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Objective of the paper

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Objective of the paper

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  1. Strategies and Techniques for Use and Exploitation of Contextual Information in High-Level Fusion ArchitecturesJuan Gómez RomeroMichael KandeferJesúsGarcía Michael PrenticeMiguel Á. Patricio Stuart C. ShapiroJosé M. Molina James LlinasApplied Artificial Intelligence Research Group (GIAA)Department of Computer ScienceUniversity Carlos III of Madrid, SpainCenter for Multisource Information FusionDepartment of Computer Science and EngineeringUniversity at Buffalo, New York, USA

  2. Objective of the paper To present and discuss some ideas on the use and exploitation of Contextual Information (CI)in Information Fusion (IF) and High-Level IF (HLIF) • Starting point for discussions, not final conclusions • Some ideas acquired from the experience in Vision Systems and Information Fusion research • Joint conversations between researchers at UB and UC3M

  3. Outline • Definitions and Role of CI in HLIF • Applications in Video Surveillance: Context-Aware Scene Recognition and Feedback • Applications in Counter-Insurgency (COIN): Context-Based Information Retrieval from Text Sources • Conclusions

  4. Categories of data in IF • Four categories of information can be applied to any IF problem: • Observational data • A priori knowledge models • Learned knowledge • Contextual information

  5. Categories of data in IF • Four categories of information can be applied to any IF problem: • Observational data • A priori knowledge models • Learned knowledge • Contextual information (CI) Information that “surrounds” a situation of interest in the world Set of constraints to a reasoning process about a situation

  6. Categories of data in IF • The frontier between categories is fuzzy and application-dependent • Most systems are largely based on observational data and a priori knowledge models (well-studied, well-behaved problems), but there are: • Problems in which context is a critical aid to estimation • COIN / Irregular Warfare (UB) • Problems in which the context is the estimation object • Ambient Intelligence and Context-Aware Systems (UC3M) • Problems in which the context determines the IF process • Computer Vision and Surveillance (UC3M)

  7. Definition of CI in IF • Dey and Abowd (2001): • “Any information (either implicit or explicit) that can be used to characterize the situation of an entity” • Henricksen (2003): • “The context of a task is the set of circumstances surrounding it that are potentially of relevance to its completion” • Kandefer and Shapiro (2008): • “The structured set of variable, external constraints to some (natural or artificial) cognitive process that influences the behavior of that process in the agent(s) under consideration”

  8. Definition of CI in IF • Contextual information can be: • static or dynamic, • descriptive or deductive, • general or specific, • external or internal, • etc. • Middleware may be required • Full characterization and specification of CI may not be possible to be known at design time, except in very closed worlds

  9. Possible Frameworks for CI exploitation • “A priori” framework • Accounts for the effect on situational estimation of that CI that is known at design time (“a priori”) • This CI should be easily incorporated to the fusion procedures (hard-wired) • It produces hybrid fusion methods, maybe numerical/symbolical

  10. “A priori” framework

  11. Possible Frameworks for CI exploitation • “A posteriori” framework • Sometimes, the integration of all CI in the IF algorithm/system is not possible: • All relevant CI may not be known or available at system/algorithm design time • CI may not be of a type that was integrated into the system/algorithm at design time and so may not be able to be easily integrated in the situation estimation process • CI exploitation is an additional process that performs several tasks: • Retrieval of relevant CI from available sources • Consistency checking (fusion hypothesis and relevant CI) • Augmentation, embellishment of fused results • Supporting of possible L4 adaptive operations

  12. “A posteriori” framework

  13. Outline • Definitions and Role of CI in HLIF • Applications in Video Surveillance: Context-Aware Scene Recognition and Feedback • Applications in Counter-Insurgency COIN: Context-Based Information Retrieval from Text Sources • Conclusions

  14. Video Surveillance Applications at UC3M • Next-generation (third generation) vision systems: • Multi-camera • Spread of resources • “Cognitive” abilities: independence, cooperation, adaptation, etc. • CI is required to outperform current limitations • UC3M proposal • Symbolic representation of scenes • Formal reasoning for HLIF

  15. “A posteriori” architecture

  16. Detail of the Context Layer • Modular ontologies to represent the scene (perceptions + context) • Abductive reasoning to obtain high-level knowledge from low-level data • Feedback loop to enhance the tracking algorithm according to the interpreted scene • Communication through the GTL/CL interface to decouple CI exploitation and usual tracking procedures

  17. Outline • Definitions and Role of CI in HLIF • Applications in Video Surveillance: Context-Aware Scene Recognition and Feedback • Applications in Counter-Insurgency (COIN): Context-Based Information Retrieval from Text Sources • Conclusions

  18. COIN Text Data Interpretation Applications at UB • Context-based Information Retrieval (CBIR) • Pre-processing procedure to enhance the knowledge that is available to the reasoner in a general inference problem • In IF, CI is used to: • Determine which sensor data is relevant to the fusion process • Complete sensor data with annotations based on background knowledge to be used by a reasoner • Counterinsurgency domain at UB • CBIR to automatically detect interesting information in COIN text messages and support decision-making

  19. “A posteriori” architecture Extended Graph Information Text Message 02/10/07 American contractor in Yarmuk said ambulance service is deteriorating; he told of a friend who suffered a heart attack in Qahtan Square, a short distance south of the hospital. His friend had to wait nearly an hour before the ambulance arrived. Critical Infrastructures located at provided at Health Service City Sreet Ambulance Service Yarmuk Quahtan Sq. Context-based Information Retrieval (CBIR) Contextual Constraints Contextual Information Forward-Inference Enhanced Information Background Knowledge Sources (BKS) Ontologies Previous Situations Axioms

  20. Outline • Definitions and Role of CI in HLIF • Applications in Video Surveillance: Context-Aware Scene Recognition and Feedback • Applications in Counter-Insurgency (COIN): Context-Based Information Retrieval from Text Sources • Conclusions

  21. Conclusions • Modern IF applications require employment and exploitation of all available information • Dynamic and difficult problems • Observational data is not enough • CI helps in the estimation of fused states • It comes at a price: • Complexity • Precision: Soft (AI-based) vs. Hard (statistical) approaches • Some successful approaches are being developed • Fusion community has to make an effort to leverage holistic research strategies and develop joint initiatives

  22. Thank You!Questions And Comments

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