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Modeling and Knowledge Representation in Artificial Intelligence

Content:

Knowledge representation and reasoning is the field of artificial intelligence (AI) devoted to representing information about the world in a form that a computer system can utilize to solve complex tasks such as diagnosing a medical condition or having a dialog in a natural language. Knowledge representation incorporates findings from psychology about how humans solve problems and represent knowledge in order to design formalisms that will make complex systems easier to design and build. Knowledge representation and reasoning also incorporates findings from logic to automate various kinds of reasoning, such as the application of rules or the relations of sets and subsets. Examples of knowledge representation formalisms include semantic nets, Frames, Rules, and ontologies. Examples of automated reasoning engines include inference engines, theorem provers, and classifiers. To obtain representable knowledge about a particular application domain it is necessary model the existing explicit or implicit knowledge in a suitable way. Suitable in the sense of its applicability to the present problem solving task. The students learn in small teams to acquaint themselves I a scientific topic, to analyze literature, to write a scientific paper, and to present their results in front of academic peers.

Example Applications:

  • Fault Diagnosis in vehicles and other technical systems.
  • Environment Modeling and Decision Support Systems.
  • Current research at MQM: Automatic Training plan generation and interactive support for trainers and trainees in fitness studios.
  • Supervisor:

    Prof. Peter Struss and Florian Grigoleit.
    For questions, please contact Florian Grigoleit

    Aim:

    The students learn in small teams to acquaint themselves in a scientific topic, to analyze literature, to write a scientific paper, and to present their results in front of academic peers.

    Module: IN0014
    Time, Location: 00.11.38 15:00-17:00
    Preliminary Discussion: July 3 12:30 00.13.62
    Start: 8.10.2014
    Miscellaneous: Number of students is limited to 10
    Language: English / German

    Process:

  • Students will work in small teams (2 students / team).
  • The results have to be presented in English. The presentations should be about 15 minutes (+ discussion) per student
  • The students write a 10 to 12 page paper (English / German) on their topic. A Word-Template for the paper will be provided.
  • Deadlines:

  • Presentation Slides (.ppt / .pptx): One week before the presentation date
  • Paper Draft Submission(.doc / .docx):
  • Final Paper Submission(.doc / .docx):

  • Topics:

    • Topic: Conceptual Modeling vs. Numerical Modeling
    • Date: tbd
    • Team:
    • Topic: Modeling for AI and Software Engineering with SysML
    • Date: tbd
    • Team:
    • Topic: Reasoning about Change / Action vs. Simulation
    • Date: tbd
    • Team:
    • Topic: Process vs. Component oriented Modeling
    • Date: tbd
    • Team:
    • Topic: Exemplary Knowledge Representation Techniques
    • Date: tbd
    • Team:

     




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