Laboratoire d'InfoRmatique en Image et Systèmes d'informationINSA de Lyon
LIRIS UMR 5205 CNRS/INSA de Lyon/Université Claude Bernard Lyon 1/Université Lumière Lyon 2/Ecole Centrale de LyonUniversité Claude Bernard Lyon 1, bâtiment Nautibus
43, boulevard du 11 novembre 1918 — F-69622 Villeurbanne cedexhttp://liris.cnrs.fr
UMR 5205
ICPS 2006
Mobile Environmental Knowledge Assistant
Pierre MARET
and Ken SASAKI, University of Tokyo
Lyon – 28 juin 2006 2ICPS’06
Pervasive environment
Autonomous playersIntensive communication taskNo centralization Open environment: new players / players leaving without global impactPeripherics
Personal assistantsSensors (wearable and fixed)
Lyon – 28 juin 2006 3ICPS’06
Multi-agent approach
Each player is an agentCompliance with pervasive systems
AutonomyIntensive communication
Agents have two components (Agent Oriented Abstraction):
Knowledge: concept classes, instance, actions (that can be further specialized). = Ontology.
A decision mechanism (associated to utility function): for instance Evaluate a message, Send an Inform, …
Lyon – 28 juin 2006 4ICPS’06
Virtual Knowledge communities
Agents are provided with a layer for acting within knowledge communitiesKnowledge community:
a leader + a topic dynamic, no concrete existence, extends the topicRelated actions: create, join, inform, request, leave..
Exchanges are based on contents
ExampleA: creates a community on concept “Metro station name”B: decides to join the community and informs about
“Ginza”, instance of “Metro station name”
Lyon – 28 juin 2006 5ICPS’06
Agents in pervasive environmentIn our approach
User-oriented peripherics are associated to Personal AgentsSensor are associated to Context Agents
Communications occurs within Virtual Knowledge Communities
Lyon – 28 juin 2006 6ICPS’06
General architecture
Virtual Knowledge Community
Ontological-contextual data
Ontological-personal data
heritage from
Sensors
Context data
Context Agent
Context-aware personal assistant
Context-aware application
Sensors
Context data
Context Agent
Ontological-contextual data
Personal Agent
Lyon – 28 juin 2006 7ICPS’06
Example : Wake-me-up! scenario
Sensor: foot pressureContext agent produces knowledge: user’s activity
level
Environmental signal delivery into a metro stationContext agent delivers knowledge: station name
Personal assistant of traveler : Personal agent knows the desired station and a evaluation rule when to
wake-up the traveler (activity is low and desired station is reached)
is interested in “Activity level” and “Metro station names”
Lyon – 28 juin 2006 8ICPS’06
Example : Wake-me-up! scenario
Wake up the user if necessary
Virtual Knowledge Community
Ontological-contextual data
Ontological-personal data
heritage from
Sensors
Context data
Context Agent
Context-aware personal assistant
Context-aware application
Sensors
Context data
Context Agent
Ontological-contextual data
Personal Agent
Community on “station name”
Community on “activity”
Metro station Foot pressure sensor
Personal assistant
Lyon – 28 juin 2006 9ICPS’06
Advantages
Application design is made easierAgents are made independently
New applications appears with new contentsSystem is open-ended and compliant with pervasive systems
Issues
Semantic heterogeneity: normalized ontologies, acquisition of semantic translators, …
Communication constraints, security, privacy……
