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Situation Awareness: Dealing with Vague Context

Situation Awareness: Dealing with Vague Context. C. Anagnostopoulos, Y. Ntarladimas, S. Hadjiefthymiades P ervasive C omputing R esearch G roup C ommunication N etworks L aboratory Department Informatics and Telecommunications University of Athens – Greece ICPS 2006@Lyon.

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Situation Awareness: Dealing with Vague Context

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  1. Situation Awareness: Dealing with Vague Context C. Anagnostopoulos, Y. Ntarladimas, S. Hadjiefthymiades Pervasive Computing Research Group Communication Networks Laboratory Department Informatics and Telecommunications University of Athens – Greece ICPS 2006@Lyon

  2. Situation Awareness: A specific flavor of Context Awareness Situation : logically interrelated contexts The current user context is interpreted as the current situation Situation determination: denotes in which situation an entity might be involved and in what degree (situation reasoning) Situation reaction: denotes the triggering of specified rules Situation adaptation: denotes the application adaptation to the current situation

  3. Imprecise Context: Contextual information is vague and cannot always be retrieved Vague context implies inexact situation modeling, which implies approximate reasoning about situations Proposed Context Model: Deal with inexact situation determination through Fuzzy Inference rules The degree of situational involvement (situation reasoning) The past behavior of the user The degree of the application pervasiveness (elimination of the user intervention)

  4. Situation Awareness: Reasoning and Activation Determination Rules: (context could be imprecise due to limited, uncertain, inexact, missing resources,…) Activation Rules: Options = {‘take no action’, ‘notification’, ‘take action’} Reasoner determines which one of those options is the most suitable for the specified task related to current user situation The certainty on ‘take action’ is not the same as the uncertainty on ’take no action’

  5. Reasoning about Uncertainty Degrees of Certainty dINV = Degree of Situational Involvement: Denotes the level of a user involvement in a certain situation. The reasoner determines the appropriate option. dPER = Degree of Pervasiveness: Denotes whether the application is capable of reasoning about the user situation in order to ubiquitously take actions with, at least, the minimum number of user notifications/interruptions. The reasoner takes into account the past behavior of the user.

  6. QSituationΠ ( is Involved By. (Bob Π  has Time. Meeting Hour Π  is Located In. (Interior Room Π  contains. Manager) Π  has Business Role. Partner Π  has Business Role. Business Partner)) Formal MeetingMeetingΠ ( is Involved By. (Partner Π  has Time. Meeting Hour Π  is Located In. (Meeting Room Π  contains. Manager Π  contains. Business Partner) Π  has Business Role. Partner Π  has Business Role. Business Partner)) isInvolvedIn hasContext Situation Person Context Partner Worker Meeting Hour Jogging Meeting Checking E-mails Temporal Business Partner Manager Secretary Working Hour Q Meeting Area Indoor Space Formal Meeting part of+ Spatial Meeting Room Indoor Room Internal Meeting Business Meeting Conference Room Artifact Staff Room Manager Meeting Situation Modeling: Ontological Perspective subsumption relation (IS-A) concept PDA Profile Compatible With relation Disjoint With relation relation DL-Syntax of a situation • Situation = set of concepts from epistemic ontologies • Semantic Web Ontologies: • RDF • RDF(S) {is-a} • OWL-DL (Description Logics) • {existential/quantificational, cardinalityrestrictions}

  7. Temporal Ontology IS-A Q Situation Temporal Context  has Time Local Context Personal Context Time Meeting Time is Involved By  has Temporal Context Local Context AND  has Business Role  has Spatial Context Partner Role  has Business Role AND  has Entry AND  is Located In AND AND Person Bob Example: Q is-a situation, which…  contains  capacity 2 contains Not Alone Manager Interior Room Number Restriction Indoor Context User Profile Ontology Spatial Context Local Context Spatial Ontology IS-A Subsumption role Local Context x Contextual Information Role with semantics x {,}

  8. Situational Similarity : Conceptual Similarity between Situational OWL Concepts Similar situations means similarcontexts from specific ontologies Local Contexts qQ piP Situational Context similarity level-2 similarity level-1 similarity level-0

  9. P1 sim(Q,Pi) P2 Q Instancesin Ontology P3 … PN Reasoning about Situational Similarity • Reasoner Selects: • Most similar situation SMAX • Each situation that subsumes SMAX • Each situation compatible with SMAX • Each situation maximizing sim() • belonging to different taxonomy • that of SMAX

  10. Approximate Reasoning dINV = Degree of Situational Involvement Crisp Reasoning M: user is attending a meeting situation FM: user is attending a formal meeting situation CeM: user is checking his/her e-mails situation

  11. Uncertain decision is taken close to ‘notification’ boundaries inactive notifying active w dINV =0.5140 Crisp Fuzzy 0.0 0.1 0.5 0.6 0.8 ‘take no action’ ‘notification’ ‘take action’

  12. Let • T = A + B + C denote all the reasoner decisions related to the three options, where • A = number of the reasoner decisions related to the ‘take no action’ option • B = number of the reasoner decisions related to to the ‘notification’ option • C = number of the reasoner decisions related to the ‘take action’ option • p = the percentage of the user notifications/interruptions over T\A, dPER = Degree of Pervasiveness • High value of p means that: • Reasoner is either uncertain about the current situation or disregards past user actions • Implies low degree of pervasiveness • Notice: • Number A does not interpret that the system does not disturb the user. Instead, • the reasoner is certain that the user is not involved in the corresponding situation!

  13. Letq be the percentage of the user rejections on each received ‘notification’ over B, In case of rejection, the reasoner records the user reaction and attempts to adapt its decisions along with the current degree of situational involvement. Hence: dPER = Degree of Pervasiveness Notice: When dPER+dINV = 1, then the reasoner is equally certain about the current user situational involvement and the decision for the corresponding task execution

  14. Fuzzy Inference Rules Fuzzy Linguistic Variables The reasoner attempts to eliminate the ‘notification’ messages, or, at least, notify the user when necessary if dINV is low then dINVP is inactive if dINV is high then dINVP is active if dINV is medium and dPER is high then dINVP is active if dINV is medium and dPER is medium then dINVP is notifying if dINV is medium and dPER is low then dINVP is inactive

  15. S1 (dINV) S2 (dINVP) #occurrence % Total Ontologies: IEEE SUO Open Cyc DAML-Time/Time-Entry GUMO FOAF FIPA Reasoner: RACER-DL Fuzzy-JESS ‘take no action’ ’notification’ 43 21.5 21 10.5 ’notification’ ‘take action’ 64 32.0

  16. Thank you! Christos B. Anagnostopoulos {bleu@di.uoa.gr} Pervasive Computing Research Group {http://p-comp.di.uoa.gr}

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