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Mobile robots:

To be able to act in an unknown environment mobile robots have to know where they are, i.e. they have to locate themselves, using some map. Then, they have to plan the their further path, avoiding collisions with stationary or moving obstacles. All this is summarized under the term Mobile Robot Navigation.

The research in the division concerns:

  • 2D and 3D map building - based on sensor (laser and omnicamera) readings, the map of an environment is built. The hybrid grid-based and object-based representations are used.
  • Localization - robots determine their position using particle filters approach.
  • Path plannig - in order to plan collision free paths, Cellular Neural Networks (CNN) are used. CNN allows combining advantages of potential field methods and the diffusion methods.
  • Coordinating the motion of a team of robots - the path for a team of robots is planned using CNN.
Researchers engaged:


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Knowledge-based design:

Conventional CAD-tools allow the user to produce technical drawings, to visualize the designed object in 3D-space and to perform geometrical transformations of such object. The next generation of these tools should enable the designer to look for innovative solutions at the conceptual phase of design, to generate efficiently the detailed design after the principal decisions have been made, to check whether this design fulfills all requirements stated by the Code of Practice and by the investor, and to investigate alternative solutions. In order to achieve such functionality, new software tools should act as expert systems possessing certain domain knowledge and able to perform automatic reasoning. The development of such software, usually referred to as KBD-tools (Knowledge-Based Design tools), is on the agenda of many research teams nowadays.

The DIS takes part in this effort confining itself to the specific sub-area, namely, to the linguistic approach to design. Treating primitives as letters we can compose words that correspond to certain parts of the designed object and incorporating those words into phrases we accomplish the synthesis of the whole artifact. Introducing a grammar we can force our generative system to produce designs that follow certain rules. On the other hand, allowing the crossover and random mutation in the grammar rules, we can generate innovative solutions.

Researchers engaged:


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Uczenie maszynowe:

Dziedzina uczenia maszynowego dotyczy projektowania algorytmów pozwalających na automatyczne uczenie się programów na podstawie eksperymantów. W szczególności, koncentrujemy się na automatycznym uczeniu się sieci Bayesowskich na podstawie otrzymanych danych. Sieci Bayesowskie, znane również jako sieci probabilistyczne, umożliwiają reprezentację łącznego rozkładu prawdopodobieństwa w zwarty sposób i stały się popularne w dziedzinie Sztucznej Inteligencji. Szczególnie wymagający jest problem uczenia się sieci Bayesowskich z ukrytymi zmiennymi (to jest, zmiennymi które nie są zaobserwowane w danych). Nasze ostatnie pracy dotyczą uczenia się ukrytych modeli klas (również znanych jako na?ve Bayes modeli), z ukrytą zmienną klasy, które należą do najprostszych typów sieci Bayesowskich z ukrytymi zmiennymi dla kategorycznych danych. Także badamy możliwości zastosowania takich modeli w robotyce.

Researchers engaged:


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Diagrammatics:

Diagrammatics, or, as some still prefer to say, diagrammatic representation and reasoning, concerns the use of diagrams in information processing and communication by humans and computers. Diagrammatic representation uses diagrams to represent data and knowledge, while diagrammatic reasoning uses direct manipulation and inspection of a diagram as the primary means of inference. Diagrams are a visual kind of analogical knowledge representation mechanism that is characterized by a direct (though not necessarily isomorphic) correspondence between the structure of the representation and the structure of the represented. For more, see for example: The research in the division concerns:
  • Problems of errors in diagrammatic reasoning and how to avoid them
    (Z. Kulpa: Self-consistency, imprecision, and impossible cases in diagrammatic representation.
    Machine GRAPHICS & VISION 12(1): 147-160, 2003).

  • Development and applications of a diagrammatic representational system for interval analysis and computation (see especially the book: Z. Kulpa. Diagrammatic Interval Analysis with Applications. IPPT PAN Reports 1/2006, xvi+232 pp., Warsaw 2006.

  • Application of diagrams in mathematics (Z. Kulpa. On diagrammatic representation of mathematical knowledge. In: Mathematical Knowledge Management. LNCS 3119, Springer, Berlin 2004, 190-204), especially:

  • Problems of formalization of diagrammatic reasoning (work in progress).
Researchers engaged:

For other publications in this area see the sections on diagrammatics and on intervals in Z. Kulpa's list of publications.


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[IPPT PAN] Instytut     Zakład Technologii Inteligentnych    [PSI logo] Strona główne
Zarządzający stroną Michal Gnatowski
Projektant strony Zenon Kulpa
Ostatnia aktualizacja 4 styczeń, 2008