Budapest University of Technology and Economics, Faculty of Electrical Engineering and Informatics

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    Distributed Intelligent Systems

    A tantárgy neve magyarul / Name of the subject in Hungarian: Intelligens elosztott rendszerek

    Last updated: 2018. március 5.

    Budapest University of Technology and Economics
    Faculty of Electrical Engineering and Informatics
    Engineering Information Technology Bsc
    Course ID Semester Assessment Credit Tantárgyfélév
    VIMIAC02 6 2/1/0/v 4  
    3. Course coordinator and department Dr. Dobrowiecki Tadeusz Pawel,
    Web page of the course
    4. Instructors Dr. Tadeusz Dobrowiecki, Department of Measurement and Information Systems
    5. Required knowledge Embedded systems, Artificial intelligence, Distributed systems
    6. Pre-requisites
    (Szakirany("AMINrendsztervAUT", _) VAGY
    Szakirany("AMINrendsztervIIT", _) VAGY
    Szakirany("AMINrendsztervMIT", _) VAGY
    Szakirany("AMIaut", _) VAGY
    Szakirany("AMIintr", _)

    VAGY Training.code=("5NAA8") )

    ÉS NEM ( TárgyEredmény( "BMEVIMIA357" , "jegy" , _ ) >= 2
    TárgyEredmény("BMEVIMIA357", "FELVETEL", AktualisFelev()) > 0)

    A fenti forma a Neptun sajátja, ezen technikai okokból nem változtattunk.

    A kötelező előtanulmányi rendek grafikus formában itt láthatók.

    System modelling, Artificial intelligence
    7. Objectives, learning outcomes and obtained knowledge The aim of the subject is to show the students how machine intelligence can be used to advance the services provided by distributed information systems and that way how to extend their usefulness and field of application.
    Machine (artificial) intelligence can be applied first of all to the system integration, information fusion, and to system safety. Intelligent solution mean also an elevated robustness, adaptivity, and flexibility.
    The curriculum reviews the design of intelligent services, starting from the knowledge based modelling technology, then presenting data acquisition and processing, and finally applying the mined information to improve system functions and to increase system capabilities for learning.
    The capabilities of an information system can be extended by distributed system solutions. We present a review of distributed, competitive, or cooperative multiagent - multirobot systems. To the curriculum belongs also the problem man-machine interaction, where the newly appearing mixed human-agent systems create novel and advanced opportunities to share intelligence and problem solving capability.
    8. Synopsis

    1st week. Review of typical applications areas of intelligent distributed systems: cyber-physical systems, intelligent embedded systems, ambient intelligent spaces, home care and AAL (Ambient Assisted Living), intelligent sensor networks, robotic team systems, information seeking systems in the Semantic Web environment, etc. (analyzing problems, basic requirements, typical tasks, re-evaluating the man-machine interactions).

    2nd week. Knowledge based modelling of distributed systems. Knowledge management: specific representation problems, logical and emotional models, temporal and spatial reasoning.

    3rd week. Design of domain models. Ontological knowledge and ontology management, ontology engineering.

    4th week. Description languages and platforms, RDF data models, OWL, Protege.

    5th week. Problem solving with ontologies, ontology based reasoning.

    6th week. Safety and reliability, context aware system technology and information management, problems of information and knowledge fusion.

    7th week. Knowledge intensive mechanisms of integration, adaptivity and robustness: sensor level information fusion, fusion architectures, fusion algorithms, semantic fusion with ontologies and ontology based reasoning. SensorWeb standard, SOS (sensor operating system) platform.

    8th week. Mining fusion information, data mining tasks, data engineering in distributed heterogeneous environments.

    9th week. Basic statistical analysis, visualization and exploratory analysis of heterogeneous data. Using analysis  data in decision support tasks.

    10th week. Adaptivity in distributed systems, basic learning schemes.

    11th week. Multiagent system architectures, multiagent environments, agent organizations, from centralized system to distributed intelligence.

    12th week. Integration via communication, agent systems and the parallel programming paradigm. Agent communication languages, agent platforms, Jason, AgentSpeak.

    13th week. Cooperation via communication: distributed reasoning, task-sharing in the market paradigm, cooperative multiagent planning. Handling conflicts in competitive environment, conflict related problems, voting protocols, knowledge intensive conflict resolution, game theoretical schemas, ad hoc solutions.

    14th week. Learning knowledge components (believes and goals, or polices), single agent schemas, cooperative learning, learning in competitive environment.

    Students learn distributed agent environment technologies and their application to embedded environmental problems, (in an intelligent home environment).

    9. Method of instruction Lecturing
    10. Assessment During the semester:

    One midterm exam (scheduled for a separate timepoint) which can be made up on the last week of the term. The required min. level is 40% (20 points). Half of the points of a valid midterm exam add to the final score.

    Multiple small (programming) assignments, 30% of them is needed for the approval.

    One larger assignment (designing a larger intelligent distributed system).

    11. Recaps Acc. to the Exam Code of the Faculty.
    12. Consultations On request.
    13. References, textbooks and resources Stuart Russell és Peter Norvig: Artificial Intelligence. A Modern Approach, 3rd Ed., Pearson, 2009

    M. Wooldridge, An Introduction to MultiAgent Systems. John Wiley & Sons, 2008.

    R. H. Bordini, J. F. Hübner, and M. Wooldridge, Programming Multi-Agent Systems in AgentSpeak using Jason. John Wiley & Sons, 2007.

    E. A. Lee and S. A. Seshia, Introduction to Embedded Systems: A Cyber-Physical Systems Approach, 1ST ed., 2011.

    K. Faceli, A.C.P.L.F. De Carvalho and S.O. Rezende, Combining Intelligent Techniques for Sensor Fusion, Applied Intelligence Vol. 20. 2004. pp 199-213

    Special Issue on Ambient Intelligence, ERCIM NEWS, Nr 47 Oct 2001

    Mehul Bhatt, Hans W. Guesgen (Eds.), Spatial and Temporal Reasoning for Ambient Intelligence Systems, COSIT 2009 Workshop Proceedings, 2009.
    14. Required learning hours and assignment
    Contact lessons
    Preparing for lectures
    preparing for exercises
    Preparing for midterm exam
    Homework 20
    Preparing for exam
    Összesen 120
    15. Syllabus prepared by Dr. Tadeusz Dobrowiecki, Department of Measurement and Information Systems