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Assessment

Grade Composition

  • Basic Conversational Agent: 30%

  • Extensions to the Agent (quality and originality): 30%

  • Written Report: 30%

  • Individual Quiz: 10%

    • A pass adds 1 full point to the grade.

    • A fail adds no points.

  • Practical Attendance:

    • Pass or Fail

    • Mandatory pass. A failure will lead to failing the entire course.

  • Midway Check-In Presentation:

    • Pass or Fail

    • Mandatory pass. A failure will lead to failing the entire course.

To complete the project, you need to provide the following deliverables; make sure to follow the instructions for providing us with these deliverables at the end of this page:

Instructions

main branch - inclusion, exclusion + extensions

inclusion_only branch

all code for final pipeline

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report

pdf for slides for midway check in

pipeline results.

All deliverables should be made available on the main branch of your GitHub repository:

Your final agent code (all the MARBEL source code files including .mas2g, .mod2g, Prolog files, etc.) should be located in the folder named agent in your repository.

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You should make all the data you collected (testing data, as well as your pilot testing data with users outside your team) available in a folder named data in your repository.

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Deliverables - Repository

Please ensure the following items are included and properly organized when submitting your project:

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1. Git Repository

  • Main Branch:

    • Contains the full codebase for inclusion, exclusion, and all extensions.

    • Includes the final pipeline implementation.

  • Branch Structure:

    • inclusion_only: Contains the codebase focusing solely on inclusion logic.

    • inclusion_exclusion: Contains the codebase for inclusion and exclusion logic combined.

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2. Report

  • A detailed report documenting the project’s development and results.

  • Submit the report in markdown.

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3. Midway Check-In

  • Provide the PDF version of your slides from the midway check-in presentation.

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4. Pipeline Results

  • Include the output/results from your pipeline to demonstrate functionality and performance.

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5. Trained Model

  • Provide the trained model file:

    • Checkpoint File: model_checkpoint.pt.

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6. Ontology

  • If any modifications were made to the ontology, ensure the updated version is included.

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7. Data

  • If any changes or additions were made to the dataset, provide the updated or additional data files.

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Key Notes

  • Ensure your Git repository is well-organized, with proper branch structure.

  • Verify that all files mentioned above are present, named appropriately, and accessible.

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