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Ambient Intelligence and Social Awareness

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Ambient Intelligence and Social Awareness Powered By Docstoc
					Towards Decentralized
Communities and
Social Awareness


   Pierre Maret

   Université de Lyon (St Etienne)
   Laboratoire Hubert Curien
   CNRS UMR 5516

                                     1
Who I am?        Pierre Maret
 PhD in CS (1995)
 Ass. Prof. at INSA Lyon (1998-2007)
 Prof. at Univ of St Etienne (Univ. of
  Lyon) since 2008

 Research background : DB, IS,
  electronic documents, knowledge
  management, knowledge modeling
                                          2
Talk on:

 Towards Decentralized Communities
  and social Awareness




                                      3
A Community ?
 What is it?
     A   set of participants?
     A   topic?
     A   protocol for the exchange of messages?
     A   data base for storing some information?


 Actually, what is/are the objectives?


                                                    4
Improve information exchanges
 Increase efficiency
 Create new opportunities for relevant
  exchanges
 Enable exchange of new types of
  information

 Deliver the right information, at the
  right moment, and to the right person

                                          5
Domains addressed

  Knowledge modeling
  Information diffusion, sharing, retrieval
  Recommendation systems




                                               6
Social Networks Sites
 Great success
 4 types:
     Content Sharing (i.e. U-Tube)
     Social Notification (i.e. Facebook)
     Expertise Promotion (i.e. Wikipedia)
     Virtual life, games (i.e. Second life)


 Great tools for building communities

                                               7
Social Networks Sites
 Regarding Content sharing and Social
  notification:

          People trust people they know

       Social network ↔ Decision making

Decision making =
   to follow recommendations
   to imitate behavior
   to support in real-life activities


                                          8
Social Networks Sites
 Social networks can be useful

 but SNS have some drawbacks




                                  9
Some drawbacks of SNS
   Multiple registration
   Close world (no interoperability)
   Privacy issues
   No control on data deletion


 Towards a unique governmental
  secure SNS ? No
 Then what?
                                        10
Need for an open approach
 An open approach for community-
  related information exchanges
   include interoperability
   avoid personal data dispersion


 Proposal: A community abstraction

  Decentralized + bottom-up approach


                                       11
Towards a decentralized approach
 1st step : Actors
 2nd step : Communities
 3rd step : Context




                                   12
Towards a decentralized approach
 1st step : Actors

 Actors : an abstraction to model any
  participant
   Person
   Personnel assistant (artifact)
   Autonomous system (artifact)

 An actor has
   Knowledge
   Behavior (decision abilities, actions)

                                             13
Actors as SW agents
 2 types of agents:
   Context agent
      Dedicated to sensors
      From raw data to information
   Personal agent
      Personal assistant. Pro-active (internal goal)
      Contains some user's knowledge
      Knowledge is "delivered to" and
       "gathered from" the environment
      Mobility scenario or in-office scenario


                                                        14
Personnel agent
 Role of a user assistant
 Piece of software
   Autonomous software with communication
    abilities

   Knowledge = abstraction of the owner's
    knowledge
   Decision abilities = actions (managed by the
    owner), related to the present knowledge

                                              15
Actor abstraction
                                    { ki } knowledge
         { ki } knowledge
                                    Tulip is_a Flower
         { bi } behavior
                                    Red is_a Color
 Actor                      Actor   Tulip has_property Red
                                    T1 instance_of Tulip

{ ki } knowledge                    { bi } behavior
{ bi } behavior                     Send message
                                    Receive message
                                    Extract Instances
                   Actor            Set Value


 Expressed using web semantic techniques :
  OWL
                                                        16
Making behavior exchangeable
 Knowledge (RDF/OWL ontologies) can be
  exchanged
 Behavior is generally hardcoded : not
  exchangeable

 A model for expressing agent's behavior in
  SWRL (expression of rules on OWL)

 Work of Julien Subercaze (PhD candidate)

                                               17
Making behavior exchangeable
 Behavior as a finite state machine
   If (transition from State A to State B)
    then (execute list of actions)




                                              18
Describing information
 Using Tags to describe agents
  information/knowledge
 Tag = Annotations, Meta-data

 Concerns any
  information/knowledge/document
   picture
   signal
   email, etc.

                                   19
Tagging activity on personal agents
 Tagging activity
   Automated
   Semi-automated
   Manual
 Useful regarding information retrieval

 Several dimensions/processes for tags
   Location, environmental information, body
    information, thoughts, …

                                            20
Tagging activity on personal agents
 Work of PhD candidate Johann Stan

 Main idea : the meaning of tag
  changes dynamically according to the
  user and circumstances.

 Circumstance :
   communities the user belongs to
   context

                                      21
2nd step : Communities
 1st Step : Actors
 Community : A set of actors with compatible
  communication abilities and shared values
  (common domain of interest)

 VKC = Virtual Knowledge Communities
An abstraction for the exchange of information in-
   between actors



                                                 22
Features for communities
 Community-related knowledge of the agents
   List of (some) communities
   List of (some) agents
   Community-related domain knowledge (about the
    community topic)


 Community-related primitives
   Protocol: create, inform, request…
   Knowledge selection (extract from its knowledge)
   Knowledge evaluation and insertion (received
    through exchanges)

                                                       23
Features for communities Communities




                           Knowledge


                           Mappings


                                  24
Agent communities
 Community protocol
   Create community (with a topic)
   Join, Leave
   Inform, request


 Specific role (any agents)
   Yellow page
   Knowledge = existing communities and
    topics

                                           25
Example
         { ki }
         //joint communities                 { ki }
         C1 (on Car)                         Tulip is_a Flower
A1       C2 (on Flower)(Owner)               C1 is a Community
                                             C2 is a Community
{ ki }                              A3       //joint communities
Tokyo is_a City                              C2 (on Flower)
//joint communities     A2
C1 (on Car)


               A3   has previously joined A1's community on Flowers.
               A3   wants to send some info to this community
               A2   needs more info about Japan.
               A2   is about to create a community on Japan

                                                                   26
Communities and social network
 Memory of interactions builds my social
  network
     With who?
     The topic?
     The context?
     The environment?


 Carried out with tags
 Used to propose interaction facilities
  (prediction)
                                            27
Communities and social network
 Example of annotations of interactions
  (manual)




 Automatic annotations: context, content analysis
 More about the context…
                                                 28
Step 3 : Context
 Context data: gathered from the environment
     Location
     Internal state
     Environment
     Activity (…)
 Situation = f(context data)

 SAUPO model:
    situation ↔ communication preferences



                                                29
SAUPO model
Situation ↔ Communication preferences




                                        30
Agent's context
 User's current activity as context data

 Identifying the user's current activity to
  promote exchanges
   Event + Content analysis and filtering
   Target : more accurate solicitations


 Contextual Notification Framework

                                             31
Agent's context
 Contextual Notification Framework (Work of
  Adrien Joly, PhD Candidate) Filtered
  ambient awareness

 Main idea :
   maintain cooperation in-between people
   while reducing overload

 Context model
 Context sniffer (with user acceptance)
 Matchmaking process (context + social
  network) and notification
                                               32
Contextual Notification Framework




                                33
Conclusion
 Improving knowledge exchanges
 Used techniques
   Semantics modeling: ontologies, owl
   Context awareness
   Social networks

 Leveraged into several scenarios or
  projects
 Leading idea : bottom-up approach
                                          34
Thank you for your attention




                               35

				
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posted:5/24/2013
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