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Mobile Environmental Knowledge Assistant

Mobile Environmental Knowledge Assistant. Pierre MARET and Ken SASAKI, University of Tokyo. Pervasive environment. Autonomous players Intensive communication task No centralization Open environment: new players / players leaving without global impact Peripherics Personal assistants

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Mobile Environmental Knowledge Assistant

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  1. Mobile Environmental Knowledge Assistant Pierre MARET and Ken SASAKI, University of Tokyo

  2. Pervasive environment • Autonomous players • Intensive communication task • No centralization • Open environment: new players / players leaving without global impact • Peripherics • Personal assistants • Sensors (wearable and fixed) Lyon – 28 juin 2006

  3. Multi-agent approach • Each player is an agent • Compliance with pervasive systems • Autonomy • Intensive 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

  4. Virtual Knowledge communities • Agents are provided with a layer for acting within knowledge communities • Knowledge community: • a leader + a topic • dynamic, no concrete existence, extends the topic • Related actions: create, join, inform, request, leave.. • Exchanges are based on contents • Example • A: 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

  5. Agents in pervasive environment In our approach • User-oriented peripherics are associated to Personal Agents • Sensor are associated to Context Agents • Communications occurs within Virtual Knowledge Communities Lyon – 28 juin 2006

  6. General architecture Lyon – 28 juin 2006

  7. Example : Wake-me-up! scenario • Sensor: foot pressure Context agent produces knowledge: user’s activity level • Environmental signal delivery into a metro station Context 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

  8. Example : Wake-me-up! scenario Foot pressure sensor Wake up the user if necessary Metro station Community on “station name” Community on “activity” Personal assistant Lyon – 28 juin 2006

  9. Advantages • Application design is made easier • Agents are made independently • New applications appears with new contents • System is open-ended and compliant with pervasive systems Issues • Semantic heterogeneity: normalized ontologies, acquisition of semantic translators, … • Communication constraints, security, privacy… • … Lyon – 28 juin 2006

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