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GILA Explanation Component (Phase 2)

GILA Explanation Component (Phase 2). Oct 5, 2008. Outline. Overview Background Conceptual Model Implementation Browser2: GILA log data browser TW OIE: OWL Instance Data Evaluation GilaExplainer: GILA log analyzer and explainer Future Work. Overview. Overview.

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GILA Explanation Component (Phase 2)

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  1. GILA Explanation Component(Phase 2) Oct 5, 2008

  2. Outline • Overview • Background • Conceptual Model • Implementation • Browser2: GILA log data browser • TW OIE: OWL Instance Data Evaluation • GilaExplainer: GILA log analyzer and explainer • Future Work

  3. Overview

  4. Overview • GILA is a general purposed integrated multi-agent platform for learning and problem-solving • GILA aims at solving problems by learning from several examples • Currently GILA is evaluated by a conflict resolution scenario in battle field air space control domain • Each run of GILA produces an OWL log recording how agents are learning and solving problem • Explanation component is needed to show how the background knowledge and examples are used in learning and solving problems

  5. Research Problems • GILA log validation • Overall log data connectivity validation (e.g. CC analysis on instance data) • Individual log data structure validation (e.g. check GILA log using integrity constrains from GILA ontologies ) • Explain data flow • Associate final solution with initial conflicts • Attribute the contribution of ILRs • Associate solutions with the provided knowledge

  6. Background

  7. GILA Data Driven Computing • [User] asks [MRE-DM] to initiate a task • To learn from experts’ knowledge • one expert trace • several expert exercises • To solve ACO Problem • [MRE-DM] queues the tasks, i.e. learning, CPL demonstration, CPL-training, performance, and run one task a time • [MRE-DM] informs [ALL] task begins • [ALL] work with each other • [LearnerX] Ask a Problem on BB • [LearnerY] Reply with the corresponding Solution on BB • [LearnerY] Reply with No-More-Solution on BB • [MRE-DM] informs [ALL] task ends (succeed or failed)

  8. Abstract GILA Data Flow Provided Knowledge runtime input prior input facts embedded in the input problem multi-phase iteration Experts’ execution trace 1: learning Q/A from users background knowledge Learned Knowledge problem/solution conflict priority 2. performance constraints constraint-violation Final Output final solution ExecutionTrace

  9. Prior Knowledge from Exports • learning mode • One expert trace • execution trace • initial state • final state • CPL (demonstration; practice) mode • Several exercises • initial state • final state • performance mode • One problem, i.e. the initial state

  10. GILA Knowledge

  11. GILA Ontology Dependency (imports) Graph Domain Knowledge GIL - ACO domain (gilaco) Constraint (cons) Abstract Steps (Asteps) Partial-plan Steps (Psteps) Sensing Steps (Psteps) World Knowledge Airspace Control Order (aco) GILA Inter-component Language (gilcore) PML-Justification (pmlj) legend General Knowledge Spatial Temporal (gkst) PML-Provenance (pmlp) new changed Data Structure (ds) unchanged

  12. Conceptual Model

  13. Event based Provenance Modelfor one-step in the log about … State i Input Data Input Data Agent Event Operation Time Location about State i+1 Output Data … Output Data Can be referred as PML Lite Ontology

  14. Example ACO State i ACO WorkingACMReq1 ACO State i+1 ACO WorkingACMReq2

  15. Implementation

  16. Browser2: GILA log data browser • GILA log consists of OWL instances, and they are interconnected • This tool let users • navigate instances by their connections • look into detailed description of instances • Note • Some links may fail because not all GILA ontologies are available on the Web.

  17. List all OWL Instances by Type

  18. Navigate One Instance and its related Instances type outlink Details inlinks

  19. Show the details of an Instance and its embedded isntances

  20. TW OIE: OWL Instance Data Evaluation • Motivation • Log entries are encoded as OWL instance data • As log entries are generated by ILRs and MREs, they may miss some required fields • OWL instance Data checks integrity constraints • e.g. missing property value, unspecific instance type • Currently implemented using SPARQL • http://onto.rpi.edu/demo/oie/

  21. Load an instance file

  22. Evaluation Result

  23. GilaExplainer: Explaining GILA log • Extract generic structure from log • Generate PML Lite relation from GILA log • Convert RDF graph to Instance graph • SPARQL based Explanation Template

  24. Focus on subject of Resource (which is described) Skip classes and properties RDF graph V.S. Instance Graph ACM1_ACM2_CONFLIT Has Identifier Conflict1 hasConflictingACMs hasConflictingACMs ACM1 ACM2 Has Identifier Has Identifier “ACM F4” “ACM FUEL 1” Shape2Oval

  25. Connectivity Analysis (Initial Results) • Goal: to check if GILA-log is well-connected • Input data • OWL file, 22M • No blank node. • Approach • Create instance graph from RDF graph • Initial results: • Many Islands, e.g. instance of constraint, not linking to any other instances • One Big connected component (2M) • Some small components (about 5K)

  26. Multi-Step Explanation for End Users IntersectionDetails Conflict ACMReq (Initial state) ACM • Which ACMs have been changed? • via which psteps by whom? • How each conflict is resolved • by which psteps with what constraint blame ACM’ ACMReq’ (final state) Constraint Psteps <owl:Class rdf:about="http://www.mindswap.org/2006/GILA/GK/gkst.owl#IntersectionDetails">

  27. Sparql based Explanation Template“List ID of all ACMs involved in conflict” PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> PREFIX aco: <http://www.atl.lmco.com/aco.owl#> SELECT ?conflictid ?acmid FROM <http://tw.rpi.edu/2008/gila/log1/g_con.rdf> WHERE { ?acm rdf:type aco:ACMDescriptor . ?acm aco:hasIdentifier ?acmid . ?conflict rdf:type aco:Conflict . ?conflict aco:hasIdentifier ?conflictid . ?conflict aco:hasConflictingACMs ?acm . ?conflict aco:hasConflictingACMs ?acm .}

  28. Future Work • Scalable GILA log storage and reasoning • Deal with log dumps generated by different system executions • Scalable Reasoning and Query support • Enriching Explanation • More log entries from ILRs and MREs • Finer reference to provided knowledge • User-defined explanation • Knowledge discovery • Duplicated entries • Log summary • Identify patterns of ILRs’ solutions, uncover interesting/strange behaviors • A frequent set of behavior patterns, which are explainable to end-users, shared by GILA components

  29. Backup

  30. Log Entity Duplication Detection • Goal: detect individual duplication using CWA. • Observations: • There could be some duplicated OWL individuals in log that can be detected by • IFP (one identical property-value pair) • Identical KEY ( multiple identical PV pairs) • Identical content ( all identical PV pairs) • We may need to ignore temporal aspect, and adhere to Close World Assumption for now • GILA ontology has not IFP defined • Directions • Efficient duplication detection using hash function? • Simple delta computation?

  31. Log summary • Goal: Provide human operator summary (at different granularity?) • Hypothesized summary entries • Overall size in terms of bytes, triples, resources, literals… • Topology analysis, e.g. connected components • Term frequency, e.g. list of # of class instances • …

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