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International Technology Alliance In Network & Information Sciences International Technology Alliance In Network & Information Sciences Dr David Mott IBM.

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Presentation on theme: "International Technology Alliance In Network & Information Sciences International Technology Alliance In Network & Information Sciences Dr David Mott IBM."— Presentation transcript:

1 International Technology Alliance In Network & Information Sciences International Technology Alliance In Network & Information Sciences Dr David Mott IBM UK Discussion with Prof Harold Boley 3 rd Dec 2009

2 [2] Suggested Agenda The ITA programme The RIF enterprise Your work How can we help?

3 [3] International Technology Alliance in Network and Information Sciences

4 [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 –1 st research defence collaboration between US (ARL) and UK (MOD) –Must be fundamental research published in open forum –Joint academia and industry, US and UK –

5 U.S. Gov. Industry Academia U.K. Gov. INDUSTRY 9.BBNT Solutions LLC 10.The Boeing Corporation 11.Honeywell Aerospace Electronic Systems 12.IBM Research 13.Klein Associates ACADEMIA 1.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. ACADEMIA 1.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 4 2 8 5 3 1 9 13 12 11 1 2 3 4 5 6 7 89 10 11 ITA Team Overview

6 [6] Plan Representation Analyzing Communication Patterns Interpretation of human activity Information Flow Analysis Interpretation of human activity Computer Mediated Interactions Cultural Modelling - Planning and Intent Battlefield II US/UK Technical Area 4 – Network Enabled Cognition Agent Support for ad-hoc Adaptive Teamwork Ontologies and Semantic Representations

7 [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 [8] Collaborative Planning Model

9 [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 [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 temporalPrecede, TemporalConstraint, TemporalEntity, TimeInterval, TimeLine, TimePoint spaceArea, Elevation, Line, Point, SpatialConstraint, SpatialCoordinateSystem, SpatialEntity, SpatialIntersection, SpatialLocation, SpatialUnion resourcesResource, ResourceAllocated, ResourceCapability, ResourceConstraint, ResourceQuantity, ResourceSet actionsActivity, Effect, Precondition collaborative problem solving Choice Point, Collaboration, Commitment, Communication, ConstraintViolated, Decision, GoalSpecification, Influence, Issue, JointPersistentGoal, MutualGoal, Problem, Solution, Trust, planningAllocation, Evaluation, EvaluationCriterion, InitialState, Plan, PlanTask, PlanTaskDescription, PlanTaskTemplate, PlanningProblem, PlanningProblemContext, ResourceCommitment, ResourceReq, TaskCommitment Copyright IBM UK Ltd, 2009

11 [11] Visualisation

12 CPM Visualiser CPM Solution Lexicon Import Export Rule Execution Rules Graphics Rationale Patterns CPM/OWL Controlled English Problem Solving Concept Modelling Collaboration Forms?? Copyright IBM UK Ltd, 2009

13 [13] Hybrid 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 [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 [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 [16] Higher Level CNLs

17 [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 [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 HIGH Linguistic transformation rule Language transformation Copyright IBM UK Ltd, 2009

19 [19] The RIF enterprise

20 [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 [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 [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 [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 [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 [25] Embedding RIF in OWL Dont 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 [26] Rationale

27 [27] Hmm…What about rationale? ITA CE Common Logic RDF/S/OWL RIF-FLD Reasoning Steps, Assumptions, Decisions, Facts Why?

28 [28] What is the best standard for representing rationale in all its complexities (including truth maintenance) ? Question Extend RIF? Bespoke? PML (from RPI)?

29 [29] premise A implies B A ________ 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 Proposition premise Premise propositions Conclusion propositions modus ponens

30 [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 entailment Universal

31 [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 [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 because assume universal Talking about space RDF Triple space reification support RS PI

33 [33] Approaches to Proposition reification Original CPM: Freeform: CE: RIF: RDF graph: P P P AND 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) CL RIF informal P …. (s1 p1 o1) RDF (s2 p2 o2) Copyright IBM UK Ltd, 2009

34 [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 is supported by the assumption that and that or by the assumption that or by the assumption that and that P Formula … ORAND AN support P URI_A1,URI_A2|URI_A3|URI_A5,URI_A6

35 [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 [36] Rationale Isa Common Logic ITA CE RDF/S/OWL RIF-FLD Reasoning Step Entailment RuleOfInference PropositionIndex (Rule) Proposition Frameslot (s p o) reifies type of rule of inference Rationale using RIF Proposition truth conditions EMBED agent Assumption VarBinding premises/ conclusions/ bindings Subclass support Formula Copyright IBM UK Ltd, 2009

37 [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 [38] Negation

39 [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) –Etheringtons semantics for default logic

40 [40] Semantic Web

41 [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 [42] Your Work How can we help the community? How can we collaborate?

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