Agent-based Systems for Ubiquitous Computing

1. Agent-based Systems forUbiquitous Computing José Viterbo viterbo@lac.inf.puc-rio.br Apresentação: Laboratory for Advanced Collaboration PUC–Rio, Brazil

2. Introduction Ubiquitous computing • Computer systems will seamlessly integrate into our everyday lives, providing services and information anytime and anywhere M. Weiser – The Computer for the 21st Century.Scientific America, 265, Sep 1991.

3. Introduction Support for mobility • UC infrastructures are more complex and deal with issues such as user mobility, disconnection, dynamic introduction and removal of devices, heterogeneous network connections, as well as the need to integrate the physical environment with the computing infrastructure

4. Introduction Context-aware • A fundamental characteristic of a software infrastructure for UC applications is context-awareness, i.e., the capability of providing services based not only on user inputs, but also on implicit contextual information acquired (and deduced) from a wide range of distributed and heterogeneous sensors • Implicit information is used to automatically trigger services

5. Introduction Some chalenges for Ubicomp researchers: • Discovery • Adaptation • Integration • Robustness • Security T. Kindberg and A. Fox - System Software for Ubiquitous Computing.Pervasive Computing, Jan/Mar 2002.

6. Introduction • Using agent technology to support UC Is motivated by agent’s intrinsic properties such as autonomy, mobility, proactivity Agents in UC • The use of multi-agent systems allows the natural partitioning of the whole application in a set of basic and independent tasks H. Harroud., M. Khedr and A. Karmouch - University of Ottawa. Building Policy-Based Context-Aware Applications for Mobile Environments. Mobility Aware Technologies and Applications, MATA 2004.

7. ACAI Example: Travel aide • A university professor flies to a new city and stays the night for two project meetings, one in the morning, one in the afternoon • With a MAS, the professor’s PDA has a connection with the local weather and traffic network, his agent matches this information with his calendar and wakes him up 15 minutes earlier, preventing him to be delayed by a traffic jam • In the first meeting, his agent receives the information that the second meeting was postponed, and arranges for another night’s stay H. Harroud., M. Khedr and A. Karmouch - University of Ottawa. Building Policy-Based Context-Aware Applications for Mobile Environments. Mobility Aware Technologies and Applications, MATA 2004.

8. UA UA User moves ACAI Architecture CPM CPM SA SA PSA SAT PSA SAT Network Network H. Harroud., M. Khedr and A. Karmouch - University of Ottawa. Building Policy-Based Context-Aware Applications for Mobile Environments. Mobility Aware Technologies and Applications, MATA 2004.

9. POLICY SERVICE AGENTManages policies of the domain under its administration to control the behavior and decision-making of the system agents PSA • SITE ASSISTANTIs in charge of preparing and setting up a temporary working environment to a user at a visited site SAT ACAI Agents • CONTEXT POLICY MANAGERResponsible for monitoring the context information and managing the environment resources based on this context CPM H. Harroud., M. Khedr and A. Karmouch - University of Ottawa. Building Policy-Based Context-Aware Applications for Mobile Environments. Mobility Aware Technologies and Applications, MATA 2004.

10. CHIL Example 2: Middleware infrastructure • Devised and developed in the project called Computers in the Human Interaction Loop (CHIL), with a view to easing service development and application integration. • CHIL emphasizes on the development of ubiquitous, context-aware services in ‘smart rooms’ equipped with numerous sensors (i.e., microphones and cameras). J. Soldatos, I. Pandis, K. Stamatis, L. Polymenakos and J. L. Crowley Agent Based Mid. Infrastructure for Autonomous Context-Aware Ubiquitous Computing. Journal of Computer Communications, 2006.

11. CHIL smart rooms • One 64 channel microphone array • Microphones for localization, in particular three clusters, each consisting of four microphones • Four fixed cameras, used for overall monitoring of the room • One active camera with pan, tilt and zoom (PTZ camera) • A panoramic (or fish-eye) surveillance camera.

12. CHIL: Agent platform Agent infrastructures: • facilitate the implementation of communication between distributed entities based on rich semantics • ease the implementation of transparent ad hoc communication between distributed components • agents provide a certain degree of autonomy which constitutes a sound basis for implementing autonomic features Shortcoming: • Software agents lack the capabilities required to support high performance transfer of sensor streams Infrastructures for distributed transfer of sensor streams are usually built as system level components that do no feature the high level capabilities of software agents

13. CHIL Low level middleware components Low level middleware components are wrapped with agent based middleware, so that they behave as software agents

14. MoCA Architecture

15. Regulation Ubiquitous systems are typically open systems • Regulation may be useful to control the interaction among heterogeneous devices and users, helping adaptation and providing security.

16. DynaCROM

17. 1st Case Study • Organizations • PUC-Rio, LIP6 • Environments • Brazil, France • Classrooms, Professors’ Rooms, etc • Roles • Students, professors, etc Marie, a computer science student at LIP6, is travelling to Brazil tospend one year at PUC-Rio as a visiting student.

18. Inference

19. Inference

20. Inference

21. Proposal Multi-agent application JADE MoCA/MAX DynaCROM MoCA Other services • infrastructure to regulate agents interaction in a ubiquitous environment

22. Contribution • While current ubiquitous support is mainly concerned withtopological aspects, we provided a way of considering the social context and itsinfluence in the entities’ interaction process.

23. Ongoing work Context: • Propose case studies with a broader range of context scopes (device capabilities, sensors, etc...) Inference: • Describe compound situations using Ontologies, and being capable to infer if some situation holds Response: • Provide mechanisms to localize and trigger services and applications

24. 2nd Case Study • Organization • PUC-Rio A teacher using a Tablet-PC inside the Active Classroom may send slides to the students using Smartphones and to be presented by the datashow connected to the Server. • Environment • Active Classrooms • Roles • Professor, student • Person • P1, P2 • Mobile devices • TabletPC-01, Smartphone-01, Server-01 • Applications • SlideShowApp-1, SlideShowApp-2 , SlideShowApp-2

25. Base Ontology

26. Norm enforcement Norm Permission to control data-show server isImplementedBy hasCondition DatashowClient Agent Person isLocatedIn ACL && Role==Teacher

27. Infered Ontology 1) Infering the location of devices • [R1: (?Dev1 ontologyURI:isCarriedBy?Person) • (?Dev1 ontologyURI:isInside?Env) • (?Person ontologyURI:isInside?Env) • [R2:(?Person ontologyURI:isInside?Env) • (?Env rfd:ID “AC-RDC”) • (?Person ontologyURI:playsRole?Role) • (?Role rfd:ID?“Teacher”) • (?Norm ontologyURI:appliesTo?Person)

28. Ongoing Implementation • Monitor Agent • knows the general norms in a given Organization (ontology + rules) • is “aware” of agents entering end leaving the room (service provided by MoCA) • must be able to assign a proper role to each agent • Is capable of infering the set of applicable norms (DynaCROM)

29. Ongoing Implementation • Monitor Agent • When a norm is applicable: • A mobile agent encapsulating an application is created and sent to the user’s mobile device

30. FIM