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Agent-Based Information Sharing for Ambient Intelligence - - PowerPoint PPT Presentation

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Agent-Based Information Sharing for Ambient Intelligence Andrei Olaru and Cristian Gratie AI-MAS Group, University Politehnica,

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  • Agent-Based

Information Sharing for Ambient Intelligence

——————————————————————— Andrei Olaru and Cristian Gratie AI-MAS Group, University Politehnica, Bucharest, Romania LIP6, University Pierre et Marie Curie, Paris, France 17.09.2010

1/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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Introduction Layers Sharing Agents Applications Goal Context Scenario Results Conclusions

Agent-Based Information Sharing for Ambient Intelligence

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  • verview

2/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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SLIDE 3
  • Agent-Based Information

Sharing for Ambient Intelligence What is AmI? Layers Sharing Agents Applications Goal Context Scenario Results Conclusions

Ambient Intelligence –

  • r

AmI – is an ubiquitous electronic environment that supports people in their daily tasks, in a proactive, but ”invisible” and non-intrusive manner.[Ramos et al., 2008, Weiser, 1993]

3/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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SLIDE 4
  • Agent-Based Information

Sharing for Ambient Intelligence What is AmI? Layers Sharing Agents Applications Goal Context Scenario Results Conclusions

Ambient Intelligence –

  • r

AmI – is an ubiquitous electronic environment that supports people in their daily tasks, in a proactive, but ”invisible” and non-intrusive manner.[Ramos et al., 2008, Weiser, 1993] People

3/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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SLIDE 5
  • Agent-Based Information

Sharing for Ambient Intelligence What is AmI? Layers Sharing Agents Applications Goal Context Scenario Results Conclusions

Ambient Intelligence –

  • r

AmI – is an ubiquitous electronic environment that supports people in their daily tasks, in a proactive, but ”invisible” and non-intrusive manner.[Ramos et al., 2008, Weiser, 1993] People · Devices

3/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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  • Agent-Based Information

Sharing for Ambient Intelligence What is AmI? Layers Sharing Agents Applications Goal Context Scenario Results Conclusions

Ambient Intelligence –

  • r

AmI – is an ubiquitous electronic environment that supports people in their daily tasks, in a proactive, but ”invisible” and non-intrusive manner.[Ramos et al., 2008, Weiser, 1993] People · Devices · Communication

3/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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SLIDE 7
  • Agent-Based Information

Sharing for Ambient Intelligence What is AmI? Layers Sharing Agents Applications Goal Context Scenario Results Conclusions

Ambient Intelligence –

  • r

AmI – is an ubiquitous electronic environment that supports people in their daily tasks, in a proactive, but ”invisible” and non-intrusive manner.[Ramos et al., 2008, Weiser, 1993] People · Devices · Communication Problem: How to get the relevant information to the interested users?

3/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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  • Agent-Based Information

Sharing for Ambient Intelligence Introduction A Layered Perspective Sharing Agents Applications Goal Context Scenario Results Conclusions

(layers based on [Seghrouchni, 2008])

4/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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  • Agent-Based Information

Sharing for Ambient Intelligence Introduction A Layered Perspective Sharing Agents Applications Goal Context Scenario Results Conclusions

(layers based on [Seghrouchni, 2008])

Hardware

4/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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  • Agent-Based Information

Sharing for Ambient Intelligence Introduction A Layered Perspective Sharing Agents Applications Goal Context Scenario Results Conclusions

(layers based on [Seghrouchni, 2008])

Hardware · Network

4/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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  • Agent-Based Information

Sharing for Ambient Intelligence Introduction A Layered Perspective Sharing Agents Applications Goal Context Scenario Results Conclusions

(layers based on [Seghrouchni, 2008])

Hardware · Network · Interoperability

4/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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  • Agent-Based Information

Sharing for Ambient Intelligence Introduction A Layered Perspective Sharing Agents Applications Goal Context Scenario Results Conclusions

(layers based on [Seghrouchni, 2008])

Hardware · Network · Interoperability · Application

4/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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  • Agent-Based Information

Sharing for Ambient Intelligence Introduction A Layered Perspective Sharing Agents Applications Goal Context Scenario Results Conclusions

(layers based on [Seghrouchni, 2008])

Hardware · Network · Interoperability · Application · Interface

4/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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SLIDE 14
  • Agent-Based Information

Sharing for Ambient Intelligence Introduction A Layered Perspective Sharing Agents Applications Goal Context Scenario Results Conclusions

(layers based on [Seghrouchni, 2008])

Hardware · Network · Interoperability · Application · Interface

4/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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  • Agent-Based Information

Sharing for Ambient Intelligence Introduction Layers Information Sharing Agents Applications Goal Context Scenario Results Conclusions

· The users must get the information that is interesting for them. → context-awareness is needed for computing relevance.

5/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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  • Agent-Based Information

Sharing for Ambient Intelligence Introduction Layers Information Sharing Agents Applications Goal Context Scenario Results Conclusions

· The users must get the information that is interesting for them. → context-awareness is needed for computing relevance. · Ambient intelligence must be reliable and dependable. → distribution is absolutely necessary.

5/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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  • Agent-Based Information

Sharing for Ambient Intelligence Introduction Layers Information Sharing Agents Applications Goal Context Scenario Results Conclusions

· The users must get the information that is interesting for them. → context-awareness is needed for computing relevance. · Ambient intelligence must be reliable and dependable. → distribution is absolutely necessary. Our goal: build a multi-agent system for the context-aware sharing of information.

5/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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  • Agent-Based Information

Sharing for Ambient Intelligence Introduction Layers Sharing Why Agents? Applications Goal Context Scenario Results Conclusions

· Agents satisfy the needs of AmI in terms of: · autonomy · reactivity · proactivity · planning · reasoning · anticipation

6/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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  • Agent-Based Information

Sharing for Ambient Intelligence Introduction Layers Sharing Why Agents? Applications Goal Context Scenario Results Conclusions

· Agents satisfy the needs of AmI in terms of: · autonomy · reactivity · proactivity · planning · reasoning · anticipation · Agents also offer beliefs, goals, intentions and easier implementation of a human-inspired behaviour.

6/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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  • Agent-Based Information

Sharing for Ambient Intelligence Introduction Layers Sharing Why Agents? Applications Goal Context Scenario Results Conclusions

· Agents satisfy the needs of AmI in terms of: · autonomy · reactivity · proactivity · planning · reasoning · anticipation · Agents also offer beliefs, goals, intentions and easier implementation of a human-inspired behaviour. · Agents can provide the intelligent component of Ambient Intelligence – they are distributed, they act locally, etc.

[Ramos et al., 2008] 6/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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  • Agent-Based Information

Sharing for Ambient Intelligence Introduction Layers Sharing Agents Existing applications Goal Context Scenario Results Conclusions

◮ orientation towards personal assistance; centralized

knowledge databases, ontologies and services:

· iDorm [Hagras et al., 2004] – learning user behaviour · MyCampus [Sadeh et al., 2005] – privacy management · ASK-IT [Spanoudakis and Moraitis, 2006] – assistance of elderly

◮ orientation towards distribution, information and

connection management:

· SpatialAgent [Satoh, 2004] – mobile agents · LAICA project [Cabri et al., 2005] – distributed data exchange and processing · AmbieAgents [Lech and Wienhofen, 2005] – context management agents · CAMPUS framework [Seghrouchni et al., 2008] – scalable, layered architecture for context sensing and ambient services · SodaPop model [Hellenschmidt, 2005] – device interoperation and fully distributed control

7/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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  • Agent-Based Information

Sharing for Ambient Intelligence Introduction Layers Sharing Agents Applications Goal Context Scenario Results Conclusions

Our goal: build a multi-agent system for the context-aware sharing of information. · how can we obtain context-aware behaviour with simple agents acting locally? · features:

◮ local behaviour ◮ simple behaviour ◮ small knowledge base ◮ use feedback and self-organization techniques ◮ use simple and generic measures for context-awareness

8/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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  • Agent-Based Information

Sharing for Ambient Intelligence Introduction Layers Sharing Agents Applications Goal Context-Awareness Scenario Results Conclusions

The measures of context-awareness are directed at local information sharing based on importance, relatedness to domains of interest, and validity in time.

9/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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  • Agent-Based Information

Sharing for Ambient Intelligence Introduction Layers Sharing Agents Applications Goal Context-Awareness Scenario Results Conclusions

The measures of context-awareness are directed at local information sharing based on importance, relatedness to domains of interest, and validity in time.

◮ where?

9/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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  • Agent-Based Information

Sharing for Ambient Intelligence Introduction Layers Sharing Agents Applications Goal Context-Awareness Scenario Results Conclusions

The measures of context-awareness are directed at local information sharing based on importance, relatedness to domains of interest, and validity in time.

◮ where? ◮ how far?

9/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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SLIDE 26
  • Agent-Based Information

Sharing for Ambient Intelligence Introduction Layers Sharing Agents Applications Goal Context-Awareness Scenario Results Conclusions

The measures of context-awareness are directed at local information sharing based on importance, relatedness to domains of interest, and validity in time.

◮ where? ◮ how far? ◮ how fast?

9/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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  • Agent-Based Information

Sharing for Ambient Intelligence Introduction Layers Sharing Agents Applications Goal Context-Awareness Scenario Results Conclusions

The measures of context-awareness are directed at local information sharing based on importance, relatedness to domains of interest, and validity in time.

◮ where? ◮ how far? ◮ how fast? ◮ for how long?

9/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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  • Agent-Based Information

Sharing for Ambient Intelligence Introduction Layers Sharing Agents Applications Goal Context-Awareness Scenario Results Conclusions

The measures of context-awareness are directed at local information sharing based on importance, relatedness to domains of interest, and validity in time.

◮ where? – specialty – specifies to which domains of

interest the information is related – controls the direction of the spread.

◮ how far? ◮ how fast? ◮ for how long?

9/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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  • Agent-Based Information

Sharing for Ambient Intelligence Introduction Layers Sharing Agents Applications Goal Context-Awareness Scenario Results Conclusions

The measures of context-awareness are directed at local information sharing based on importance, relatedness to domains of interest, and validity in time.

◮ where? – specialty – specifies to which domains of

interest the information is related – controls the direction of the spread.

◮ how far? – space-locality – the information spreads

around its source

◮ how fast? ◮ for how long?

9/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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  • Agent-Based Information

Sharing for Ambient Intelligence Introduction Layers Sharing Agents Applications Goal Context-Awareness Scenario Results Conclusions

The measures of context-awareness are directed at local information sharing based on importance, relatedness to domains of interest, and validity in time.

◮ where? – specialty – specifies to which domains of

interest the information is related – controls the direction of the spread.

◮ how far? – space-locality – the information spreads

around its source

◮ how fast? – pressure – translates directly into

relevance of the information – controls how fast the information spreads.

◮ for how long?

9/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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  • Agent-Based Information

Sharing for Ambient Intelligence Introduction Layers Sharing Agents Applications Goal Context-Awareness Scenario Results Conclusions

The measures of context-awareness are directed at local information sharing based on importance, relatedness to domains of interest, and validity in time.

◮ where? – specialty – specifies to which domains of

interest the information is related – controls the direction of the spread.

◮ how far? – space-locality – the information spreads

around its source

◮ how fast? – pressure – translates directly into

relevance of the information – controls how fast the information spreads.

◮ for how long? – persistence – specifies for how long

the information is valid – controls the time for which the information will remain in the system.

9/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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  • Agent-Based Information

Sharing for Ambient Intelligence Introduction Layers Sharing Agents Applications Goal Context-Awareness Scenario Results Conclusions

The measures of context-awareness are directed at local information sharing based on importance, relatedness to domains of interest, and validity in time.

◮ where? – specialty – specifies to which domains of

interest the information is related – controls the direction of the spread.

◮ how far? – space-locality – the information spreads

around its source

◮ how fast? – pressure – translates directly into

relevance of the information – controls how fast the information spreads.

◮ for how long? – persistence – specifies for how long

the information is valid – controls the time for which the information will remain in the system. These measures are aggregated into a measure of relevance.

9/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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  • Agent-Based Information

Sharing for Ambient Intelligence Introduction Layers Sharing Agents Applications Goal Context Application Scenario Results Conclusions 10/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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  • Agent-Based Information

Sharing for Ambient Intelligence Introduction Layers Sharing Agents Applications Goal Context Application Scenario Results Conclusions

· create a certain distribution of interest – by inserting facts with low persistence and pressure, and different specialties.

10/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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  • Agent-Based Information

Sharing for Ambient Intelligence Introduction Layers Sharing Agents Applications Goal Context Application Scenario Results Conclusions

· create a certain distribution of interest – by inserting facts with low persistence and pressure, and different specialties. · test the behaviour of the system by inserting 3 data facts,

  • f

different specialty, with medium pressure and high persistence.

10/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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  • Agent-Based Information

Sharing for Ambient Intelligence Introduction Layers Sharing Agents Applications Goal Context Application Scenario Results Conclusions

· create a certain distribution of interest – by inserting facts with low persistence and pressure, and different specialties. · test the behaviour of the system by inserting 3 data facts,

  • f

different specialty, with medium pressure and high persistence. · test the behaviour of the system by inserting 1 data fact with high pressure.

10/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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  • Agent-Based Information

Sharing for Ambient Intelligence Introduction Layers Sharing Agents Applications Goal Context Application Scenario Results Conclusions

· create a certain distribution of interest – by inserting facts with low persistence and pressure, and different specialties. · test the behaviour of the system by inserting 3 data facts,

  • f

different specialty, with medium pressure and high persistence. · test the behaviour of the system by inserting 1 data fact with high pressure. Expect: control of the resulting distributions depending

  • n their respective measures of context-awareness.

10/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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  • Agent-Based Information

Sharing for Ambient Intelligence Introduction Layers Sharing Agents Applications Goal Context Scenario Results Conclusions

fact distribution in the agent grid agents: specialty specialty for each domain pressure

11/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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  • Agent-Based Information

Sharing for Ambient Intelligence Introduction Layers Sharing Agents Applications Goal Context Scenario Results Conclusions

fact distribution in the agent grid agents: specialty specialty for each domain pressure

11/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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  • Agent-Based Information

Sharing for Ambient Intelligence Introduction Layers Sharing Agents Applications Goal Context Scenario Results Conclusions

fact distribution in the agent grid agents: specialty specialty for each domain pressure

11/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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  • Agent-Based Information

Sharing for Ambient Intelligence Introduction Layers Sharing Agents Applications Goal Context Scenario Results Conclusions

fact distribution in the agent grid agents: specialty specialty for each domain pressure

11/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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  • Agent-Based Information

Sharing for Ambient Intelligence Introduction Layers Sharing Agents Applications Goal Context Scenario Results Conclusions

fact distribution in the agent grid agents: specialty specialty for each domain pressure

11/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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SLIDE 43
  • Agent-Based Information

Sharing for Ambient Intelligence Introduction Layers Sharing Agents Applications Goal Context Scenario Results Conclusions

fact distribution in the agent grid agents: specialty specialty for each domain pressure

11/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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SLIDE 44
  • Agent-Based Information

Sharing for Ambient Intelligence Introduction Layers Sharing Agents Applications Goal Context Scenario Results Conclusions

fact distribution in the agent grid agents: specialty specialty for each domain pressure

11/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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  • Agent-Based Information

Sharing for Ambient Intelligence Introduction Layers Sharing Agents Applications Goal Context Scenario Results Conclusions

fact distribution in the agent grid agents: specialty specialty for each domain pressure distribution of high-pressure fact

11/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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  • Agent-Based Information

Sharing for Ambient Intelligence Introduction Layers Sharing Agents Applications Goal Context Scenario Results Conclusions

fact distribution in the agent grid agents: specialty specialty for each domain pressure distribution of high-pressure fact

11/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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SLIDE 47
  • Agent-Based Information

Sharing for Ambient Intelligence Introduction Layers Sharing Agents Applications Goal Context Scenario Results Conclusions

fact distribution in the agent grid agents: specialty specialty for each domain pressure distribution of high-pressure fact

11/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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  • Agent-Based Information

Sharing for Ambient Intelligence Introduction Layers Sharing Agents Applications Goal Context Scenario Results Conclusions

Why obtaining these results is not straightforward:

◮ agents only know about 20 facts, only few of them

being about their neighbours.

◮ agents are both pro-active and reactive, so feedback

may generate overloads in their message inbox.

◮ knowledge bases are very limited in size, so it is

essential to have a good algorithm to sort knowledge and forget irrelevant knowledge.

12/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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SLIDE 49
  • Agent-Based Information

Sharing for Ambient Intelligence Introduction Layers Sharing Agents Applications Goal Context Scenario Results Conclusions

◮ a multi-agent system was built, with agents that have

local knowledge and interact locally.

◮ simple measures for context-awareness were developed,

that allow the computation of the relevance of facts, according to their context, and also according to the agent’s context.

◮ the system was tested and relevant results were

  • btained.

13/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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  • Cabri, G., Ferrari, L., Leonardi, L., and Zambonelli, F. (2005).

The LAICA project: Supporting ambient intelligence via agents and ad-hoc middleware. Proceedings of WETICE 2005, 14th IEEE International Workshops on Enabling Technologies, 13-15 June 2005, Link¨

  • ping, Sweden, pages 39–46.

Hagras, H., Callaghan, V., Colley, M., Clarke, G., Pounds-Cornish, A., and Duman, H. (2004). Creating an ambient-intelligence environment using embedded agents. IEEE Intelligent Systems, pages 12–20. Hellenschmidt, M. (2005). Distributed implementation of a self-organizing appliance middleware. In Davies, N., Kirste, T., and Schumann, H., editors, Mobile Computing and Ambient Intelligence, volume 05181 of Dagstuhl Seminar Proceedings, pages 201–206. ACM, IBFI, Schloss Dagstuhl, Germany. Lech, T. C. and Wienhofen, L. W. M. (2005). AmbieAgents: a scalable infrastructure for mobile and context-aware information services. Proceedings of the 4th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2005), July 25-29, 2005, Utrecht, The Netherlands, pages 625–631. Ramos, C., Augusto, J., and Shapiro, D. (2008). Ambient intelligence - the next step for artificial intelligence. IEEE Intelligent Systems, 23(2):15–18. Sadeh, N., Gandon, F., and Kwon, O. (2005). Ambient intelligence: The MyCampus experience. Technical Report CMU-ISRI-05-123, School of Computer Science, Carnagie Mellon University. Satoh, I. (2004). Mobile agents for ambient intelligence. Proceedings of MMAS, pages 187–201. Seghrouchni, A., Breitman, K., Sabouret, N., Endler, M., Charif, Y., and Briot, J. (2008). Ambient intelligence applications: Introducing the campus framework. 13th IEEE International Conference on Engineering of Complex Computer Systems (ICECCS’2008), pages 165–174. 13/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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SLIDE 51
  • Seghrouchni, A. E. F. (2008).

Intelligence ambiante, les defis scientifiques. presentation, Colloque Intelligence Ambiante, Forum Atena. Spanoudakis, N. and Moraitis, P. (2006). Agent based architecture in an ambient intelligence context. Proceedings of the 4th European Workshop on Multi-Agent Systems (EUMAS’06), Lisbon, Portugal, pages 1–12. Weiser, M. (1993). Some computer science issues in ubiquitous computing. Communications - ACM, pages 74–87. 14/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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  • 14/ 15

Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010

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  • Thank you!

——————————————————————— Any Questions?

15/ 15 Computer Science & Engineering Department . . Andrei Olaru and Cristian Gratie . IDC 2010 . Tangier, Morocco, 17.09.2010