor Research Ltd.
11.Systems Engineering& Assessment Ltd.
ACADEMIA1. Cranfield University, Royal Military
College of Science, Shrivenham
2. Imperial College, London
3. Royal Holloway University of London
4. University of Aberdeen
5. University of Cambridge
6. University of Southampton
7. University of York
7
10
6
42
853
1
9
1312
11
123
4
5
6
7
8 91011
ITA Team Overview
[6]
Plan Representation
AnalyzingCommunication Patterns
Interpretation of human activity
Information Flow Analysis
Interpretation of human activity
Computer Mediated Interactions
Cultural Modelling - Planning and Intent
Battlefield IIUS/UK
Technical Area 4 – Network Enabled Cognition
Agent Support for ad-hoc Adaptive Teamwork
Ontologies and Semantic
Representations
[7]
• How can a shared understanding of a plan or other “artifact” be obtained?
– How can plan details be communicated and understood across different planners?
– How can a Commander describe his intent and rationale to the planners and operations staff?
ITA Project 12 Task 3 research focus
[8]
Collaborative Planning Model
[9]
Plan representation
Visualisation
CNL Rationale
Digitised Semantics
Representation rich expression of problems and their
solutions structure and logical relations/rules based on generic, re-useable domain
concepts formal, unambiguous, semantics
Rationale for explanation of intent, beliefs and assumptions
Layers of Controlled Natural Languages for human communication
Visualisation for creation and exploration of solutions
Semantic representation for machine processing and formal definition of logical relations
Towards A Solution
[10]
Collaborative Problem Solving Model
basic logic and rationale
Agent, Assumption, ConceptualSpace, Container, Entailment, Inconsistency, PossibleWorld, Proposition, PropositionIndex, Quantity, ReasoningStep, Set, Triple, VarBinding, WorldState
general purpose
ConceptualThing, Constraint, Synchronisation
temporal Precede, TemporalConstraint, TemporalEntity, TimeInterval, TimeLine, TimePoint
space Area, Elevation, Line, Point, SpatialConstraint, SpatialCoordinateSystem, SpatialEntity, SpatialIntersection, SpatialLocation, SpatialUnion
resources Resource, ResourceAllocated, ResourceCapability, ResourceConstraint, ResourceQuantity, ResourceSet
actions Activity, Effect, Precondition
collaborative problem solving
Choice Point, Collaboration, Commitment, Communication, ConstraintViolated, Decision, GoalSpecification, Influence, Issue, JointPersistentGoal, MutualGoal, Problem, Solution, Trust,
planning Allocation, Evaluation, EvaluationCriterion, InitialState, Plan, PlanTask, PlanTaskDescription, PlanTaskTemplate, PlanningProblem, PlanningProblemContext, ResourceCommitment, ResourceReq, TaskCommitment
Copyright IBM UK Ltd, 2009
[11]
Visualisation
CPM Visualiser
CPM
Solution
Lexicon
Import E
xport
Rule E
xecution
Rules
Graphics
Rationale
Patterns
CPM/OWL
Controlled English
Problem Solving
Concept Modelling
Collaboration
Forms??
Copyright IBM UK Ltd, 2009
[13]
Hybrid Visualisation of RationaleHybrid Visualisation of Rationale
Exploring CNL
Editors
Assumptions, decisions and key facts leading to
resource conflict
Chain of Rationale in CE and Conceptual Graphs
Temporal rationale
Copyright IBM UK Ltd, 2009
[14]
Visualising Rules
A Crate must contain only ammunition of the correct type
“pick any thing from the crate and it have the correct content type”:
if
( the crate C holds the thing S ) and
( the crate C has the value A as content type )
then
( the thing S has the value A as type )
.
[15]
Evolving Design, Evolving Language
15
What do the yellow
areas mean?
How do I represent this in a
language?
the AS90 uses the NATO_L15 for the bombardment at a rate of 2 .
if ( the resource request RR is required by the bombardment T ) and ( the bombardment T has the AS90 A as executor ) and ( the resource request RR requires the NATO_L15 R )
and ( the bombardment T has the value D as duration ) and ( the value Q = the value D * the value 2 )
then ( the resource request RR has the value Q as quantity )
linguistic transformation rule
LOW
HIGH
Copyright IBM UK Ltd, 2009
[16]
Higher Level CNLs
[17]
“Levels of Language”
girl pick fruit. turn. see mammoth. girl run. reach tree. climb. mammoth shake tree. girl yell yell. father run. throw spear. mammoth roar. fall. father take stone. cut meat. give girl. girl eat finish. sleep.
girl pick fruit. turn. see mammoth. she run to tree and climb it. mammoth shake tree. girl yell yell. father run toward her. he throw spear at mammoth. it roar and fall. with stone father cut meat for girl. she eat finish and she sleep.
(from The Unfolding of Language, Guy Deutscher)
elegance, succinctness, specialist
verbose, awkward, genericLOW
HIGH
+ content and function words and grammatical constructs
[18]
at ~~root the ~~noun 1 only ~~verbSing 1 the ~~noun 2 ==> if ( the ~~noun 1 X ~~verbSing 1 the thing Y ) then ( the thing Y is a ~~noun 2 ).
the AS90 only fires the NATO_L15.
if ( the AS90 X fires the thing Y ) then ( the thing Y is a NATO_L15 ) .
LOW
HIGHLinguistic transformation rule
Language transformation
Copyright IBM UK Ltd, 2009
[19]
The “RIF enterprise”
[20]
To express more complex logic of an ontology in a general Mathematical form in an XML syntactic form
To embed the XML syntax in RDF/S/OWL To express the logic in an English like way To represent rationale in the XML syntax
The syntax of the language must have a corresponding formal semantics
All languages in the solution must be formally mappable between each other
Based on standards where possible
Requirements
[21]
Ideally the mathematical logic should be highly expressive, eg First Order Predicate Logic
In practice we may have to accept a less expressive language, especially if based on standards
Therefore Need a full FOPL language as the “gold standard” for
expressing all that we might need Use a subset of FOPL as being the base logic for our
language But must be more expressive than RDF/S/OWL
Requirements - relaxed
[22]
Common Logic
Common Logic as the “gold standard” logic language because:
an ISO standard for FOPL provides a Mathematical form (CLIF) that is readable
(forall … (if (and …) … (earliestfinish t x)))
(ITA) Controlled English for the “human-face” of the logic because:
it is intended as a CNL for CommonLogic we have used it in ITA and have parsers, inferences etc if ( the task T has the value X as earliest start time and has the value MD as minimum duration ) and ( the value X1 = the value X + the value MD ) then ( the task T has the value X1 as earliest completion time ) .
ITA CE
Components selected (1)
[23]
RDF/S/OWL as the semantic web language(s) because it is the SWT standard we have used it already on ITA
RIF as the specification of logic because it is an emerging W3C standard for rule interchange framework for defining different logic subsets focus on definition of semantics Based on:
RIF-FLD RDF compatibility mapping from RIF-BLD to
RDF/S/OWL Other extensions as defined (eg assumptions,
negation)
RIF
RDF/S/OWL
Components selected (2)
[24]
Common Logic
ITA CE
RDF/S/OWL
RIF-FLD
key ITA work to be done
RIF-FLD & CL semantics
RIF-FLD
Sowa
Integration
negation?
Copyright IBM UK Ltd, 2009
[25]
Embedding RIF in OWL
• Don’t want to have different files for RIF and OWL
• Ontology for RIF (“rir”)– rir: Document, rir:Group, rir:Formula, rir:Isa, rir:Frame, rir:And,
rir:Implies, rir:Forall, …
– (rir:Formula used loosely to be Formula or Isa, And, Or etc)
– Variables and Constants interchangeable, via http://rifInRDF#Var and http://rifInRDF#Const datatypes
– Universal rules used to define logical implications, via a fixed Document/Group structure
– Set of universal rules attached to ontology via “LogicSet” entity
[26]
Rationale
[27]
Hmm…What about rationale?
ITA CE
Common Logic
RDF/S/OWL
RIF-FLD
Reasoning Steps,
Assumptions, Decisions,
Facts
Why?
[28]
What is the best standard for representing rationale in all its complexities
(including truth maintenance)
?
Question
Extend RIF?
Bespoke?
PML (from RPI)?
[29]
premise
A implies B A________ B
A implies B
A implies B not B___________ not A
modus ponens modus tollens
Rules of Inference and ReasoningSteps
Logical inference
proposition
A ReasoningStep is an entailment … or an intuition? … or an illogical piece of reasoning?
Entailment
Proposition(logical inference)
Proposition
VarBinding
conclusion
Rule of Inference
Entailment
Reasoning Step
Propositionpremise
Premise propositions
Conclusion propositions
“modus ponens”
[30]
Support and Rationale
• Support is the pathway from propositions (universal, inferred and assumptions) to other propositions via entailments (reasoning steps)
• It is possible to:– generate rationale graphs of a fact, showing the true and false pathways, and the relevant
assumptions and reports.
– Detect incompatible sets of assumptions
– Make and undo assumptions, recalculate the truth values of dependent facts
– Explore possible worlds
E
E
Inferred proposition
True Support
E
False Support
Universal
Assumption
Assumption
entailmentUniversal
[31]
Examples of Rationale
• …but simple concepts lead rapidly to apparently complex support
• Better ways to visualise this are needed ….
• The removal of assumptions can resolve inconsistencies
[32]
Talking about: I assume/decide/believe/because of Solution must have:
explicit support to propositions (ReasoningStep and PropositionIndex)
a “magic” reification step
Proposition (s1 p1 o1)
(s2 p2 o2)
(s3 p3 o3)
Rationale is “talking about”
Proposition
Proposition
because
assume
universal
“Talking about” space RDF Triple space
reificationsupport
RS
PI
PI
[33]
Approaches to Proposition reification
• Original CPM:
• Freeform:
• CE:
• RIF:
• RDF graph:
P
PPAND
Triple
Triple
(s1 p1 o1)
(s2 p2 o2)
P “I decided to move the tanks forward over the hill”
P “the task T realises the objective O”
P Formula … FrameSlot (s1 p1 o1)
(s1 p1 o1)CL
RIF
informal
P “<rdf:rdf> …. </rdf>” (s1 p1 o1)RDF(s2 p2 o2)
(s2 p2 o2)
Copyright IBM UK Ltd, 2009
[34]
Truth Values (New)
• Truth is not defined by the existence of an RDF triple but the rationale support
• ATMS “label” permits efficient calculation of truth conditions of a proposition (hence triples)– Proposition P: (OR (AND A1 A2) A3 (A5 A6)) where AN are
(atomic?) propositions
• RIF: the label as a property of Proposition:
• Bespoke efficient property of Proposition:
• CE sentence: that <P> is supported by the assumption that <A1> and that <A2> or by the assumption that <A3> or by the assumption that <A5> and that <A6>
P Formula OR AND ANsupport
P support “URI_A1,URI_A2|URI_A3|URI_A5,URI_A6”
[35]
Summary of rationale using RIF
• “RIR”: a set of RDF types and properties to represent a RIF-FLD ontology embedded in an RDF document
• An Entailment that has:– Premise propositions (including rules)
– Conclusion propositions
– Variable bindings
• A Proposition has truth support defined by PropositionIndex(es)– permits defeasible reasoning and paraconsistency, possible
worlds, modelling of agents beliefs, etc
• A Proposition points to rir:Formula (isa, frameslot…)
• The rir:Frameslot, rir:Isa (#), rir:Subclass (##) reify to RDF triples, as per the RIF compatibility document
[36]
Rationale
Isa
Common Logic
ITA CE
RDF/S/OWL
RIF-FLD
ReasoningStep
Entailment
RuleOfInference
PropositionIndex
(Rule) Proposition
Frameslot
(s p o)
reifiestype of
rule of inference
Rationale using RIF
Proposition
truth conditionsEMBED
agent
Assumption
VarBinding
premises/conclusions/
bindings
Subclasssupport
Formula
Copyright IBM UK Ltd, 2009
[37]
RATIONALE
Integration of Rationale
Common Logic
ITA CE
RDF/S/OWL
RIF-FLD
key ITA work to be done
RIF-FLD & CL semantics
RIF-FLD
Sowa
Copyright IBM UK Ltd, 2009
[38]
Negation
[39]
(Tentative) Approach to Negation
• Create RIF Dialect– Retain RIF-FLD mapping to RDF/S/OWL
• Symmetric negation as classical negation:– Neg operator
– CE: “it is false that …”
– Neg premise only matches a Neg fact (?)
– Classical semantics
• Default negation as assumption based default reasoning– Naf operator
– CE: “if condition1 and it is assumed false that … then …
– Assumption permitted as long as its not inconsistent (or use ATMS)
– Etherington’s semantics for default logic
[40]
Semantic Web
[41]
Semantic & User Interface Research
• How can the semantic web be used to:– harness collective intelligence
– support the collective endeavour of groups of people
• Semantic Wikis– Use of Semantic MediaWiki and a CNL interface to
collaboratively construct ontologies
• Graphical Queries– graphical drawing of SPARQL queries
– Visual Query Builder at the level of the conceptual model
• Semantic Web techniques– SWEDER - Semantic Wrapping of data sources and rules
– GIDS – Framework for distributed access to interlinked data
[42]
Your Work
How can we help the community?
How can we collaborate?
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