Transcript Ambient Intelligence and Social Awareness

Towards Decentralized
Communities and
Social Awareness
Pierre Maret
Université de Lyon (St Etienne)
Laboratoire Hubert Curien
CNRS UMR 5516
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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
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Talk on:
 Towards Decentralized Communities
and social Awareness
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A Community ?
 What is it?
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A
A
A
A
set of participants?
topic?
protocol for the exchange of messages?
data base for storing some information?
 Actually, what is/are the objectives?
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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
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Domains addressed
 Knowledge modeling
 Information diffusion, sharing, retrieval
 Recommendation systems
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Social Networks Sites
 Great success
 4 types:
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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
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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
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Social Networks Sites
 Social networks can be useful
 but SNS have some drawbacks
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Some drawbacks of SNS
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Multiple registration
Close world (no interoperability)
Privacy issues
No control on data deletion
 Towards a unique governmental
secure SNS ? No
 Then what?
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Need for an open approach
 An open approach for communityrelated information exchanges
 include interoperability
 avoid personal data dispersion
 Proposal: A community abstraction
Decentralized
+ bottom-up approach
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Towards a decentralized approach
 1st step : Actors
 2nd step : Communities
 3rd step : Context
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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)
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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
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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
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Actor abstraction
{ ki } knowledge
{ bi } behavior
Actor
Actor
{ ki } knowledge
{ bi } behavior
Actor
{ ki } knowledge
Tulip is_a Flower
Red is_a Color
Tulip has_property Red
T1 instance_of Tulip
{ bi } behavior
Send message
Receive message
Extract Instances
Set Value
 Expressed using web semantic techniques :
OWL
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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)
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Making behavior exchangeable
 Behavior as a finite state machine
 If (transition from State A to State B)
then (execute list of actions)
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Describing information
 Using Tags to describe agents
information/knowledge
 Tag = Annotations, Meta-data
 Concerns any
information/knowledge/document
 picture
 signal
 email, etc.
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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, …
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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
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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 inbetween actors
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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)
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Features for communities Communities
Knowledge
Mappings
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Agent communities
 Community protocol
 Create community (with a topic)
 Join, Leave
 Inform, request
 Specific role (any agents)
 Yellow page
 Knowledge = existing communities and
topics
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Example
A1
{ ki }
//joint communities
C1 (on Car)
C2 (on Flower)(Owner)
{ ki }
Tokyo is_a City
//joint communities
C1 (on Car)
A3
A3
A2
A2
A3
{ ki }
Tulip is_a Flower
C1 is a Community
C2 is a Community
//joint communities
C2 (on Flower)
A2
has previously joined A1's community on Flowers.
wants to send some info to this community
needs more info about Japan.
is about to create a community on Japan
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Communities and social network
 Memory of interactions builds my social
network
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With who?
The topic?
The context?
The environment?
 Carried out with tags
 Used to propose interaction facilities
(prediction)
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Communities and social network
 Example of annotations of interactions
(manual)
 Automatic annotations: context, content analysis
 More about the context…
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Step 3 : Context
 Context data: gathered from the environment
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Location
Internal state
Environment
Activity (…)
 Situation = f(context data)
 SAUPO model:
situation ↔ communication preferences
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SAUPO model
Situation ↔ Communication preferences
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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
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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
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Contextual Notification Framework
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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
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Thank you for your attention
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