1. 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

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 d INV = Degree of Situational Involvement: Denotes the level of a user involvement in a certain situation. The reasoner determines the appropriate option. d PER = 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. Situation Modeling : Ontological Perspective SituationPersonContext Meeting Formal Meeting Internal Meeting Manager Meeting Temporal Spatial Artifact Meeting Hour Working Hour Indoor Space Indoor Room Meeting Area Meeting Room Staff Room Partner Manager Business Partner isInvolvedInhasContext part of+ Checking E-mails Jogging subsumption relation (IS-A) Compatible With relation relation concept Conference Room Business Meeting Worker Secretary PDA Profile Disjoint With relation Q Q  Situation Π (  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  Meeting Π (  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)) Situation = set of concepts from epistemic ontologies Semantic Web Ontologies: RDF RDF(S) {is-a} OWL-DL (Description Logics) {existential/quantificational, cardinality restrictions} DL-Syntax of a situation

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

8. Situational Similarity : Conceptual Similarity between Situational OWL Concepts Similar situations means similar contexts from specific ontologies qQqQpiPpiP similarity level-0 Local Contexts similarity level-1 similarity level-2 Situational Context

9. Reasoning about Situational Similarity Q P1P1 P2P2 P3P3 PNPN … sim(Q,P i ) Reasoner Selects: Most similar situation S MAX Each situation that subsumes S MAX Each situation compatible with S MAX Each situation maximizing sim() belonging to different taxonomy that of S MAX Instances in Ontology

10. d INV = Degree of Situational Involvement Approximate Reasoning 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 0.10.50.80.60.0 ‘take no action’ ‘notification’‘take action’ inactivenotifyingactive d INV =0.5140 Crisp Fuzzy w

12. d PER = Degree of Pervasiveness 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, 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. d PER = Degree of Pervasiveness Let q 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 : Notice : When d PER +d INV = 1, then the reasoner is equally certain about the current user situational involvement and the decision for the corresponding task execution

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

15. S 1 (d INV ) S 2 (d INVP ) #occurrence % Total ’notification’ ‘take no action’ ’notification’‘take action’ 43 21 64 21.5 10.5 32.0 Ontologies : IEEE SUO Open Cyc DAML-Time/Time-Entry GUMO FOAF FIPA Reasoner: RACER-DL Fuzzy-JESS

16. Thank you! Christos B. Anagnostopoulos {bleu@di.uoa.gr}{bleu@di.uoa.gr P ervasive C omputing R esearch G roup {http://p-comp.di.uoa.gr}http://p-comp.di.uoa.gr